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BUGSPharmaSUG 2019 Papers - Index
247 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "What's Rules Got to Do with It?" | Helena Sviglin | Presentation | data cleaning | source | 19 |
| 2 | %LOGBINAUTO: A SAS 9.4 Macro for Automated Forward Selection of Log-Binomial Models | Matthew Finnemeyer | In a study, relying on log-binomial models to generate estimates of relative risk for clinical outcomes, there was a need to select from a plethora of clinically-relevant covariates to produce parsimonious explanatory models. While SAS 9.4 allows for automated selection (forward, back, stepwise) for some non-linear models, it does not extend this functionality to all non-linear models, including log-binomial models. As it would be very time-consuming to code this for each model, the %LOGBINAUTO macro was created to automate forward selection for log-binomial models and implemented for the study. This macro utilizes nested %DO loops and non-sequentially manages a list of user-supplied covariates, using measures of statistical significance and improved model-fit, to select for their inclusion into a log- binomial model | binomial distribution, proc step, proc genmod, proc sql, sample, forward selection, response variable, macro put, macro variable | source | 12 |
| 3 | 7 Habits of Highly Effective (Validation Issue) Managers | Amy Garrett | Pinnacle 21 Validator identifies problems in data; however, diagnostics, assessment and resolution of reported validation issues may feel like a complicated, never-ending process. In this presentation, we will discuss common challenges in managing data validation issues and how to handle them effectively. We will show you how to identify the source of validation issues, and how to classify them to understand when to fix or when to explain. We will also discuss cross-team collaboration, ways to improve your process, and habits that lead to faster issue resolution | data cleaning, sample | source | 6 |
| 4 | A Cloud-based Framework for Exploring Medical Study Data | Peter Schaefer | The e-poster presents the concepts of a cloud-based framework used to implement an application for exploring medical study data. The framework allows to easily integrate the kind of scripts that are used by the FDA in their JumpStart services or scripts that create the type of data analyses and tables, listings, figures (TLFs) as suggested in the white papers published by the PhUSE working group "Standard Analyses & Code Sharing". After a brief introduction, the concepts and benefits of the proposed framework are described focusing on the metadata about the analyses and TLFs and how these metadata can drive the user interface and the output of the application. Then, the underlying cloud-based platform is described which allows for the metadata-driven implementation of the proposed framework. Finally, there are some examples based on the scripts that are available from the PhUSE "Standard Analyses & Code Sharing" working group | adverse events, clinical trial | source | 10 |
| 5 | A macro of evaluating the performance of the log-rank test using different weight for enrichment studies | Chuanwu Zhang, Byron J. Gajewski, Jianghua(Wendy) He |
Log-rank test is a traditional nonparametric hypothesis test often used to compare the time-to-event data of two arms. Different weight functions applied to the log-rank test lead to different tests, e.g., the log- rank, Gehan, Peto-Peto, Fleming-Harrington, etc. Little is known regarding the performance of log-rank test with different weight functions when one arm contains subgroups with different hazard rates, which can be seen in enrichment studies. For example, in a two arms and two subgroups study, compared to the control arm, one treatment subgroup may benefit gradually, while the other treatment subgroup may benefit immediately then lose it. How can we know the most efficient (i.e. best performance) weight functions for log-rank test should be selected for analyzing such data? In this paper, we will develop a macro to examine the performances of the log-rank test using different weight functions in terms of empirical power based on simulated data. Our macro contains three parts | hazard function, rank test, log-rank test, treatment arm, censoring, macro variable, sample size, sample, weibull distribution, exponential distribution, treatment period, %if-%then-%else | source | 13 |
| 6 | A Macro to Expand Encrypted Zip Files on the SAS LSAF Environment | Steven Hege | This paper will introduce a short SAS macro that uses java objects to expand zip archive files located on the SAS Life Science Analytics Framework (LSAF). This macro runs on LSAF storing files directly on the environment in a specified folder and can expand password encrypted archives. Our group has found this macro useful when handling data archives uploaded directly into LSAF | macro definition, data integrity | source | 10 |
| 7 | A Practical Guide to the Issues Summary in the Data Conformance Summary of Reviewer's Guides | Gary E. Moore | In the Reviewer's Guides for SDTM (SDRG) and ADaM (ADRG), the Data Conformance Summary is based on validation tools like the Pinnacle 21 Validator (formerly OpenCDISC) and/or proprietary in - house validators. This paper will discuss the purpose of a Reviewer's Guide. Using real life examples, it will illustrate when issues should be resolved by data modifications and when it is appropriate to provide explanations for non-compliance. The paper will contain ideas on the proper way to describe non - compliance issues | data cleaning, adam dataset, time, subject visits, time variable, data validation rules, data science, cdisc | source | 15 |
| 8 | A Practical SAS Macro for Converting RTF to PDF Files | Kaijun Zhang, Xiao Xiao, Brian Wu |
As part of clinical trial reporting, it has been a raising demand to create a large numbers of RTF outputs when a study reaches its major milestones in a study. CROs often receive requests from clients on converting all reports in a user-friendly file format document, usually PDF format, to ease delivery and facilitate review process. One solution for RTF to PDF conversion is via some ready tool, another alternative way is through SAS programming. The mechanism under SAS System for OS/2 and Windows enables it to talk to other PC applications by Dynamic Data Exchange (DDE). Specifically, DDE enables the creation of fully customized Microsoft Word documents from a Base SAS program, and is therefore ideal for automating the conversion of RTF files to high quality PDF files | sas base, clinical trial | source | 7 |
| 9 | A Quick Way to Cross Check Listing Outputs | Shunbing Zhao | Very often, listings are required to support summary reports in clinical trials. Normally these listings are in RTF format, and each one could be tens or hundreds of pages in length. Since a single study could involve dozens of listings, it becomes very challenging to detect potential issues in the process of generating listing, or cross-check between the summary report and the corresponding listing. This paper presents a convenient way to find out how many subjects are included in each listing, which is a good indicator for cross-checking against summary reports. A macro was developed to go through each listing and produce an overall report, which includes a comprehensive summary tab with information, i.e., file name, title of the listing, number of subjects in that listing; it also includes a separate tab for each listing to display the list of involved subjects | sample, excel | source | 4 |
| 10 | A SAS and VBScript cyborg to send emails effectively. | Nikita Sathish | Clinical trial monitoring requires that SAS programmers periodically generate statistical analysis reports and distribute the reports to cross-functional teams for review. Most reports are distributed via email or an enterprise content management system. Manual generation of email, however, can i ntroduce errors. For example, recipients can be omitted, formatting errors can occur, and reports may not be sent in a timely manner. SAS programmers can avoid these limitations by sending reports directly from SAS to large, customized, email distribution lists. SAS programmers can further improve report distribution by combining SAS with VBScript. VBScript allows programmers to overcome security concerns associated with SAS, by not having to store login credentials or tweak the configuration file. We recommend that programmers send emails from SMTP server through SAS using VBScript | macro variable, api, cdisc | source | 6 |
| 11 | A SAS Macro to Provide Survival Functions along with Cox Regression Model Efficiently | Chia-Ling (Ally) Wu | Depending on study design and analysis needs, multiple statistics generated from different SAS procedures may be needed for time-to-event analyses. This paper describes a SAS macro that combines those SAS procedures to generate survival functions and the Cox proportional hazard ratio in one shot, which can help users save time in coding and generate a customized output quickly and easily. Details of the execution and application of the macro will be demonstrated through examples. The macro call returns two different output layouts, i.e., optional estimates in rows and independent variables in columns, or reversely, the independent variables in rows and estimates in columns. Macro users can choose the optional estimates, such as the number of subjects at risk, the number of events and censored observations, the quartiles of the survival function, the coefficient of predictors, the Cox proportional hazard ratio, and the corresponding p-value | hazard ratio, survival function, proc phreg, proc step, regression analysis, disease-free survival, predictor variable, hazard function, main effect, survival analysis, interaction effect, macro parameter | source | 6 |
| 12 | A Simple SAS Utility to Combine Existing RTF Tables/Figures and Create a Multi-level Bookmark Hierarchy and a Hyperlinked TOC | Lugang Larry Xie | Rich Text Format (RTF) is a popular format that most sponsors adopt to create tables, listings and figures (TLF) in the pharmaceutical industry. However, there is an unmet need for a method to concatenate the outputs into a presentable single RTF file independent of the computing system. This paper presents a simple SAS approach to concatenate any SAS generated RTF files, including both tables/listings and figures, and create a multi-level bookmark hierarchy and a hyperlinked TOC, without having to modify the macros/programs that create the individual outputs. It can be executed in any environment with SAS installed. In addition, a simple approach to convert the combined RTF file into a PDF or DOCX format is also introduced, and it can be used for OSI BIMO listings submission | dataset, pharmaceutical industry | source | 9 |
| 13 | A Standardized Data Sample: Key to Improving the Submission Strategy | Prafulla Girase, Joanna Koft |
As mentioned in the FDA's study data technical conformance guide, the agency offers a process for submitting sample standardized datasets for validation. Although sample submissions are tests only and not considered official submissions, they can be of a great value to sponsors in various ways. This paper is based on sponsor's practical experience of submitting sample submissions on five different compounds which are currently approved therapies in the market. The paper will walk through the key parts of a sample submission as well as how to plan and implement one. The paper will also discuss excerpts from the regulatory feedback received on sample submissions and how it helped in continuous improvement of sponsor's submission strategy | sample, data cleaning | source | 5 |
| 14 | A Utility to Automate Reconciliation of Report Numbers and Titles | Valerie Williams, Ganesh Prasad |
In clinical trials, analysis reports are generated in the form of tables, listings and figures (TLFs) for incorporation into Clinical Study Reports (CSRs) that are submitted to regulatory bodies for review and approval of a study drug or device. These TLFs are distinguished from one another by their unique repo rt numbers and titles. A change either of these two im portant pieces of metadata conveys a completely different meaning to the output so it is very important that they are accurate. Lead programmers need to identify and fix cases of missing TLFs and/or incorrect TLF report numbers or titles, prior to CSR submission. A utility program was developed, to automate this process and reduce time required to reconcile the report header information | excel, sample, macro parameter, macro call | source | 5 |
| 15 | Accounting and Record Keeping | Robert Simpson | source | 13 | ||
| 16 | Achieving Zen: A Journey to ADaM Compliance | Kirsty R. Lauderdale, Kjersten Offenbecker, Alice Ehmann |
For many programmers and statistician creating compliant ADaM specifications, programs and data sets is confusing and a bit overwhelming. What should be included versus what to leave out? What level of traceability is needed? What information should be presented in the specs, and what efficiencies can be utilized in the code? Follow us as we work through some of the common pitfalls and map out a path which will help you navigate this winding road to come out with a more compliant product which is clearer for everyone to follow and understand. We will shine light on how to create compliant specifications which will lead you to compliant data sets that are everything the FDA is looking for | time variable, adamig, cdisc | source | 7 |
| 17 | ADaM 1.1 Compliant ADEVENT and ADTTE development in a Cardiovascular Study | Chao Su | Major adverse events such as death, stroke and myocardial Infarction are reported in cardiovascular studies. In addition to these major events, other important information about the clinical assessments is collected at different levels. In CV studies, the clinical assessments from different evaluators, typically investigator s and adjudicators, are reported for the same event. Therefore, it is more complicated to build ADaM datasets to store and analyze the corresponding data. In this paper, a CDISC compliant Basic Data Structure (BDS) dataset called ADEVENT is developed to store all collected major CV events and the corresponding detailed information. This dataset is used to generate the table of concordance between investigato rs and adjudicators. The time to event dataset ADTTE used for the primary analysis is derived from the ADEVENT dataset. The traceability b etween ADEVENT and ADTTE is described and discussed in this paper | adam dataset, raw data, cdisc | source | 6 |
| 18 | ADME Study PK SDTM/ADaM And Graph | Fan Lin | ADME (Absorption, Distribution, Metabolism and Excretion) study is usually conducted in early clinical drug development stage to understand the route of drug excretion and its metabolites in human body. It measures the concentrations of the parent/metabolite(s) and determines the amount of radioactivity in plasma, urine and feces (Gerlie Gieser, Investigators Forum 2012). Due to the sample species involving urine/feces and subjects being discharged at different times the complexities of creating CDISC compiled SDTM and ADAM datasets are increased compared to other PK studies. In this paper we will introduce the complexities of ADME PK study and our approaches to resolve these challenges. This paper demonstrates the process of ADME study PKMERGE/PC/ADPC/TF in a flow chart, and then describes the details in each step, followed by a list of challenges existing in current industry | pk parameter, imputation, sample, adam dataset, cdisc | source | 9 |
| 19 | Advance Your Career with PROC TM! | John M. LaBore, Joshua M. Horstman |
Most of us would like to advance our careers in one way or another. You may wish to become a respected technical expert in your field, move up into a leadership role, or even go independent as a consultant or small business owner. Regardless of your career aspirations, leadership and communication skills are critical to your success. Toastmasters International provides an avenue to develop these skills in a constructive and supportive environment. This paper will provide a summary of the Toastmasters program based on the authors' combined 20 years of experience with the program. We'll discuss how you can execute "PROC TM" and the benefits it can bring to your career |
proc step, statistical programmer | source | 6 |
| 20 | Advanced Project Management beyond Microsoft Project, Using PROC CPM, PROC GANTT, and Advanced Graphics | Stephen B. Sloan, Lindsey Puryear |
The Challenge: Instead of managing a single project, we had to craft a solution that would manage hundreds of higher- and lower-priority projects, taking place in different locations and different parts of a large organization, all competing for common pools of resources. Our Solution: Develop a Project Optimizer tool using the CPM procedure to schedule the projects and using the GANTT procedure to display the resulting schedule. The Project Optimizer harnesses the power of the delay analysis feature of PROC CPM and its coordination with PROC GANTT to resolve resource conflicts, improve throughput, clearly illustrate results and improvements, and more efficiently take advantage of available people and equipment |
proc step, proc sql, excel | source | 9 |
| 21 | An Application of Supervised Machine Learning Models on Natural Language Processing: Classifying QC Comments into Categories | Xiaoyu Tang, Todd Case |
Quality Control (QC) trackers provide text data that describe issues documented during QC of deliverables such as ADaM and SDTM datasets as well as Tables, Listings and Figures (TLFs) . When documenting these issues, programmers often classify their questions into general categories, such as ADaM and Specs, SDTM and Specs, TLF, SAP, etc.. Classifying issues by programmers is subjective and time-consuming. Hence, it's important to have an objective and efficient way to classify those issues - i.e., which category the question belongs to. Having such a tool to classify free text comments not only enables us to classify those questions in just one click, but also provides us information on frequencies of each category to know the categories of most concern. Our goal is to use natural language processing (NLP) tools and machine learning methods to model such a classifier using data from the QC Tracker text | data cleaning, sample, random sampling, support vector machine, machine learning, training set, proc freq, cdisc | source | 10 |
| 22 | An Automated, Metadata Approach to Electronic Dataset Submissions | Janette Garner | In 2014, the Food and Drug Administration (FDA) provided guidance regarding Section 745A(a), an amendment to the Federal Food, Drug, and Cosmetic Act (FD&C Act) that requires regulatory submissions (e.g., new drug applications [NDAs] or biologics license applications [BLAs]) to be submitted in electronic format. The guidance took effect at the end of 2016. This paper presents a metadata-driven solution that facilitates the generation of the dataset package for electronic dataset submission that is compliant with the FDA expectations based on published FDA guidance documents | quality assurance, adam dataset, clinical data, sdtm domain, cdisc | source | 8 |
| 23 | An Innovative Efficacy Table Programming to Automate Its Figure Generation to Ensure Both High Quality and Efficiency | Xiangchen (Bob) Cui, Letan (Cleo) Lin |
Efficacy table and its figure programming are key part of Statistical Programming to support Clinical Study Report (CSR) [1]. Typically efficacy table programming and its figure programming are two totally independent processes. However they share the common SAS codes to generate statistics for table reporting and figure creation, re the reading of efficacy ADaM datasets to subset the records and select the population, and the calling of SAS Statistical procedures to generate statistics. Hence the consistency between efficacy table programming and its figure programming is very crucial to achieving the quality. Since Statistical Programming for CSR Reporting may undergo a lot of changes until very late in the preparation stage, it requires a lot of resources to maintain the consistency | forest plot, dataset, logistic regression, subgroup analysis, data cleaning, logistic distribution, regression analysis, cpu time, clinical programming, imputation, clinical trial, independent qc, least squares mean, multiple imputation, production run, sas programming | source | 23 |
| 24 | An Introduction to the Process of Improving a Neural Network | YuTing Tian | The top-level goal of this paper is to suggest a template for the process of building a high performing Deep Neural Network in SAS. It is hoped that a reader can use the process, shown in this paper, as a template for their own work in deep neural networks. Even though deep neural networks are popular, not many papers discuss them in the context of a supporting, overall process of building a neural network. A second goal is to show details of several "tabs" in Enterprise Miner - especially the powerful, but often overlooked, Auto-neural tab. A lower level goal is to build a network that can outperform a network in a paper by one of my professors. Deep learning is a kind of neural network and a specific kind of machine learning (e.g . artificial intelligence). Deep learning is a recent and powerful machine learning algorithm that e nables a computer to build a multi-layer non-linear model | data mining, neural network, proc hpsplit, missing value, feature selection, deep learning, confusion matrix, proc varclus, proc step, outlier, data cleaning | source | 22 |
| 25 | An Overview of Three New Output Delivery System Procedures in SAS 9.4: ODSTABLE, ODSLIST and ODSTEXT | Lynn Mullins | The SAS Output Delivery System (ODS) enables programmers to create and manipulate predefined ODS objects in a DATA STEP to create highly customized output. ODS gives you great flexibility in generating, storing, and reproducing SAS procedures and DATA STEP output, with a wide range of formatting options. You can use ODS to accomplish the following tasks: Create reports for viewers or browsers Customize the report contents Customize the presentation Create more accessible SAS output By default, ODS output is formatted according to instructions that a PROC STEP or DATA STEP defines. However, ODS provides ways for you to customize the output. You can customize a single table, graph or the style for all your output. SAS 9.4 contains many ODS enhancements. One of these enhancements are three new ODS procedures |
proc step, output delivery system | source | 2 |
| 26 | Application of Criterion I2 in Clinical Trials Using SAS | Igor Goldfarb, Mitchell Kotler |
The goal of this paper is to demonstrate how meta-analyses criteria (specifically, I2 and Cochran's Q) can be calculated using SAS software and applied to the examination of the heterogeneity across subgroups. Some European regulatory authorities require an analysis of the subgroups effect estimated using the criterion I2, which was developed and suggested by Higgins and his colleagues to provide a researcher with a better measure of the consistency between trials in a meta- analysis. Originally the criterion I2 was developed to estimate heterogeneity across the studies, whereas the authorities required its application to the scrutiny across subgroups of the same study. The criterion I2 describes the percentage of total variation across studies that is due to heterogeneity rather than chance. Negative values of I2 are put equal to zero so that I2 lies between 0% and 100%. A value of 0% indicates no observed heterogeneity, and larger values show increasing heterogeneity | clinical trial, degrees of freedom | source | 6 |
| 27 | Application of R Functions in SAS to Estimate Dose Limiting Toxicity Rates for Early Oncology Dose Finding | Huei-Ling Chen, Zhen Zeng |
Pool-adjacent-violators algorithm (PAVA) is a solution to isotonic regression used to estimate dose limiting toxicity (DLT) rates in early oncology trials. In the current version of SAS, there is no ready-to-use statistical procedure to implement PAVA. However, there are multiple R packages available for PAVA estimation. In most pharmaceutical companies, SAS is the mainstream working environment for statistical analysis and reporting. It would be very helpful to exploit the bridge between SAS and R to take advantage of both worlds: R's capability in versatile statistical analysis tools, and SAS's well validated existing statistical procedures and mature reporting system. This paper presents a SAS macro to accomplish this collaborative task by embedding the available R PAVA function into a SAS program. This paper provides details on three important features used to streamline the process of integration | macro definition, proc iml, macro parameter, proc step, dataset, dose, credible interval, dose finding | source | 9 |
| 28 | Applications and Their Limitations of Real-World Data in Gene Therapy Trials | Karen Ooms | There are approximately 7,000 distinct rare diseases that exist affecting 350 million people worldwide, and approximately 80% of those rare diseases are caused by faulty genes. Scientific advances such as the CRISPR/Cas9 genome-engineering system1 have simplified the pharmaceutical and biotech industry's ability to develop gene therapies especially for single gene mutation disorders. The FDA has more than 700 active INDs for gene and cell therapies and in 2017 approved two cell -based gene therapies - chimeric antigen receptor T-cells (CAR-T) and approved the first gene-therapy product to be administered in vivo which in addition was the first to target a specific rare disease genetic condition. Collins and Gottlieb, of the NIH and FDA respectively, have stated that "it seems reasonable to envision a day when gene therapy will be a mainstay of treatment for many diseases"2 | source | 9 | |
| 29 | Applying an Experimental GTL Feature to CONSORT Diagrams | Shane Rosanbalm | SAS added an experimental feature to the TEXTPLOT statement in GTL as part of 9.4 M3. When the OUTLINE option was invoked, this experimental feature allowed the user to capture a dataset with information about where the outline was being drawn using the OUTFILE and OUTID options. This paper is about the application of the experimental OUTFILE and OUTID options in an attempt to make the creation of CONSORT diagrams a little less labor intensive | excel, data step | source | 7 |
| 30 | Artificial Intelligence and Real World Evidence - it takes two to tango | Charan Kumar Kuyyamudira Janardhana | Stakeholders and decision makers are increasingly using real -world evidence (RWE) and technology to solve the problems of human health. Real World Data (RWD) and RWE will play a big role in health care decision making. Data sources can include claims data, electronic medical record data, genomics data, imaging data, sensors, wearables and many others. As big data gathered in real -world healthcare settings becomes more prevalent and robust, it is increasingly being used across the entire healthcare system fo r evidentiary purposes This data has the potential to guide us create better study design and answer unanswered queries in trial set-up. The data from analytics can further form inputs to medical product development. The utility of AI comes from its appli cation to huge data arising out of RWE information bank. Natural Language Processing (NLP), an AI tool can help in charting unstructured data and provide a contextual meaning | clinical trial, machine learning, big data, deep learning, predictive analytics, prescriptive analytics, adverse events, clinical data, neural network, artificial intelligence, unstructured data, insurance claims | source | 9 |
| 31 | Attracting and Retaining the Best! | Kelly Spak | A great manager recognizes the skill needed to attract and retain the best employees. This presentation will identify ways that you can work with your recruiting team to identify ways that will help set you above the competition by attracting and retaining the best employees. Topics will include recruiting, candidate experience, onboarding, training and continued career development | source | 2 | |
| 32 | Auto-Annotate Case Report Forms with SAS and SAS XML Mapper | Yating Gu | A common approach to annotating blank case report forms (CRFs) is to manually create and draw text boxes in PDF, and then type in the annotations that document the location of the data with the corresponding names of the SDTM datasets and the names of those variables included in the submitted datasets. The CRF design is similar across many studies, and there are usually only minor updates in different CRF versions within studies, however, it is very time consuming to manually copy and paste annotations across CRFs. Therefore, an approach that could help auto-annotate a new blank CRF based on an existing annotated CRF could save a tremendous amount of time and effort. This paper introduces a method to import annotations from an annotated CRF to SAS using SAS XML Mapper, manage annotations, and incorporate annotations back into a new blank CRF using SAS. Some relevant programming details and examples will be provided in this paper | dataset, annotated crf, sdtm domain, sample, macro variable, cdisc | source | 7 |
| 33 | Auto-generation of Clinical Laboratory Unit Conversions | Alan Meier | When mapping local labs to SDTM data structures, dealing with the free-text used by sites to indicate the original reporting units, in order to generate conversion factors to sponsor's standard reporting units is often a major headache. Most lab units are in an "amount per volume" structure, where amounts are normally in some multiplier of mass (grams/moles), counts(cells), International Units or Enzyme Units and volumes are mainly in liters. This paper will look at a strategy to automate with SAS much of the task through a three-step process: step 1 - parse and categorize the amounts and volumes, step 2 - normalize to a published conversion factor, and step 3 - calculate the final conversion factor | laboratory data, raw data, cdisc | source | 9 |
| 34 | Automate the Mundane: Using Python for Text Mining | Nathan Kosiba | As programmers and biostatisticians, we have a number of tasks that are broken down to a "copy and paste from document A to document B" scenario. These tasks range from copying the name of the study and sponsor into a SAP to pulling Inclusion and Exclusion criteria for the TI domain. We perform these tasks ad nauseam. While SAS is very good at dealing with structured data and analyses, it is not well equipped to deal with unstructured text such as a protocol. Python, on the other hand, has numerous tools for dealing with unstructured text and breaking it into meaningful pieces of information. While Python can do a vast amount of different things, we will be focusing on how to process information in a protocol to populate Metadata and create Trial Design datasets. Using Python and regular expressions, we can process large amounts of information held in a protocol or other document and retrieve what we need without much manual effort | character variable, clinical trial | source | 5 |
| 35 | Automate the Process to Ensure the Compliance with FDA Business Rules in SDTM Programming for FDA Submission | Xiangchen (Bob) Cui, Hao Guan, Min Chen, Letan (Cleo) Lin |
FDA has published "FDA Business Rules" [1], and expects sponsors to submit SDTM datasets which are compliant with the rules, as well as CDISC IG [2]. These rules assess if the data supports regulatory review and analysis. Some of them are specific to FDA internal processes, rather than to CDISC SDTM standards. Pinnacle 21 is the most commonly used tool by both the industry and FDA to check compliance with both FDA business rules and CDSIC rules. However, Pinnacle 21 is usually used at a late stage of SDTM programming development cycle, and it cannot help users to resolve its findings regarding "Error" and/or "Warming" messages, even if it is used at the very early stage. This paper presents a systematic approach to automate SDTM programming process to ensure compliance with FDA Business Rules | data validation rules, sdtm programming, sdtm domain, controlled terminology, clinical trial, length statement, data cleaning, raw data, macro definition, cdisc controlled terminology, csr, character variable, macro call, cdisc | source | 16 |
| 36 | Automated Dynamic Data Exchange (DDE) Replacement Solution for SAS GRID | Ajay Gupta | In the pharmaceutical and contract research organization (CRO) industries, Microsoft EXCEL is widely used to create mapping specs and in reporting. SAS enables you to export data into an EXCEL spreadsheet and do EXCEL formatting in many ways. But, Dynamic Data Exchange (DDE) is the only technique that provides total control over the EXCEL output. DDE uses a client/server relationship to enable a client application to request information from a server application. SAS is always the client. In this role, SAS requests data from server applications, sends data to server applications, or sends commands to server applications. Unfortunately, DDE is not supported on SAS Grid Computing. This paper explores a replacement solution for DDE on a SAS grid by using a SAS Stored Process, Microsoft Visual Basic for Applications (VBA), and SAS Add-in for Microsoft Office. The paper also explores the automation process and extends the solution to format Microsoft Word documents |
excel, sample, sas enterprise guide, ods excel, grid computing, sas grid | source | 15 |
| 37 | Automating SAS Program Table of Contents for Your FDA Submission Package | Lingjiao Qi, Bharath Donthi |
To submit a complete and compliant data package to the FDA for product approval, the submission must include the SAS programs that generated the analysis datasets, tables, and figures. Including these programs in the submission package helps the FDA data reviewers to understand the process by which the variables for the respective analyses were created, and to confirm the analysis algorithms. Organizing these SAS programs into a Table of Contents is highly recommended as it serves as an easy reference for the FDA reviewers, verifies that each expected file is included in the submission, and provides the FDA with easily-accessible details for each program. The Table of Contents is usually compiled manually by reviewing each individual program and typing the required information into a word processor. This time-consuming process is inefficient and error-prone | macro call, macro variable, proc step, do loop, proc report, dataset, clinical trial, in=, temporary dataset, data cleaning | source | 14 |
| 38 | Automation of Review Process | Shunbing Zhao | Statistical outputs for clinical study report are produced as separate tables, listings and figures (TLFs) in the form of Rich Text Format (RTF). The output review process usually involves a huge amount of TLFs and all kinds of comments back-and-forth. It becomes more and more challenging to track the status of each comment from individual reviewers; very often a few important comments are left unattended or overlooked until very late stage. This paper presents a few useful SAS macros to streamline the review process in a well-controlled way: Firstly, a SAS macro is developed to combine all outputs in RTF format into a single RTF file for different reviewers to comment. The names of the original RTF files are kept on the header of each page of the combined file for later reference. Secondly, reviewers can use the MS Word "Review" feature and put comments freely on each page as necessary | macro definition, excel, sample | source | 6 |
| 39 | Avoiding Disaster: Manager's Guide on How to Rescue a Failing Outsourced Project | Dilip Raghunathan | Outsourcing has become a large part of the business model for many pharmaceutical companies. It allows the sponsor to focus on its core competencies, keep their workforce lean and scale up when needed. However, an unfortunate by-product of this growing trend is occasional failure of the outsourced vendor to meet the timelines and/or quality of the deliverable. The failure to meet agreed objectives on a deliverable result in loss of time, money, resources, morale and strains the relationship between sponsor and the vendor. Furthermore, such failures in studies/projects that are critical to the organization end up severely hurting the chances of regulatory approval and affect time to market. In this paper, I will present a tested, step-by-step pragmatic approach from the perspective of the sponsor in identifying a failed outsourced project and provide a mechanism to rescue and/or salvage the work | likelihood function, quality assurance | source | 9 |
| 40 | Back to the Future: Heckbert-s Labeling Algorithm | Christopher J. Smith | There are several different axis tick mark algorithms in existence. We will discover one of these by time travelling back to 1990 to learn about Heckbert's nice numbers algorithm for labeling graph axes . Then, we will travel back to the future to proactively apply this algorithm to clinical data, using ODS Graphics and DYNAMIC variables from the Graph Template Language (GTL). Lastly, we explore other uses of DYNAMIC variables to provide data-driven solutions to graphical outputs. SAS 9.4 M2 was used in the examples presented. This paper is written for the intermediate to advanced SAS users. In particular, it assumes familiarity with the SGPLOT procedure and GTL |
proc sgplot, graph template language, proc sql, proc step | source | 11 |
| 41 | Badge in Batch with Honeybadger: Generating Conference Badges with Quick Response (QR) Codes Containing Virtual Contact Cards (vCards) for Automatic Smart Phone Contact List Upload | Troy Martin Hughes | Quick Response (QR) codes are widely used to encode information such as uniform record locators (URLs) for websites, flight passenger data on airline tickets, attendee information on concert tickets, or product information on product packaging. The prolife ration of QR codes is due in part to the broad dissemination of smart phones and the accessibility of free smart phone applications that scan QR codes. With the ease of QR code scanning has come another common QR code usage -the identification of conference attendees. Conference badges, emblazoned with attendee-specific QR codes, can communicate attendee | source | 13 | |
| 42 | Bayesian Methods for Treatment Design in Rare Diseases | Xuan Sun, Ruohan Wang |
During the last thirty years, Bayesian methods have been developed rapidly with the explosively growth of high-speed computers. Bayesian Methods are quite useful and easy to interpret with the graphical displays of treatment effects by modeling. In rare diseases, we could not have the access to large samples for a parametric data analysis. Under this circumstance, Bayesian Methods might be a more flexible framework for rare diseases treatment modeling. In this article, the challenge of rare diseases is introduced in section I. The Bayesian Method and how to use Bayesian Method in rare diseases treatment prediction is introduced in section II. And Bayesian | prior probability, bayesian statistics, regression analysis, logistic regression, treatment effect, likelihood function, sample, logistic distribution | source | 5 |
| 43 | Begin with the End of Validation: Adapting QbD Approach in Statistical Programming to Achieve Quality and Compliance Excellence | Linghui Zhang | In many ways, quality and compliance are essential in the pharmaceutical industry. Statistical programming conducts the variety of activities of data management, analysis, and report in the entire data flow in drug development, including preclinical and clinical research, regulatory submission, and post marketing surveillance. Quality and compliance are both crucial components of statistical programming. To achieve high quality, validation process is developed to identify data issues, and is usually performed before data and data-related products are released finally. However, the validation process is time- and resource-consuming. It's quite challenge to fix the data issues after validation in complex clinical trial programs, under tough timeline and temporary workforce shortness | clinical programming, raw data, data cleaning, clinical research, clinical trial, statistical programmer, adam dataset, independent qc, cdisc | source | 8 |
| 44 | Best Practices for ISS/ISE Dataset Development | Bharath Donthi, Lingjiao Qi |
The integrated summary of safety (ISS) and integrated summary of efficacy (ISE) are vital components of a successful submission for regulatory approval in the pharmaceutical industry. ISS and ISE allow reviewers to easily compare individual outcomes, tracking subjects ' results across the entire clinical development lifespan of the investigational product. Furthermore, ISS/ISE facilitate broad views of the investigational product's overall efficacy and safety profiles. However, building integrated datasets is a challenging task as it requires the programmer to achieve consistent structures and formats while also ensuring that each dataset is CDISC-compliant. This paper provides best practices for ISS and ISE dataset development to guide integrated analysis dataset design and production in an efficient manner | adam dataset, imputation, adverse events, clinical trial, sdtm domain, missing value, proc step, concomitant medications, data science, cdisc | source | 9 |
| 45 | Best Practices in Data Standards Governance | Melissa R. Martinez | Most organizations working in the pharmaceutical and biotechnology industries have adopted CDISC submission data standards by now, but the challenge of effective governance and compliance within an organization remains high. CDISC data standards are notoriously open for interpretation and the assumptions and understanding of the data standards can vary widely among users. Adopting CDISC standards is more than just making CDISC-style submission datasets and using tools to help with the programming and compliance checks. Each organization needs to define its own interpretation of CDISC standards to ensure consistency among its studies, put in place workflows and processes to facilitate the governance process, determine what it means to be compliant with the CDISC data standards, and find tools to help with the governance process and compliance determination | data standard, life sciences, raw data, controlled terminology, cdisc | source | 4 |
| 46 | Bidirectionality to LOINC: Handling the Nitty Gritty of Lab Data | Bhargav Koduru | Laboratory data is often challenging to work with during tabulation and analysis data set creation. This paper will include solutions to some of these complexities encountered during the LB (SDTM) and ADLB (ADaM) data set structure, including but not limited to: * How to use the Model Permissible variables of the Findings class in the LB SDTM data set (Ex | adverse events, laboratory data, sample, cdisc | source | 8 |
| 47 | Build Popular Clinical Graphs using SAS | Sanjay N. Matange | Survival Plots, Forest Plot, Waterfall Charts and Swimmer Plots are some of the popular, frequently requested graphs in clinical research. These graphs are easy to build with SAS using the SGPLOT procedure. Once you understand how SGPLOT works, you can develop a plan, prepare the data as per this plan and then use the right plot statements to create almost any graph. This Hands-on workshop will take you step-by-step through the process needed to create these graphs. You will learn how to analyze the graph and make a plan. Then, put together the data set that has all the needed information. Finally, layer the right plot statements in the right order to build the graph. Once you master the process for these graphs, you can use the same process to build almost any other graph. This Hands on Workshop will teach you how to use SGPLOT procedure like a pro |
kaplan-meier plot, forest plot, proc sgplot, censoring | source | 18 |
| 48 | Building Automations for Generating R and SAS Code Supporting Visualizations Across Multiple Therapeutic Areas | Anastasia Alexeeva, Mei Zhao, William Martersteck |
Besides the typical portfolio work statistical programmers perform throughout the course of a clinical trial, there may be a need to create additional safety and efficacy visualizations across numerous trials. The motivation for these requests may be to understand clinical data better, or to use results of analyses to make important business decisions about a compound or indication. In order to do this, analysts write new programs that generate the outputs, create and validate input data sets, and author specifications for input data sets and outputs. Whether individual data sets or analyses are executed in SAS, R, or another programming language is subject to the preference of the programmer and the business need. T his paper demonstrates an example of how to automate the generation of SAS and R programs by the use of a trial-specific file that contains global variable assignments | macro variable, clinical trial | source | 11 |
| 49 | Camouflage your Clinical Trial with Machine Learning and AI | Ajith Baby Sadasivan, Limna Salim, Akhil Vijayan, Anoop Ambika |
Pharmaceutical companies are currently putting a lot of focus on implementing significant changes in R&D strategies, through constant review of the scientific publications. Transparency into current research and sharing of research findings and patient lev el data for further research becomes crucial. In July 2018, the US Food and Drug Administration published a guidance which facilitates the use of Electronic Health Record Data in clinical investigations. Access to clinical study reports and EHR (through FH IR) has been granted to the research community in order to accelerate collaborative research. Anonymization and Pseudonymization have been the topic of discussion since the introduction of the General Data Protection Regulation. GDPR acknowledges the priv acy augmentation provided by these techniques. The document provides exceptions to many of the tough provisions of the regulation, when personal data is anonymised | clinical trial, csr, life sciences, machine learning, raw data, sample, randomization, adverse events | source | 10 |
| 50 | CBER Data Standards Update and BioCompute Progress | Elaine Thompson | sample, raw data, data cleaning, data validation rules, clinical research, data standardization, flat file | source | 27 | |
| 51 | China NMPA (National Medical Products Administration) reform and new regulations/guidelines/requirements | Yi Yang | Historically the regulatory environment in China has been highly challenging. However, since 2015 China health authority started reforming the regulatory environment in China to bring China medical products up to international standards in terms of efficacy, safety and quality, so as to better meet the public needs for drugs as well as to improve the process of access to innovative drugs and therapies from global. Reforms are building smoother processes for innovative drug development in terms of adopting global standards and technical requirements, increasing review and approval transparency, accelerating new drugs review and approval. China health authority has refined old regulations to clearly define the requirements to clinical trial operation, multi-regional clinical trial design, biostatistics principles, electronic data capture, data management and statistical analysis reporting and on-site inspection | clinical trial, clinical research, clinical data, data science, adverse drug reaction, sample, sample size | source | 11 |
| 52 | Clinical Development Standards for FDA Bioresearch Monitoring (BIMO) Submissions | Denis Michel, Julie Maynard |
The Food and Drug Administration (FDA) published the Bioresearch Monitoring Technical Conformance Guide in February 2018. The document provides specifications for clinical data submission by pharmaceutical companies used in planning of FDA Bioresearch Monitoring (BIMO) inspections. Three types of information are required: clinical study level information, subject level data line listings by clinical site, and a summary level clinical site dataset. The clinical study level information are PDF files related to the clinical trial. The subject level data line listings by clinical site are typically generated as SAS programmed PDF files. The guide states that FDA will be able to generate the listings in the future from submitted clinical datasets compliant with CDISC SDTM and ADaM standards. The summary level clinical site dataset is provided as a SAS V5 transport file named clinsite.xpt with a data definition table named define.pdf | sample, excel, clinical trial, statistical programmer, dataset, adam dataset, macro definition, csr, label statement, clinical programming, cdisc | source | 11 |
| 53 | Cluster Analysis - What it is and How to use it | Alyssa M. Wittle, Michael Stackhouse |
A Cluster Analysis is a great way of looking across several related data points to find possible relationships within your data which you may not have expected. The basic approach of a cluster analysis is to do the following: transform the results of a series of related variables into a standardized value such as Z-scores, then combine these values and determine if there are trends across the data which may lend the data to divide into separate, distinct groups, or "clusters". A cluster is assigned at a subject level, to be used as a grouping variable or even as a response variable. Once these clusters have been determined and assigned, they can be used in your analysis model to observe if there is a significant difference between the results of these clusters within various parameters | proc step, proc gplot | source | 28 |
| 54 | Code Generators: Friend or Foe | Janet E. Stuelpner | Good code generators are invaluable tools. Or are they? SAS is constantly changing; adding new features and functions, adding new tools to the tool box while making the manipulation of data and the creation of tables more efficient. Many code generators exist in SAS. Some are embraced quickly, Others are not as people cling to the old methodologies. This presentation will show how code generators have grown over time and what is available now to make the task of programming easier, quicker and more efficient | proc sql, sas studio, sas enterprise guide, proc step | source | 8 |
| 55 | Common Pinnacle 21 Report Issues: Shall we Document or Fix? | Ajay Gupta | Pinnacle 21, also known as OpenCDISC Validator, provides great compliance checks against CDISC outputs like SDTM, ADaM, SEND and Define.xml. This valida tion tool provides a report in E xcel or CSV format which contains information as errors, warnings, and notices. At the initial stage of clinical programming when the data is not very clean, this report can sometime s be very large and tedious to review. If the programmer is fairly new to this report s/he might not be aware of some common issues and will have to fully depend on an experienced programmer to pave the road for them. Indirectly, this will add more review time in t he budget and might distract the programmer from real issues which affect the data quality. In this paper , I will discuss some common issues with the Pinnacle 21 report messages created from running against SDTM datasets and propose some solutions based on my experience |
data cleaning, clinical data, raw data, missing value, controlled terminology, database lock, proc freq, adverse events, sdtm domain, trial elements, cdisc | source | 11 |
| 56 | Compare and conquer SDTM coding | Phaneendhar Gondesi | Appropriate reporting of raw data in CDISC (Clinical Data Interchange Standards Consortium) complaint format is very important in a submission. Mapping and coding are key for reporting. C oding of SDTM (Study Data Tabulation Model) domains could be very challenging especially for a new SDTM programmer. This paper aims to ease SDTM coding by Providing general lay out of overall SDTM domain programming Similarities in coding process for same variable across different domains * Differences in coding process for same variable across different domains | raw data, sdtm domain, sdtm programmer, cdisc | source | 5 |
| 57 | Comparing SAS Viya and SAS 9.4: How Their Features Complement Each Other | Amy Peters | SAS Viya extends the SAS Platform in a number of ways and has opened the door for new SAS software to take advantage of its capabilities. SAS 9.4 continues to be a foundational component of the SAS Platform, not only providing the backbone for a product suite that has matured over the last forty years, but also delivering direct interoperability with the next-generation analytics engine in SAS Viya. Learn about the core capabilities shared between SAS Viya 3.4 and SAS 9.4, and about how they are unique. See how the capabilities complement each other in a common environment. In addition to these core capabilities, see how the SAS software product lines stack up within each one, including analytics, visualization, and data management. Some products, like SAS Visual Analytics, have one version aligned with SAS Viya and a different version with SAS 9.4 | sas viya, sas base | source | 2 |
| 58 | Confessions of a SAS PROC SQL Instructor | Charu Shankar | After teaching at SAS for over 10 years to thousands of learners, this instructor has collected many best practices from helping customers with real-world business problems. Hear all about her confessions on making life easy with mnemonics to recall the order of statements in SQL. Learn about the DATA STEP diehard user who now loves SQL thanks to this little-known secret gem in PROC SQL. Hear about the ways in which ANSI SQL falls short and PROC SQL picks up the slack. In short, there are many confessions and so little time. session is open to all interested in improving their SQL knowledge and performance |
proc sql, proc step, dictionary tables, sashelp, group by, order by | source | 14 |
| 59 | Considerations and Updates in the Use of Timing Variables in Submitting SDTM-Compliant Datasets | Jerry Salyers | Often, the appropriate use and population of Timing variables can present many challenges for sponsors when converting their operational database or legacy data to an SDTM-compliant format. This paper will discuss many of the common scenarios encountered when converting operational data to SDTM. One of the common scenarios involve those occasions where the case report form (CRF) allows for checking an "ongoing" or "continuing" box in lieu of providing an end date. In such cases, the SDTM- based datasets require that these data points be represented by the correct use of the Relative-Timing variables (i.e., ---STRF, ---ENRF, --STTPT, --STRTPT, --ENTPT, and --ENRTPT) | controlled terminology, time variable, sample, adverse events, cdisc | source | 9 |
| 60 | Considerations in Effectively Generating PK Analysis Input Datasets | Jianli Ping | Generating high quality pharmacokinetic (PK) analysis input or PK merge data set in a timely manner is critical for PK parameter generation and downstream programming on CDISC compliant PC/PP data sets. While there is guidance for SDTM PC/PP and ADaM ADPC/ADPP detailed in the CDISC implementation guides, there is a lack of general standards for creating PK analysis input data set s. The different formats of source data as captured from CRF and PK lab can result in further challenges during PK merge data set creation. In this paper, general approaches for PK merge data set creation are proposed that can facilitate PK analysis and generation of PK CDISC compliant data sets and also as a source data for NonMEM programming. This paper also discusses the core PK merge data set variables with commonly selected matrices that should be included and their derivations through case studies and excerpts of SAS programs | pk parameter, raw data, key, macro call, work efficiency, cdisc | source | 10 |
| 61 | Considerations When Representing Multiple Subject Enrollments in SDTM | Kristin Kelly, Mike Hamidi |
In clinical trials, it has become more common for a study design to allow subjects to re -enroll in the same study or subsequent studies within a submission. For studies that allow subjects to re -screen for the same study, it may be difficult to determine how to represent the data for multiple enrollments in SDTM. There are a number of approaches seen in industry, but many pose issues. An example of this is creating multiple records in DM with the same USUBJID to represent each enrollment. Though this may seem the most straightforward approach, many tools used at FDA are configured to expect one record per subject and thus, the data may not readily load into their tools. Another approach is to assign different USUBJID values for the same subject within a study and across studies. This also creates issues for review because it is difficult to track the same subject across studies | clinical trial, unique key, cdisc | source | 8 |
| 62 | Considering Job Changes in an Ever-Changing Environment | Kathy Bradrick, Ed Slezinger |
What is changing in our industry? The simple answer is… everything. Technology is quickly changing, employment laws are changing, sponsor companies are seeing more merger and acquisition activity than ever, and so are CROs and service providers. So how do you as the SAS programmer navigate all these changes as you contemplate career and job changes? See perspectives from executives in both pharma and service providers as they explore changes in our industry that are affecting hiring decisions and company strategies | data science, service provider | source | 6 |
| 63 | Create Interactive Data Visualizations for Clinical Data Review Using Base SAS and Javascript | Raj Kiran Boddu | Is your organization stuck between the slowness of building custom reports for data review, versus the cost of JMP? Learn how to leverage free, open source JavaScript libraries together with the power of Base SAS and build interactive visualizations and dashboards that are web-based, shareable and have drill down, filtering and cross-filtering capabilities. With the help of macros and specific examples, this paper will show you how to recreate common visualizations produced by off -the-shelf software like JMP. While the use case is of particular interest to programmers who support clinical data review, the concepts and techniques can be applied in many areas in Pharma and other industries | data visualization, sas base, clinical data, data review, proc json, macro definition, volcano plot, dataset, scatter plot | source | 12 |
| 64 | Create publication-ready variable summary table using SAS macro | Geliang Gan | Variable summary table is an important tool of not only getting to know the data but helping data management by picking up outlier data points. Even though SAS equips us with all procedures needed, it is not a fun task to make one. Creating a variable summary table involves numerous calls on certain SAS procedures. The purpose of this paper is to share a practical way of automatically generating variable summary table with p-values using a carefully designed and easy-to-use SAS macro. Before the macro starts calling any SAS procedure and conducts calculation, a series of foolproof checking steps against data and parameter assignments are run to assure the successful macro execution. After that, the macro utilizes procedures necessary to produce and collect all information needed according to parameter values specified. By default, frequencies and percentages are tallied for categorical variable, Chi-square or Fisher's Exact test is performed for p values | p-value, interval variable, fisher exact test, variable list, nominal variable, exact test, class variable, proc means | source | 12 |
| 65 | Creating a DOS Batch File to Run SAS Programs | David W. Franklin | We often have many SAS programs to run in a directory. While it is possible to run each individually, it is better if a DOS Batch file be created with the list of programs being run and the order in which they are run. This paper looks at a SAS macro that will take the list of SAS programs in a directory, as specified by the user, and create a DOS Batch file to which can then be run to run all the SAS programs. Also presented will we a small SAS program that you can run at the end of the program to send you an email saying when the programs had finished, and whether there are any issues in the SAS LOG to review! | sas log, file statement | source | 4 |
| 66 | Creating In-line Style Macro Functions | Arthur X. Li | Macro functions that are used in our program are defined by the macro facility. By providing values for the function parameters, the macro function generates a result. We can insert the result directly into a macro statement in our program. Programmers seldom know that we can create a user-defined macro function as well. This paper will focus on the methods of creating an in-line style macro function via various examples | macro function, macro variable, macro put, put statement, rename statement, global symbol table, sas log, symbol table, macro definition, macro loop, macro facility | source | 4 |
| 67 | Dataset Specifications: Recipes for Efficiency and Quality | David A. Scocca | Specifications are the backbone of dataset programming; quality specifications are the recipes with which we produce quality datasets. While we frequently talk about programming strategies and techniques, we spend much less time focusing on dataset specifications. Having high-quality specifications can greatly improve programmer efficiency and speed up validation. Developing specifications and programs hand- in-hand can reduce overall review time and avoid a great deal of unnecessary rework. Questions addressed in this presentation include: How do dataset specifications fit into the clinical trial process? What makes a high-quality dataset specification? How can we make the overall process of dataset production-from specifications through programs-as efficient as possible? Are there ways to standardize the specification development process? | data cleaning, raw data, study day, adam dataset, adverse events, sdtm domain, cdisc | source | 7 |
| 68 | De-Identification of Data & It's Techniques | Shabbir Bookseller | In the past few years, with the emergence of modern technologies in the image of big data, the privacy concerns had grown widely. Now a day's sharing data is much easier then saying Hello. De-identification is a tool that organizations can use to remove personal information from data that they collect, use, archive, and share with other organizations. For many reasons, not the least of whi ch is patient privacy, any shared data must first be de-identified De-identification is not a technique of securing data, but a collection of approaches, algorithms, and tools that can be applied to various kinds of data with differing levels of effectiveness of an individual's privacy. In general, privacy protection improves as more aggressive de-identification techniques are employed, but less utility remains in the resulting dataset | data masking, study day, pii | source | 7 |
| 69 | Define-XML with ARM | Lei Jing, Chao Wang |
Currently, Analysis Results Metadata (ARM) is a required component in PMDA data sub mission package. ARM assists the reviewer by providing traceability from key efficacy and safety analysis results to analysis datasets and dataset related elements, which adds significant value to a regulatory submission as well. However, the process of developing ARM in Define-XML may be very time-consuming if it is handled manually. This paper presents an effective approach to integrate ARM automatically into existing Define - XML v2.0. A macro is designed to convert all required information from ARM metad ata into valid XML syntax and then insert the XML codes into existing Define-XML. This automating process will reduce the development cycle and increase package quality | dataset, excel, adam dataset, clinical trial, define-xml, macro definition, cdisc | source | 8 |
| 70 | Designing Flexible Data Standards Models | Melissa R. Martinez | When we think of CDISC submission data standards, we often focus on the tables and variables that the standards describe. We build products and business processes around creating these tables , but isn't there more to the story than just the submission data sets? This paper explores ways to design data standard models with data processing in mind. It also shows how to add to industry data standard models to define and standardize additional attributes, such as de-identification algorithms, variable calculations, and even blocks of SAS code. Finally, this paper describes features available in SAS Life Science Analytics Framework version 5.1 that facilitate these concepts | data standard, life sciences, raw data, clinical trial, excel, cdisc | source | 4 |
| 71 | Developing Analysis & Reporting Standards For Pharmaco-Epidemiology Observational Studies | Bo Zheng, Xingshu Zhu |
In the blossoming world of modern data analytics, there is an unfulfilled need for standardization within real world evidence (RWE). Lack of standardization often leads to longer and more frustrating program development cycles. This paper discusses our experience with developing standards across RWE primary data collection (PDC) studies. We developed a process for Pharmacoepidemiology PDC studies by standardizing variables based on existing CDISC conventions, developing data quality review tools, and creating a set of modular macros that creates a unified deliverables package. Having a core standardization process in place is beneficial for reducing the time it takes to identify and resolve data quality issues and getting deliverables to customers. As RWE continues to expand, implementing standards provides a unique opportunity to help guide its path towards even greater acceptance within the scientific community | statistical programmer, raw data, data cleaning, adam dataset, proc import, proc step, cdisc | source | 5 |
| 72 | Developing Custom SAS Studio Tasks for Clinical Trial Graphs | Olivia Wright, Sanjay N. Matange |
SAS Studio provides point-and-click tasks for basic statistics, biostatistics models, and statistical graphs. SAS Studio users can create their own Custom Tasks by modifying existing tasks or writing new ones using simple text commands from Apache Velocity Template Language. Custom Tasks can simplify sharing of code by turning complex SAS graphics code into point-and-click tasks that can be shared. The same functionality can be integrated into a more complete analytic modeling workflow by adding additional graphing options to an existing built-in task. In this workshop, we will work hands-on to create a graphical user interface for code-heavy SGPLOT clinical trial graphs |
sas studio, survival analysis, macro variable, proc step, clinical trial, proc sgplot, bar chart, kaplan-meier plot | source | 15 |
| 73 | Do-It-Yourself CDISC! A Case Study of Westat's Successful Implementation of CDISC Standards on a Fixed Budget | Richard M. Mitchell, Rachel Brown, Jennifer K. Fulton, Stephen Black, Marie Byrd Alexander |
Do you find the prospect of implementing CDISC within the confines of a tight timeline and a fixed budget to be overwhelming? Westat, a premier research organization, offers the continuum of clinical trials support services, such as data management, data analysis, regulatory affairs, and site monitoring. Clinical research is dynamic, and it is imperative to comply with ever-changing federal guidances and regulations. This paper describes how Westat's statisticians, programmers, and data managers approached the implementation of CDISC standards on a fixed budget; discussing how we overcame challenges by creating tools and processes to improve efficiency, developing training programs, and reimagining how project teams collaborate to ensure data sets submitted to the FDA are compliant | clinical trial, sas programmer, data governance, sas programming, macro definition, trial summary, quality control, sample, data science, cdisc | source | 6 |
| 74 | DOMinate your ODS Output with PROC TEMPLATE, ODS Cascading Style Sheets (CCS), and the ODS Document Object Model (DOM) | Louise S. Hadden, Troy Martin Hughes |
SAS practitioners are frequently forced to produce SAS output in man datory formats, such as using a company logo, corporate or regulated government templates, and/or cascading style sheet (CSS). SAS provides several tools to enable the production of customized output. Among these tools are the Output Delivery System (ODS) Document Object Model (DOM), cascading style sheets, PROC TEMPLATE, and ODS style overrides (usually applied in procedures and/or in originating data). This paper and presentation investigates "under the hood" of the Output Delivery System destinations and the PROC REPORT and investigates how mastering ODS TRACE DOM and controlling styles with the CSSSTYLE= option, PROC TEMPLATE, and style overrides can satisfy client requirements and enhance ODS output |
proc step, proc report, proc template, output delivery system, ods html, txt, proc format, if-then-else, &variable, format statement, sas output, excel | source | 18 |
| 75 | Don't Just Rely on Processes; Support Local Subject Matter Expertise | Deidre Kreifels, Steven R. Kirby, Mario Widel |
Since the dawn of time (or at least since we started analyzing data from clinical trials) companies engaged in clinical research have developed processes designed to ease the burden of programmatic data review, data standardization and data analysis tasks. These processes typically include standards for input data, standards for outputs and code to get from one to the other. In many cases people who have a limited understanding of the process (or the subject matter) can quickly generate outputs that are right most of time. But how can we efficiently manage cases where the process does not produce accurate results? And how can we be sure that people following the process are able to evaluate if the results are accurate? A big part of the answer is ensuring that process users have sufficient subject matter expertise | clinical trial, clinical research | source | 7 |
| 76 | Effective Graphical Representation of Tumor Data | Sanjay N. Matange | The "Duration of Treatment" and "Tumor Response" information for subjects in a study has traditionally been visualized in separate graphs where the subjects may be sorted by different criteria. In such cases, the Clinician must work harder to associate the subject across different graphs. Displaying the data together, sorted by the tumor response makes it easier for the Clinician to understand this information. 3D waterfall graphs have been proposed for such visuals. This paper will show you how to build a 3D Waterfall Graph with tumor response and duration of treatment in one graph using SAS. We will also discuss the features of this approach. Finally, we will present effective 2D alternative displays to visualize all the data in one combined graph thus making it easier to decode the information | swimmer plot, proc sgplot, waterfall chart | source | 13 |
| 77 | Effective Marketing and Selling Yourself through Social Media | Tara Potter | pharmaceutical industry | source | 10 | |
| 78 | Embedded Processes - evolving face of QUALITY in the world of Robotic Process Automation (RPA). | Charan Kumar Kuyyamudira Janardhana | Systems and processes have been the backbone of any emerging and existing organization in clinical domain. The password to live a disease-free life is the 'most wanted' i.e., 'drug'. In search of this 'most wanted' fugitive, drug companies are investing in their best possible detectives. Quality and compliance management is the key, ensuring subject safety and data integrity in our domain falling in line with the regulatory requirements. Evolution from a paper-based quality system to electronic systems led to the emergence of regulations and guidelines such as 21CFR PART 11 and GxP. Evolving further towards Robotic process automation (RPA) - how will quality be perceived and implemented is the problem statement. RPA does not involve any form of physical robots instead it is the software robots which mimic human activities by interacting with applications in the same way that a person does | customer satisfaction, internal audit | source | 9 |
| 79 | End of Computing Chores with Automation: SAS Techniques That Generate SAS Codes | Yun (Julie) Zhuo | SAS programmers often have to confront computing chores that require repetitive typing. Manual coding is time consuming and inefficient. It also almost inevitably introduces human errors which compromise quality. While SAS has no shortage of automation techniques, it does not always occur to SAS programmers to write SAS codes that generate SAS codes. In this paper, we focus on three code- generating techniques. Each technique will be demonstrated using a simple practical example of automating variable label creation. Code samples will be provided. This paper also provides programming tips, explores other applications, and compares the three techniques using the simplicity, flexibility, and efficiency metrics | macro variable, call execute, put statement, label statement, data step, character functions, sas output, sas programmer, proc step, proc contents, sas log, excel, proc sql | source | 10 |
| 80 | Enforcing Standards in an Organization: A Practical 6 Step-Approach | Priscilla Gathoni, Dany Guerendo Christian |
Is your organization struggling to enforce standards? Is complacency and siloed programming within functional units haunting your organization? This paper will explore a 6-step practical approach for organizations to assess standards using CDISC and FDA guidance, show the importance of standards, the possible repercussions to institute for lack standards adherence, show the importance of a gatekeeper for capturing standards adherence metrics, and finally present a generic macro adherence utility for checking the usage of standards in a study folder. A clear communication for the location and type of standards available within an organization will help eliminate excuses for not using standards. Additionally, an explicit message on the value that standardization brings in increasing efficiency; reducing the need for mundane tasks, and efficient resource utilization is explored. Further, creative methods for encouraging the use of standards are mentioned in the paper | proc report, clinical data | source | 9 |
| 81 | Enhancing Randomization Methodology Decision-Making with SAS Simulations | Kevin Venner, Jennifer Ross, Kyle Huber, Graham Nicholls |
This paper will illustrate how SAS is an effective tool to conduct simulations for making data -driven randomization methodology decisions. SAS can be used to develop simulation programs to investigate the expected treatment balance and other randomization goals with comparing various randomization methods (stratified blocked randomization list, minimization, etc.) and associated parameters (block size, biased-coin probability, etc.). A case study is provided to show how simulations can evaluate the expected treatment balance for different randomization methodologies / parameterizations being considered; and how simulations can investigate other randomization goals, such as minimum subjects required to be randomized at a site to ensure both treatment arms are represented. This paper will illustrate how SAS simulation programs can be developed with configurable macros that are readily adapted for each individual protocol | randomization, treatment arm, macro variable, sample, macro definition, sample size, clinical trial, proc freq | source | 15 |
| 82 | Ensuring Programming Integrity with Python: Dynamic Code Plagiarism Detection | Michael Stackhouse | Integrity in your team's programming is of the utmost importance, not only when preparing for a regulatory submission, but simply to ensure the quality of the analysis throughout your drug's development. A common validation technique used throughout the industry is double programm ing - where two programmers work independently to obtain identical results. But how do you guarantee independence? And what if you suspect programming independence was violated? Linux tools like diff may not be sufficient to identify harmful similarities. This paper will explore a Python based tool that can bring these issues to light. The tool is dynamic enough to locate similarities at any point in your programs, and can allow flexibility for minor syntactic changes, such as dataset name changes or reordering of statements. All results are gathered into easily reviewable files to help ensure that your team's work can uphold the integrity and reputation that you rely on | excel, proc freq | source | 17 |
| 83 | Equivalence, Superiority and Non-inferiority with Classical Statistical Tests: Implementation and Interpretation | Marina Komaroff | Determining superiority of an experiment al treatment versus a standard of care has been a popular objective of randomized controlled trial in the pharmaceutical industry. Superiority is determined by the statistical and clinical significance of a clinical endpoint . However, researchers often que stion why non - significant p-values cannot be viewed as evidence that the two treatments were equivalent. They might say: if the p -value < 0.05 we assume the null hypothesis is false, so it must also follow that if the null hypothesis is true, the p -value must be ≥ 0.05. It is not clear whether this reasoning is a poor attempt to salvage a failed study or reflects a misunderstanding of null hypothesis testing. This paper will clarify the meaning of p-values and demonstrate how p -values can be used to conclu de equivalence (no difference) in treatment effects | sample size, sample, proc ttest, treatment effect, clinical trial, proc step | source | 7 |
| 84 | Excel in SAS | Charu Shankar | EXCEL and SAS are universally loved. Both have their strengths. EXCEL has been around a long time and many non-SAS users use the spreadsheet to enter & manage their transactions. SAS is great for analyzing data. Why not marry the strengths of both? get data from EXCEL into SAS, complete the analysis in SAS and then send the results t o EXCEL. This would be a great help for colleagues who don't have SAS on their desktops. Come learn the many ways to get EXCEL to SAS. From EXCEL and data from SAS to EXCEL. This session will cover the following ways to move between EXCEL and SAS. 1. PROC IMPORT - to read EXCEL into SAS 2. SAS Access engines to read EXCEL into SAS 3. ODS TAGSETS - take sas to Cool EXCEL pivot tables 4. PROC EXPORT - export SAS to EXCEL |
excel, proc step, xls, proc export, proc import, sas base, dataset | source | 10 |
| 85 | Excel with MS Excel and X Commands: SAS Programmers' Quick Tips Guide to Useful Advanced Excel Functions and X Commands. | Shefalica Chand | SAS is the bread and butter for clinical/statistical programmers in the pharmaceutical and biotech industry. Nevertheless, there are many functionalities, tools, and resources that can be used in conjunction with SAS to facilitate a better user experience for biostatistics and other cross-functional partners. In this paper, we will share some useful advanced MS EXCEL functions like VLOOKUP, HYPERLINK, CONCATENATION, etc., to improve efficiency, remove ambiguity and redundancies, and enhance accuracy by eliminating manual intervention (for example, creating specification, tracking sheet, managing review comments, and tracking resolutions and implementations). Here is a sneak-peek into these EXCEL functions: VLOOKUP: This function helps search values based on a particular column (index column) from one EXCEL sheet to another by matching common values in the index columns. HYPERLINK: This function can help create hyperlinks to external files |
excel, macro call | source | 9 |
| 86 | Expediting Drug Approval: Real Time Oncology Review Pilot Program | Laxmi Samhitha Bontha | Health care industry aims to provide right treatment and immediate care for a patient. New drug approval in the United States take an average of 12 years from pre-clinical testing to approval with just the approval process averaging around two and half years. It is important to provide patients new, potentially lifesaving therapies at the earliest. To achieve this FDA launched Real Time Oncology Review (RTOR) pilot program which allows FDA to review data earlier, before the applicant formally submits the co mplete application. For a drug to be selected to be evaluated in the RTOR category it should meet criteria such as easily interpreted endpoints, straight forward study design, drugs showing substantial improvements over available therapy, drugs which have been given break through designation previously. The RTOR process is designed to take about 20weeks of time. Currently, the RTOR pilot program is being used for supplemental applications for already-approved cancer drugs | clinical research, database lock | source | 4 |
| 87 | Experiences in Building CDISC Compliant ADaM Dataset to Support Multiple Imputation Analysis for Clinical Trials | Xiangchen (Bob) Cui | Multiple imputation (MI) is becoming an increasingly popular method to address the missing data problem in regulatory clinical trials, especially when the outcome variables come from repeated assessments. SAS procedures, PROC MI and PROC MIANALYZE, apply the multiple imputation techniques to generate multiple imputations for incomplete multivariate data and to analyze results from multiply imputed data sets, respectively. How to use PROC MI to build CDISC compliant ADaM dataset to support MI analysis is a new ADaM programming technique. This paper illustrates how to apply ADaM BDS data structure to build such one through an example. We will not present how to use PROC MI procedure, for it has been very well explained in SAS user manual and other papers. However we do provide some tutorial of related statistical concepts to help Statistical Programmers to better understand this procedure and apply it in ADaM programming |
proc mi, proc step, imputation, multiple imputation, adam dataset, missing value, efficacy summary, data cleaning, clinical trial, logistic regression, proc mianalyze, logistic distribution, regression analysis, cdisc | source | 20 |
| 88 | Exploring Common CDISC ADaM Conformance Findings | Trevor Mankus | Having analysis data that is compliant with the CDISC ADaM standard is critically important for the regulatory review process. ADaM data are required to be provided in both FDA and PMDA submissions because the data allows those agencies to better understand the details of the performed analyses and reproduce the results for further validation. Validation of ADaM data is a primary focus for regulatory agencies so they can begin their review of the results. This presentation will review some of the more commonly occurring validation rules which were found across all our customer data packages which were validated using our automated software and discuss potential reasons for why these rules fired | data cleaning, label statement, adverse events, controlled terminology, cdisc | source | 6 |
| 89 | Extending the umbrella of compliance: LSAF with R Distributed Computing | Ben E. Bocchicchio, Sandeep Juneja, Rick Wachowiak |
proc http, web service | source | 3 | |
| 90 | F2Plots: Visualizing relative treatment effects in cancer clinical trials | Yellareddy Badduri | In every year there were many clinical trials are conducting on different types of Cancers. With Cancer trials increasingly reporting nontime-to-event outcomes, data visualization has evolved to incorporate parameters such as responses to therapy, duration and degree of response, and novel representations of underlying tumor biology. Graphs and figures are excellent tools for data visualization and they have capability to display data figuratively and enables rapid interpretation. F2 plots (Forest and Funnel) were initially developed for presenting results of meta-analysis. Forest plot is an intuitive, convenient and used to show the relative treatment effect of an intervention between groups within the larger cohort. Forest plot is easily understood constitute several horizontal lines, which represent the 95% confidence interval, and a central symbol in the middle of the line segment, which represents a point estimate that is usually the median or mean | forest plot, confidence interval, treatment effect, clinical trial, scatter plot, data visualization, overall survival, proc step | source | 9 |
| 91 | Figure it out! Using significant figures from reported lab data to format TLF output | Elizabeth Thomas, Lauren Williams |
Laboratory data comes from a variety of sources with differing levels of precision and often needs to be manipulated and/or transformed before the results are reported in a table, listing, or figure. While reporting precision is well-established for many standard lab parameters, pre-specification of reporting precision for a novel lab value can be challenging. One option is to let the data, as reported, determine the precision. This paper gives a short refresher on the rules of significant figures (sig figs). You will learn how to calculate sig figs given a character-formatted result, how to keep track of sig figs for transformed data (sums, averages, ratios, and transformation by a constant), and how to format a result given an unrounded value and sig fig | character variable, laboratory data | source | 8 |
| 92 | Find Your Story | Adam Sales | Healthcare and medical treatment is something we all engage in. As such, we who work in pharmaceutical development have an extraordinary opportunity to connect to our work and its impact. Ironically, many leaders do not prioritize this bond, failing to help team members develop a meaningful relationship with their work. Instead we often hear about simple motivational encouragement. Leaders promote recognition, instant awards, advancement, and personalized approaches based on employee interests-even though they have minimal control over many of these incentives and all of them are barter systems. In losing sight of the impact of our work, any job can become rote. In contrast, by keeping the significance and impact of our work front and center, the task itself can become motivational | source | 5 | |
| 93 | Finding Work and Networking | Joshua M. Horstman | health insurance | source | 8 | |
| 94 | Findings About: De-mystifying the When and How | Soumya Rajesh, Michael Wise |
CDISC offers Findings About (FA) and Supplemental Qualifiers (SuppQual) to handle information that doesn't fit into standard domains - or 'Non-standard variables'. They are however, quite distinct from each other and the appropriate use for each may still lead to confusion. "When should FA be created?" or "When is it best to use SUPPQUAL?" These are important questions that can only be answered by asking additional data questions. When the data does not fit into the parent domain, it may only be mapped to SUPPQUAL if it relates to one parent record. However, almost all other situations are covered by FA - wherein data relates to multiple records, or when a two-way relationship is needed etc. FA would be the right approach then, because it has versatility beyond what's offered by SUPPQUAL. For example, FA would provide a way of storing symptoms along with the time that they began and relating each back to the AETERM in the AE dataset | adverse events, sdtmig, cdisc | source | 9 |
| 95 | Fine-tuning your swimmer plot: another example from oncology | Steve Almond | Swimmer plots are an effective graphical presentation of longitudinal data such as periods of treatment/observation, occurrences of events, and the durations of events or subject status. These types of plots are particularly popular for oncology trials, where the treatment and follow -up periods are displayed along with tumor assessment results and the duration of various responder cr iteria. The ease of creating swimmer plots has increased in successive SAS versions within the ODS Graphics procedures. This paper reviews the basics for constructing the elements of such plots, and provides tips and tricks for implementing aesthetic touches simply with the main SGPLOT procedure (SAS 9.4) statements and without the use of annotations or the Graphics Template Language |
swimmer plot, best response, proc sgplot, duration of response, treatment period, proc step, class variable, procedures, adverse events | source | 25 |
| 96 | Flagging On-Treatment Events in a Study with Multiple Treatment Periods | David W. Franklin | Typically, for data like Adverse Events, the common practice is to flag events that occur on treatment with a definition of something like "Event starting on or after date of first treatment and 30 days after date of last treatment". This fairly easy to program, but when things like a cycle of treatment may have multiple periods due to patient events, this usual approach does not work. This paper introduces the ADaM variable APHASE and how this solved a structural problem with the data, presents a macro that was very useful in producing the solution | trial elements, treatment period, data science, adverse events | source | 4 |
| 97 | Forest Plots for Beginners | Savithri Jajam, Olesya Masucci |
A forest plot is a common visualization for meta-analysis. The origin of forest plots were generated in at least to the 1970s by Freiman et al to display the results of multiple studies with mark depicting point estimates and horizontal lines showing the confidence intervals for each study. A decade later, Lewis and Ellis produced a similar plot for meta-analysis along with the pooled estimate. This type of plot was not called a "forest plot" in print for some time. However, the phrase originates from the idea that the plot appears as a forest of lines and is first used in a publication in 1996. In the 1980s no standard computer packages could easily produce these plots and they came from specifically produced computer programs, which is for most part the case even today | forest plot, confidence interval, proc template, proc step, subgroup analysis, treatment effect | source | 9 |
| 98 | Forewarned is forearmed or how to deal with ADSL issues | Anastasiia Oparii | The Subject-Level Analysis Data Set is an important and essential part of each study which helps to review information about the patient across a clinical trial. Moreover, it provides traceability between all the analysis data sets and source data. Therefore, A DSL should be derived with special care. If you are really lucky, you even don't realize how many tricky questions it may cause when raw data is not clear enough or just something goes wrong. This paper summarizes some common issues related to ADSL progra mming and suggests potential solutions to avoid problems in advance. In particular, it focuses on dealing with partial or completely missing dates and their connections with other derived variables along with additional validation for variables which are selected from SDTM domains. Furthermore, it walks through some useful examples and provides SAS macros to identify issues | randomization, date variable, adverse events, clinical trial, raw data, cdisc | source | 16 |
| 99 | Framework for German Dossier Submissions | Kriss Harris | When submitting your drug benefit assessments to the German Authority or other (foreign) regulatory agencies, you need to provide your reports in a specific format. These reports are usually in a non- English language, so the characters are different from the English characters that you are used to. The characters contain accents and other special characters, and the numerical results contai n commas where you expect decimal points to be, and vice versa. To provide this report, a medical writer typically uses Microsoft Word to copy the results from a Clinical Study Report (CSR) into the document, and includes the appropriate formatting and translations. The copying must be done very carefully, and this process is error prone and very exhaustive. Also, the German authorities might have additional follow -up questions. Therefore, following this process is inefficient. There is a way to make the pr ocess more automated, and this paper demonstrated those methods | overall survival, median survival, proc step, proc report, proc sql | source | 8 |
| 100 | Free Duplicates Here! Get Your Free Duplicates | Kristen Reece Harrington | In clinical trials, it is common to produce an overall summary as well as several subset versions of that summary. The overall summary is typically referred to as "unique" and the subset versions as "duplicates". For traceability, a separate program is required for each individual output; cramming the creation of the unique and duplicates into a single program won't pass muster. The most common approach to this problem is to create a macro. This macro is called by each of the separate unique and duplicate programs to produce the output files. The brute force method to create these separate programs is manually copying the unique program once for each duplicate to be produced, renaming files and changing values of macro subsetting parameters. This paper will explore an automated approach to creating the duplicate SAS programs | macro call, adverse events, proc freq | source | 5 |
| 101 | Freq Out - Proc Freq's Quick Answers to Common Questions | Christine McNichol | PROC FREQ, true to its name, gives frequency counts, as well as other informative statistics, and those frequency values can be output to a dataset. But PROC FREQ can do more than just count. Its ability to provide a unique list and flexibility to use unsorted data can save both time and keystrokes in a variety of scenarios. Combining these features with the out= option, provides another method to add to the programming arsenal for a way to grab a list of subjects or parameters, investigate a difference or do a quick comparison |
proc freq, proc step, data step, proc sort, out= | source | 8 |
| 102 | From Lesion size to Best Response - Implementing RECIST through programming | Ankit Pathak | RECIST stands for Response Evaluation Criteria in Solid Tumors and serves guidance for assessing tumor shrinkage and disease progression, an important endpoint in many Oncology Clinical Trials. Investigators, cooperative groups, industries, and government authorities use RECIST, first published in 2000. Currently the revised version is RECIST v1.1, published in 2008 (Eisenhauer E.A. et al). This paper looks at RECIST v1.1 from a programmer's perspective to derive the Best Overall Response of a subject using collected Lesion data from across multiple visits in an ongoing study and using SAS as a programming language | percent change from baseline, change from baseline, baseline | source | 12 |
| 103 | Generate Customized Table in RTF Format by Using SAS Without ODS RTF - RTF Table File Demystified | Kai Koo | Rich Text Format (RTF), developed and maintained by Microsoft, is a popular document file format supported by many word processors in different operating systems. Because RTF data file is human- readable, experienced programmers can write their own RTF document, including tables and graphs directly based on the RTF specification. Although skilled SAS users can generate high quality RTF tables through ODS RTF and related procedures (e.g., PROC REPORT/TEMPLATE), tables in RTF format can also be produced by SAS DATA STEP without any ODS RTF codes involved. A SAS DATA STEP-based macro for RTF table preparation will be introduced in this article. The basic concept of coding tables in RTF format will be explained. The advanced techniques used in this macro for setting margins, header, footer, title, footnote, font types, highlight, border, column width, cell merging, and different approaches of inserting/embedding image file will also be demonstrated |
sample, macro definition, dataset, fisher exact test, exact test, macro variable | source | 13 |
| 104 | Generating Color Coded Patient ID Spreadsheet (PID list) To Make It Easier for the Reviewers. | Ranjith Kalleda, Ashok Abburi |
This paper is for explaining a technique for displaying changes observed in the patient information by using different colors with the help of a SAS code. This technique is useful in the discovery of the changes observed in the patient's data over a period of time. Most of the data in the illustrations involve data about adverse events, serious adverse events, lab abnormalities and reason for discontinuation. These outputs are produced in EXCEL spreadsheets |
adverse events, sample | source | 5 |
| 105 | Get Smart! Eliminate Kaos and Stay in Control - Creating a Complex Directory Structure with the DLCREATEDIR Statement | Louise S. Hadden | An organized directory structure is an essential cornerstone of data analytic development. Those programmers who are involved in repetitive processing of any sort control their software and data quality with directory structures that can be easily replicated for different time periods, different drug trials, etc. Practitioners (including the author) often use folder and subfolder templates or shells to create identical complex folder structures for new date spans of data or projects, or use manual processing or external code submitted from within a SAS process to run a series of MKDIR and CHDIR commands from a command prompt to create logical folders. Desired changes have to be made manually, offering opportunities for human error. Since the advent of the DLCREATEDIR system option in SAS version 9.3, practitioners can create single folders if they do not exist from within a SAS process | dataset, options statement, excel, macro facility, macro definition | source | 11 |
| 106 | Get to the Meat on Machine Learning | Aadesh Shah | You've probably heard of machine learning and artificial intelligence, but are you sure you know what they are? If you're struggling to make sense of them, you're not alone. There's a lot of buzz that makes it hard to tell what's science and what's science fiction. For many of us, machine learning seems futuristic and scary. Recently, though, it's been showing up, as we hear many new presentations about machine learning at different conferences like Phuse, CDISC Interchange as well as the most famous PharmaSUG 😉. YouTube know which videos you would like to watch in your home section, Facebook recommends local event in your area, also recommends friends, LinkedIn recommends you connect with your ex-boss. And while that's all exciting, some of us are still wondering what exactly machine learning is. This paper will walk you through the process basics, work in practice, Machine Learning vs Artificial Intelligence | machine learning, deep learning | source | 4 |
| 107 | Hands-on Training for Machine Learning Programming | Kevin Lee | The most popular buzz word nowadays in the technology world is "Machine Learning (ML)." Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes such as: self-driving vehicles; online recommendation on Netflix and Amazon; fraud detection in banks; image and video recognition; natural language processing; question answering machines (e.g., IBM Watson); and many more. This is leading many organizations to seek experts who can implement Machine Learning into their businesses. Hands-on Training of Machine Learning Programming is intended for statistical programmers and biostatisticians who want to learn how to conduct simple Machine Learning projects. Hands-on Training will go through the following simple steps. 1. Identify the problems to solve 2. Collect the data 3. Understand the data by data visualization and metadata analysis 4. Prepare data - training and test data 5 | machine learning, logistic regression, data science, neural network, logistic distribution, regression analysis, deep learning, sample, training set, validation set, support vector machine, data cleaning | source | 12 |
| 108 | Have You Met Define.xml 2.0? | Christine McNichol | Define.xml is critical for a reviewer getting to know a study's datasets. The define.xml facilitates the building of this relationship with the study data from casual introduction through the innermost workings of the datasets. To provide the best possible define.xml for reviewer's use, it is important to be comfortable with define.xml and how it communicates information about the datasets. Though define.xml might be intimidating at first, fear not. Following the flow through define.xml to get to know the d ata is like getting to know a new friend. Define.xml reveals levels of information about the study datasets from structure to details about where the values came from, even linking to documentation of issues encountered and decisions made. The anatomy of a define.xml, its purpose, what is special about each section and what can be learned about the study datasets from each will be discussed. Meet define.xml - friend, not foe | adam dataset, format statement, sample, controlled terminology, cdisc | source | 15 |
| 109 | Heat Map and Map Chart using TIBCO Spotfire | Ajay Gupta | TIBCO Spotfire is an analytics and business intelligence platform which enables data visualization in an interactive mode. Users can create heat maps and map charts using inbuilt functions in Spotfire. The easiest way to understand a heat map is to think of a cross table or spreadsheet which contains colors instead of numbers. The default color gradient sets the lowest value in the heat map to dark blue, the highest value to a bright red, and mid-range values to light gray, with a corresponding transition (or gradient) between these extremes. Heat maps are well-suited for visualizing large amounts of multi- dimensional data and can be used to identify clusters of rows with similar values, as these are displayed as areas of similar color. Patterns in heat maps are clear, because colors are used to display the frequency of observations in each cell of the graph. Also, Map chart can be useful to show the population density in the world map | heatmap, scatter plot, data visualization, adverse events, proc freq | source | 14 |
| 110 | History Carried Forward, Future Carried Back: Mixing Time Series of Differing Frequencies | Mark Keintz | Many programming tasks require merging time series of varying frequency. For instance you might have three datasets (YEAR, QTR, and MONTH) of data, each with eponymous frequency and sorted by common id and date variables. Producing a monthly file with the most recent quarterly and yearly data is a hierarchical last-observation-carried-forward (LOCF) task. Or you may have three irregular times series (ADMISSIONS, SERVICES, TESTRESULTS), in which you want to capture the latest data from each source at every date encountered (event-based LOCF). These are tasks often left poorly optimized by most SQL-based languages, in which row order is ignored in the interests of optimizing table manipulation. This presentation shows how to use conditional SET statements in the SAS DATA STEP to update specific portions of the program data vector (i.e. the YEAR variables or the QTR variables) to carry forward low frequency data to multiple subsequent high frequency records |
set statement, time series analysis, data step, proc sql, proc freq, proc step, date variable, merge statement, call missing, last observation carried forward | source | 11 |
| 111 | Homogenizing Unique and Complex data into Standard SDTM Domains with TAUGs | Sowmya Srinivasa Mukundan, Charumathy Sreeraman |
Clinical research supports discovery of new and better ways to detect, diagnose, treat and prevent disease. Furthermore, the core focus of each therapeutic area (TA) is on research and development of treatments, together with prevention of specific diseases. It must be envisaged that each TAs demands diverse way of collecting, measuring and analyzing data based on the focus of the research. SDTM is one of the pioneer CDISC foundational standards. It defines and underpins the strategy for submitting data tabulations to regulatory authorities. The SDTMIG organizes and formats data to support streamlined data collection and analysis across different TAs. TAUGs are extended Foundational Standards to represent data that pertains to specific disease areas. It supports pharmaceutical / biotech companies with imp lementation of these CDISC standards for a specific disease and facilitate resolutions for mapping additional or unique data points needed to support any given TA for their analysis | sample, sdtm programming, disease response, tumor identification, sdtm domain, cdisc | source | 10 |
| 112 | How to Build a Complicated Patient Profile Graph by Using Graph Template Language: Turn Mystery to a LEGO Game | Ruohan Wang, Chris Qin |
As a visual report, graphical patient profile can improve the readability of correlated data. It is desirable to use for submissions and publications as a way to comprehensively present safety and efficacy data. A common time frame is usually used to combine multiple forms of data, such as lab results, vital signs, and clinical events with demographic information. This type of graph can be highly complex and customized to achieve reviewers' needs. SAS's Graph Template Language (GTL) is a powerful and flexible tool used to draw these graphs. This paper provides instructions to start building with GTL, and how to combine various pieces together like playing a LEGO game. Real world exam ples are given to allow the readers follow step-by-step with notes. The examples are generated by SAS 9.4 and in publication quality | study day, scatter plot, bar chart | source | 24 |
| 113 | How to use SUPPQUAL for specifying natural key variables in define.xml? | Sergiy Sirichenko | Define.xml must identify natural keys for each dataset to specify uniqueness for records and sort order. Sometimes standard SDTM/SEND variables are not enough to completely describe the structure of collected study data. In this presentation, we will show examples and recommend appropriate strategies for using SUPPQUAL variables or other common alternatives in the natural key. We will also provide guidance on how to document SUPPQUAL natural keys in define.xml | natural key, surrogate key, cdisc | source | 8 |
| 114 | Implementing CDISC Standards for Device-Drug Studies | Karin LaPann, Wenying Tian |
Devices can be part of a drug or biologic submission as a method to deliver drug. The regulatory agencies request that the device be either studied separately or as a drug-device combination product. The study teams are then able to apply these standards to create standardized collection forms and datasets beginning-to-end, from CDASH to ADaM. In this case study of an implementation we will describe how we created ADaM device dataset standards using ADaM principles. The data standards at our company were developed using a "beginning to end" approach. That means we started by designing appropriate data collection forms using the CDISC CDASH principles. The device data collection forms were developed in 2012, for our CDASH forms library, and are still in use today. Then we created standards and guidance for mapping the seven SDTM device domains. These followed the guidance of SDTMIG-Medical Devices v1.0 of 2012-12-04 | data governance, sdtm domain, cdisc | source | 10 |
| 115 | Implementing Laboratory Toxicity Grading for CTCAE Version 5 | Keith Shusterman, Mario Widel |
Laboratory toxicity grading has been an important part of safety reporting since the FDA began accepting electronic data in 1999. Staying up to date with the various CTCAE versions can be a challenge. CTCAE Version 5.0 adds a layer of complexity with new grading criteria dependent on baseline measurements. We will present a practical method for deriving toxicity grades in the SDTM LB domain based on the new CTCAE, as well as reporting toxicity events in an OCCDS dataset derived separately from the BDS dataset with the laboratory findings | ctcae grade, baseline, macro variable, macro definition, call execute, sample, dataset, adverse events, sdtm domain, cdisc | source | 11 |
| 116 | Importing EXCEL Data in Different SAS Maintenance Release Version | Huei-Ling Chen, Chao-Min Hoe |
It was noted that using PROC IMPORT procedure to convert the same EXCEL data file to SAS dataset, the outputs could be inconsistent from different computers. The objective of this manuscript is to investigate the inconsistencies and to provide explanations and solutions. Several EXCEL data files were tested. It was observed that different outcomes were due to the SAS version or the maintenance release on different PCs |
excel, proc import, error message, proc step, dataset, automatic macro variable | source | 7 |
| 117 | Improving the Relationship between Statisticians and Programmers in Clinical Trial Studies | Mai Ngo, Mary Grovesteen, Vaughn Eason |
Successful deliveries of analysis outputs for a clinical trial depends on a strong biostatistics team, which typically includes a study statistician and a programming team with several statistical programmers. As trials get more complex and biostatistics teams face increased pressure to produce outputs efficiently and on a timely basis, a strong working relationship between the study statistician and the programming team is vital to the success of the analysis project. Yet with the time pressure and the increased complexity of the analysis as well as challenging data issues common in clinical studies, the communication between the statistician and programmers tends to break down when it is needed the most. This results in frustration from both sides, inefficiencies that could have been avoided, and stressful last-minute work and rework. Having worked both as statistical programmer and statistician, we have been fortunate to gain valuable hands-on perspectives from both sides | clinical trial, mock shell, final analysis, data cleaning, clinical data, data science | source | 8 |
| 118 | In the Style Of David Letterman's "Top Ten" Lists, Our "Top Ten" PROC SQL Statements To Use in Your SAS Program | Margie Merlino | One of the challenges in using PROC SQL is the notion that a programmer must first immerse him or herself in the education of Structured Query Language (SQL) before they can use PROC SQL. Certainly, there is syntax that a programmer must adhere to for successful execution a PROC SQL statement, but we propose that there are many simple and some not so simple statements that can be used without a robust knowledge of all the intricacies of SQL. What follows is a "Top Ten List" ala David Letterman's popular feature. We list ten scenarios and a PROC SQL example that addresses that scenario and can be used to manipulate the SAS data and display a result or provide an output dataset for further manipulation. Our goal is to provide code examples that a programmer ca n easily copy/paste into their own SAS program |
proc sql, proc step, create table, macro variable, cross join, adverse events, group by | source | 7 |
| 119 | Incremental Changes: ADaMIG v1.2 Update | Nancy Brucken, Brian Harris, Terek Peterson, Alyssa Wittle, Deborah Goodfellow |
The ADaM Implementation Guide (ADaMIG) has now been available to industry since 2009, providing a standardized way to communicate and analyze study data. Improvements and clarifications were added in 2016 with the release of v1.1. Since that release, the ADaM team has been working on some items that were not yet ready for v1.1 but are now ready for the next release, v1.2. These items include important clarifications to existing text, standard nomenclature for stratification variables within ADSL, and a recommended approach for bi-directional toxicity grades. In addition, an update on the removal of the new suggested permissible variable within the Basic Data Structure (BDS) called PARQUAL will be provided. The ADaMIG v1.2 will be discussed from both the perspective of changes from v1.1 as well as changes made since the public review of v1.2 | adam dataset, ctcae grade, adverse events, cdisc | source | 8 |
| 120 | Information Requests During An FDA Review | Hong Qi, Lei Xu, Mary N. Varughese |
Filing a marketing application is a pivotal and exciting milestone for the long -term effort of product development. Even though there are industry standards to follow, the requirements and processes of submissions vary among products, indications, and sponsoring companies. Despite the extraordinary efforts in preparing a comprehensive submission package, regulatory agencies often issue information requests (IRs) from the pre-supplemental biologics license application (pre-sBLA) meeting, during the application review, and labeling process. IRs may involve different aspects of the product including the target indication, patient population, safety profile, reviewers' scientific interest on getting further information on the potential benefits of the medicine, additional case-report forms, and/or even the collected previous therapies not included in the ADaM datasets | adam dataset, dataset, cdisc | source | 8 |
| 121 | Innovative Technologies utilization in 21st Novel Clinical Research programs towards Generation of Real World Data. | Srinivasa Rao Mandava | R&D budgets of Pharmaceutical industry have been increasing year after year with oncology and metabolic disease drug developments as lead engines. However, return on R&D investments reduced from 10.1 in 2010 to 3.2% in 2017. On the other side, Technology utilization is increasing in the same phase for better handling and cost & time reduction scenarios. Current $ 60 billion clinical research market is extremely slow and there by expensive in some ways. Outdated data technics, confusion and confrontation over eligibility, large numbers of subjects drop-out rates are the prime reasons for longer trial times of about 10 years. For the last few years digital technologies utilization across the industry is increasing with three focused areas such as 1. Engage 2. Innovate 3. Execute in dealing with three key groups such as a) patients b) providers c) payers | clinical research, clinical trial, pharmaceutical industry, insurance claims | source | 6 |
| 122 | Integrating programming workflow into computing environments: A closer look | Tyagrajan Swaminathan, Sridhar Vijendra |
System-level workflows are well known to improve process compliance, increase traceability and enhance audit trails in various domains including document management. It is time programming workflows are made integral to the domain of statistical programming for clinical trials to enhance productivity, reduce management overhead, ease tracking of large programming deliverables produced by globally dispersed teams and consequently, reduce time-to-market for every drug. System-level workflows are mapped based on the corresponding real-world processes that they model but conventional statistical computing environments (SCEs) that most programmers are used to hardly allow for automatic record keeping of processes taking place within the environment. What is recorded in a disparate programming status tracker has scope for human error since there is a possible lag and/or a gap between what is done in the programming environment and what is mentioned in the status tracker | audit trail, clinical programming, independent qc, sample | source | 11 |
| 123 | Integrating SAS and Microsoft Excel: Exploring the Many Options Available to You | Vincent DelGobbo | This paper explains different techniques available to you when importing and exporting SAS and Microsoft EXCEL data. You learn how to import EXCEL data into SAS using the IMPORT procedure, the SAS DATA STEP, SAS Enterprise Guide, and other methods. Exporting data and analytical results from SAS to EXCEL is performed using the EXPORT procedure, the SAS DATA STEP, SAS Enterprise Guide, the SAS Output Delivery System (ODS), and other tools. The material is appropriate for all skill levels, and the techniques work with various versions of SAS software running on the Windows, UNIX (including Linux), and z/OS operating systems. Some techniques require only Base SAS and others require the SAS/ACCESS Interface to PC Files |
excel, sas enterprise guide, proc step, variable, dataset, proc import, sas base, proc export, datetime, delimited file, xls, libname engine, proc print, label statement, sample, datetime variable, format statement, proc contents, proc sql | source | 23 |
| 124 | Interactive Graphs | Kriss Harris, Richann J. Watson |
This paper demonstrates how you can use interactive graphics in SAS 9.4 to assess and report your safety data. The interactive visualizations that you will be shown include the adverse event and laboratory results. In addition, you will be shown how to display "details-on-demand" when you hover over a point. Adding interactivity to your graphs will bring your data to life and help improve lives! | change from baseline, bar chart, series plot, box plot, adverse events, sample, macro variable, scatter plot, macro loop, by group processing | source | 24 |
| 125 | Interactive TLFs - A Smarter Way to Review your Statistical Outputs | Bhavin Busa | In the clinical industry, the data is represented and analyzed in the form of tables, listings and figures (TLFs) which are typically generated using SAS. For the Biometrics team, plotting data and presenting statistical information using SAS ODS and SAS procedures is not new. However, the output that is generated using SAS ODS and SAS procedures are for the most part static and only gives end users an ability to "look" at the output without giving them an ability to explore and drill-down further. With the rapidly increasing availability of data visualization and analytic tools, the industry landscape is shifting. In addition, the end users now want to 'see' their data more interactively, identify trends, visualize the patient profiles and review results at a high-level while still being able to drill -down to get a complete picture |
adverse events, clinical trial, treatment arm, data visualization, sdtm domain, raw data, subject-level data, concomitant medications, cdisc | source | 5 |
| 126 | ISS Challenges and Solutions for a Compound with Multiple Submissions in Parallel | Aiming Yang | Integrated Summary of Safety (ISS) is required for new and supplemental drug or biologic applications. For an oncology compound with multiple indications and tens of ongoing trials, challenges seeking sound strategies and execution of integrating trials are numerous. In this paper, we share some challenges and successful executed solutions. There are two parts in the paper. In part I, we share the challenges and solutions of establishing a centralized ISS team. To address the needs of the multiple submissions and filings of this compound, we first established a dedicated ISS team. The specialization enables the same team to work on multiple ISS packages, with successive or concurrent timelines in turnarounds of several weeks. In part II, we share some essential established programming techniques. The ISS programs we use can accommodate multiple versions of SDTM source datasets and meet the needs of today's CDISC standards filing requirements | adam dataset, statistical programmer, cdisc | source | 4 |
| 127 | It's a wonderful day in this neighbourhood - Managing a large virtual programming team | Victoria Holloway | Programming can be lonely, isolated work within a large virtual team. Whether in the office or at home, everyone needs to be part of something bigger. Creating a virtual neighborhood-style community center enables the programmers to interact with each other as neighbors. Community is the first step for building trust and respect through experience and successes. Important public works projects including infrastructure, such as electricity, water, phone, and internet are essential to the community and can be likened to training, code sharing, work breaks and process improvements. Nothing happens unless there are funds to support these endeavors, so linking the team to the financial aspects is also key to running successful projects. Counting the beans includes how to track the work and project management tools | source | 6 | |
| 128 | It's All About the Base-Procedures | Jane Eslinger | As a Base SAS programmer, you spend your day manipulating data and creating reports. You know there is a procedure that can give you what you want. As a matter of fact, there is probably more than one procedure to accomplish the task. Which one should you use? How do you remember which procedure is best for which task? This paper is all about the Base procedures. It explores the strengths of the commonly used, nongraphing procedures. It discusses the challenges of using each procedure and compares it to other procedures that accomplish similar tasks. The first section of the paper looks at utility procedures that gather and structure data: APPEND, COMPARE, CONTENTS, DATASETS, FORMAT, SORT, SQL, and TRANSPOSE. The next section discusses the Base SAS procedures that work with statistics: FREQ, MEANS/SUMMARY, and UNIVARIATE. The final section provides information about reporting procedures: PRINT, REPORT, and TABULATE | proc step, proc sql, proc contents, proc freq, proc report, proc tabulate, proc compare, proc transpose, proc append, proc print, proc format, proc sort, sas base, proc datasets, proc means, proc univariate, create table, dictionary tables, macro variable | source | 20 |
| 129 | Joining the SDTM and the SUPPxx Datasets | David W. Franklin | When creating ADaM datasets from SDTM datasets, programmers often do not understand why we have the supplemental SUPPxx datasets and how to deal with the structure of the data with these datasets. This paper will briefly explain why we have these supplemental datasets, and more importantly, introduce a macro that will bring these to their 'parent' and make their use easier to handle | sdtm domain, safety population, cdisc | source | 9 |
| 130 | Jump Start your Oncology knowledge | Xiaoyin Zhong, Feng Liu |
Clinical trial in oncology provides an exciting work environment among programmers due to its uniqueness in many cancer types, innovative design, and specific efficacy analysis. As oncology programmers in pharmaceutical industry or academics, it is critical to understand and interpret the disease jargon and statistical analysis to better communicate with statisticians, physicians/clinicians, regulatory, and other study personnel effectively. The purpose of this paper is to introduce key concepts, study design and analysis in oncology to jump start your knowledge base for new oncology programmers or experienced programs who are new to oncology. This paper will highlight definition of oncology, uniqueness of oncology drug development, and emerging treatment options for oncology as well as overview of study design including traditional and innovative design s uch as master protocol design. In addition, oncology efficacy evaluation and data standard will be presented | clinical trial, progression-free survival, overall survival, complete response, phase i, randomization, phase ii | source | 9 |
| 131 | Large-scale TFL Automation for regulated Pharmaceutical trials using CDISC Analysis Results Metatadata (ARM) | Stuart Malcolm | The creation of a Clinical Study Report (CSR) for Phase II/III Pharmaceutical clinical trial involves the production of several hundred Tables, Figures and Listings (TFL). This can be a time-consuming activity when each TFL is programmed manually. While some TFL are common for many studies, there is always a requirement to create study-specific TFL. In addition, CDISC Analysis Results Metadata (ARM) Define.xml is often requested at the end of the trial. This paper outlines an approach to TFL automation that involved creation of the CDISC Analysis Results Metadata at the start of the process, not the end, and uses this metadata to generate the TFL. A SAS program structure is described that allows standard TFL to be created while also providing flexibility to easily incorporate study-specific analyses | proc step, clinical trial, csr, macro variable, macro definition, proc report, data cleaning, cdisc | source | 16 |
| 132 | Learning SAS GTL Basics with Practical Examples | Jinit Mistry | Over time, the business needs for data visualization have evolved due to the increased complexity of clinical trials. There has been an increased demand for complex graphs as part of clinical study reports, based on statistical analysis plans. For efficacy endpoints of oncology trials, complex graphs such as KM plots, forest plots, waterfall plots, spider plots, and box plots require creating more robust and customizable SAS code. To address this need, various statistical graphics (SG) procedures and Graph Template Language (GTL) have been introduced in SAS. SAS SG procedures use an associated GTL template in the background because SG procedures have limited ability to create graphs. This makes GTL critical to understand in order to create customizable templates and associate them with the data to accomplish the goals of the analyses. GTL can create more customizable graphs over SG procedures to address such complex graphical needs | proc step, proc template, macro variable, hazard ratio, overall survival, proc sgplot, kaplan-meier plot, output delivery system | source | 12 |
| 133 | Lessons Learned from Teaching 250+ Life Science Analytics (LSAF) Classes to Our Colleagues at Janssen Research and Development. | Margie Merlino, Jeanne Keagy |
In 2014 Janssen purchased SAS Drug Development (SDD), later renamed Life Sciences Analytics Framework (LSAF), from SAS Institute and successfully implemented the system into their Data Management (DM) processes for uploading and validating SDTM datasets. The following year, Janssen expanded the use of LSAF to their Data Analysis (DA) processes for creating ADaM datasets, TFL's and ad-hoc analyses and simulations with study data. Prior to the use of LSAF, Janssen's DA group used Windows SAS and an in-house developed user interface to manage the activities related to clinical trial deliverables and ad -hoc analyses. Using LSAF for both DM and DA had many advantages such as both groups could access one central repository to view all study data, interim and database locks. However, despite the success of LSAF within DM, the DA transition to the LSAF environment did not go well | statistical programmer, clinical trial, cdisc | source | 7 |
| 134 | Let Your Log Do the Work for You | Yuliia Bahatska, Vladlen Ivanushkin |
Of course, there is no magic trick that will completely free you from writing the code, at least the authors of this paper are not aware of such. However, the tips we suggest can help the programmers to avoid struggling through technical details and let them concentrate on more sophisticated tasks which require deep understanding of the subtleties of programming. In this paper we will show different scenarios where the information such as ODS table names or template specifics can be put into the log or read from the log by a couple of lines of code. This means that you can use this part of your brain's CPU to learn something else |
cpu time, proc template, proc step, proc sql | source | 10 |
| 135 | Let's Flip: An Approach to Understand Median Follow-up by the Reverse Kaplan-Meier Estimator from a Programmer's Perspective | Nikita Sathish, Chia-Ling (Ally) Wu |
In time-to-event analysis, sufficient follow -up time to capture enough events is the key element to have adequate statistical power. Achieving an adequate follow -up time may depend on the severity and prognosis of the disease. The median follow -up is the medi an observation time to the event of interest, which is an indicator to see the length of follow -up. There are several methods to calculate median follow - up, and we have chosen to use the more robust and code-efficient reverse Kaplan-Meier (KM) estimator in our paper. Median follow-up is a less commonly presented descriptive statistic in reporting survival analysis result, which could pose some challenges to understand. This paper aims to provide the concept of median follow- up, the statistical interpretation of median follow-up both numerically and visually, and the SAS LIFETEST procedure to delineate survival plots and compute survival function using the reverse KM estimator |
kaplan-meier estimator, survival analysis | source | 5 |
| 136 | Leveraging Intermediate Data Sets to Achieve ADaM Traceability | Yun (Julie) Zhuo | Traceability, a fundamental principle of ADaM, provides transparency and increases confidence for the FDA reviewers. Building traceability could be a daunting task for a complex ADaM data set especially when it involves multiple data sources and multi-step derivations. In this paper, we illustrate the benefits of using an intermediate data set to achieve traceability using example SDTM and ADaM data sets from a Phase III oncology study. Through the intermediate data set, along with its metadata, it is possible to trace the final analysis value to a record in the intermediate data set, and then from there to the source domains | protocol deviations, flag variable, date variable, imputation, final analysis, data cleaning, survival analysis, sdtm domain, cdisc | source | 12 |
| 137 | Lit Value Locator: An efficient way to pinpoint data. | Varunraj Khole | This paper describes a method to locate any alphanumeric text string, in character or numeric format within any dataset present in default as well as user defined SAS libraries. Often a times, data presented to us comes in different format and is stored in multiple locations. To ensure that all the required data is captured, a user needs to implement smart coding which can find the required data and give its location. This method would be very useful while searching for a particular data string when the user is working with multiple SAS libraries. This method can be an essential tool for a data analyst working in finance, pharmaceuticals, life sciences, healthcare, banking and various other industries. By combining powerful SAS macro language, procedures, efficient do loops and handful of extremely useful SAS functions this method will provide the user with a detailed description of the search value and provide summary statistics for the same | macro parameter, ods html, work library | source | 11 |
| 138 | Logistic and Linear Regression Assumptions: Violation Recognition and Control | Deanna Naomi Schreiber-Gregory, Karlen Bader |
Regression analyses are one of the first steps (aside from data cleaning, preparation, and descriptive analyses) in any analytic plan, regardless of plan complexity. Therefore, it is worth acknowledging that the choice and implementation of the wrong type of regression model, or the violation of its assumptions, can have detrimental effects to the results and future directions of any analysis. Considering this, it is important to understand the assumptions of these models and be aware of the processes that can be utilized to test whether these assumptions are being violated. Given that logistic and linear regression techniques are two of the most popular types of regression models utilized today, these are the ones that will be covered in this paper. Some Logistic regression assumptions that will reviewed include: dependent variable structure, observation independence, absence of multicollinearity, linearity of independent variables and log odds, and large sample size | regression analysis, logistic regression, proc reg, predictor variable, logistic distribution, correlation, sample, outlier, response variable, sample size, cook's distance, scatter plot, covariance, multicollinearity, standard deviation, proc calis, box plot, condition index, proc step, normality test, proc glm | source | 21 |
| 139 | Machine Learning Approaches to Identify Rare Diseases | Xuan Sun, Ruohan Wang |
Rare diseases are very difficult to identify and diagnose than other diseases, since there are not enough data and experts in rare diseases. Better availability of patient data and improvement in machine learning algorithms empower us to tackle this problem computationally. In this paper, we adapt state of the art machine learning algorithms to make this classification, such as K-nearest neighbors, Support Vector Machine, Neural Networks and Naive Bayes. We find that using these machine learning methods, we can identify people with rare diseases with low misclassification rate | machine learning, neural network, missing value, confusion matrix, data cleaning, training set, support vector machine, distance measure, sample | source | 5 |
| 140 | Macro Templates - Industry Specific SAS Programming Standardization | Tabassum Ambia | Industries are increasingly focusing on developing their own sets of SAS macros to support development of datasets (SDTM, ADaM), Tables, Listings, Figures (TLFs) and proceeding towards their own standardization. The use of job-specific standard set of macros affects production time, resources required, programmers' learning opportunities, debugging time and the quality of output. There are pros and cons of using standardized macro templates. This is a quasi-automated process which largely cuts down the effort and amount of time required for programming that helps to complete a huge amount of work within a short time frame, outputs remain consistent and reduce probable programming errors. Stringent use of standardized automated macros also has its challenges which includes limitation of outputs different from standard ones where programmers need to circumvent macros, time required to debug errors, training required for new programmers etc | macro variable, proc report, adverse events, adam dataset, proc step, data cleaning, macro definition | source | 8 |
| 141 | Making Lab Toxicity Tables Less Toxic on Your Brain | Lindsey Xie, Jinlin Wang, Jennifer Sun, Rita Lai, Richann J. Watson |
Processing and presenting lab data is always challenging, especially when lab limits are assessed in two directions. The lab data process becomes even more complicated when multiple baselines are required due to different analysis criteria or are inherent in the study design. This paper discusses an approach to create lab toxicity grade variables in ADLB for lab bi-directional toxicity report. They are mixed variables defined by Clinical Data Interchange Standards Consortium (CDISC) Analysis Data Model Implementation Guide (ADaMIG) v1.1 and draft ADAMIG v1.2 and by sponsor to make ADLB easily interpreted and related summary tables easily produced. This paper is based on the lab Common Terminology Criteria for Adverse Events (CTCAE) toxicity grade summary, taking into account lab tests with abnormal assessment in either increased direction or decreased direction | ctcae grade, analysis set, baseline, treatment period, worst value, raw data, laboratory data, data science, adam dataset, cdisc | source | 13 |
| 142 | Making the Days Count: Counting Distinct Days in Overlapping or Disjoint Date Intervals | Noory Y. Kim | Suppose you want to combine multiple date intervals. How do you avoid double-counting calendar days when the intervals overlap? Previous papers on this topic have followed a sensible "combine-then-count " method in principle, but in practice have presented code implementations whose calculations are not easy to follow. This paper presents a macro impleme ntation that follows the "combine-then-count" paradigm more closely for the purpose of having calculations that are easier to follow and verify | out=, date variable, group by, proc sql, proc step, sample, create table, macro call, stage 1, stage 2 | source | 12 |
| 143 | Merging Sensor Data with Patient Records in Clinical Trials - Systems and Benefits | Surabhi Dutta, Shubhranshu Dutta |
Patient care involves data capture in all phases of care delivery including clinical trials, provider visits, lab work, sensor data from wearable sensors and patient generated data from hand held devices. We are accustomed to devices that generate health indicator data in large volumes and rapid rate. This paper discusses the benefits, challenges and methods of merging these disparate datasets characterized by volume, variety, velocity. The process of healthcare in the current scenario is a one-way system where patient health indicator data is entered in the Electronic Health Records by a healthcare practitioner. This one sided approach is generally based on the health conditions at the date and time of patient and provider contact. While the purpose of Electronic Health Records is recording patient data during a visit to the provider, patient reported outcomes are often ignored in the process | adverse events, clinical data, time series analysis, clinical trial, flat file, concomitant medications | source | 9 |
| 144 | Metadata Repository V1.0 - A Case Study in Standards Governance | Aparna Venkataraman | Over the last couple of decades Metadata Repository (MDR) tools have been playing a growing role in the Pharmaceutical/Bio-Technology space. It has been long since Regulatory agencies as well as the sponsor companies have, for various reasons, widely recognized the need for integrating data standards starting at Protocol Development and Data Collection all the way through to Submission. However, we still face tremendous challenges in maintaining and managing Clinical Data Standards that fall far beyond the capabilities of any single MDR tool that is currently available in the market. We will explore in this paper, the tools that need to be in the Standards Manager's toolbelt to successfully dispense End-to-End Clinical Data Standards across the company In the multi-dimensional world of Clinical Data Standards, a nimble Governance Model will play an undeniable role in getting that critical drug in the hands of the patient at the right time | data standard, clinical trial, metadata management | source | 7 |
| 145 | Minimally Invasive Surgery to Merge Data | Paul D. Hamilton | Many professional papers on the topic of combining data sources have been presented at SAS User conferences over the decades. Most focus on the issue of combining data through the DATA STEP (set, merge, update) or on joining in SQL. However, there is another, completely different technique using SAS Component Language functions that is optimal for a small range of applications. This paper will present the technique (used in a DATA STEP and in macro language), explain how it differs from traditional data combination techniques, and offer specific examples illustrating where it might be useful. The technique can also be used to extract either metadata or actual data from SAS data sources. Former developers of SAS/AF will recognize the technique; Base and macro programmers will be amazed! |
proc step, clinical trial, concomitant medications, sysrc, proc sql, macro facility, macro definition | source | 4 |
| 146 | More Traceability: Clarity in ADaM Metadata and Beyond | Wayne Zhong, Richann J. Watson, Daphne E. Ewing, Jasmine Zhang |
One of the fundamental principles of ADaM is that datasets and associated metadata must include traceability to facilitate the understanding of the relationships between analysis results, ADaM datasets, and SDTM datasets. The existing ADaM documents contain isolated elements of traceability, such as including SDTM sequence numbers, creating new records to capture derived analysis values, and providing excerpts of define.xml documentation. An ADaM sub-team is currently developing a Traceability Examples Document with the goal of bringing these separate elements of traceability together and demonstrate how they function in detailed and complete examples. The examples cover a wide variety of practical scenarios; some expand on content from other CDISC documents, while others are developed specifically for the Traceability Examples Document | adverse events, sample, adam dataset, sdtm domain, proc contents, imputation, safety population, cdisc | source | 26 |
| 147 | Multiple Studies BIMO Submission Package - A Programmer's Perspective | Ramanjulu Valluru, Harsha Dyavappa |
As support documentation of its Bioresearch Monitoring (BIMO) activities, FDA's Center for Drug Evaluation and Research (CDER) requests that sponsors of new drug applications (NDAs), biologics license applications (BLAs), and NDA or BLA supplemental applications containing clinical data provides the following three items: Clinical Study -Level Information; Subject-Level Data Line Listings by Clinical Site; | clinical trial, excel, adam dataset, sample, per protocol, adverse events, clinical data, cdisc | source | 17 |
| 148 | Next Innovation in Pharma - CDISC data and Machine Learning | Kevin Lee | The most popular buzz word nowadays in the technology world is "Machine Learning (ML)." Most economists and business experts foresee Machine Learning changing every aspect of our lives in the next 10 years through automating and optimizing processes. This is leading many organizations including drug companies to explore and implement Machine Learning on their own businesses. The presentation will discuss how Machine Learning can lead the next innovation in pharma with CDISC data. The presentation will start with the introduction of most innovative companies and how they innovate and lead the industry using Machine Learning and data. Then, the presentation will show how pharma should learn from them to innovate using Machine Learning and CDISC data. The presentation will also introduce the basic concept of machine learning and the importance of data. In the Machine Learning/AI driven process, data is considered as the most important component | machine learning, clinical data, clinical trial, pharmaceutical industry, cdisc | source | 5 |
| 149 | Non-Clinical (SEND) Reference Guide for Clinical (SDTM) Programmers | Dharmendra Tirumalasetti, Bhavin Busa |
The U.S. FDA now requires the use of standardized data submission, SEND (Standard for Exchange of Nonclinical Data), for non-clinical data. Many Sponsor companies have started preparing SEND datasets towards their upcoming submissions, although, they still lack the much-needed expertise to get their data submission-ready. In our experience, one of the reasons could be due to lack of available resources/subject matter experts in the Non-clinical team within an organization. One of the solutions to overcome the resourcing challenges is to utilize existing pool of Clinical (SDTM) Programmers. In this paper, our intent is to provide a quick reference guide for Clinical (SDTM) Programmers to develop SEND domains for non-clinical studies. We will present commonalities and differences between SDTM and SEND domains. In addition, we will summarize our experience and lessons learned with performing mapping and standardization of non-clinical legacy studies to make it submission-ready | clinical data, clinical trial, raw data, cdisc | source | 6 |
| 150 | Not That Dummy Data | Yuliia Bahatska | When it comes to data visualization it is expected that the data is presented the way it is stored in the analysis dataset. Labels may be changed, formats may be applied, or some derivations may be made, but definitely programmers are not expected to add the values to the data. But is it always true? SAS has quite a wide range of ways to create figures and usually at least one of the graphical procedures or Graph Template Language (GTL) can provide you with the required output, but sometimes you still need a workaround. In this paper we will concentrate on the cases when in order to present the data in the desired way it is helpful to add artificial or dummy values | proc means, group by | source | 10 |
| 151 | ODS Magic: Using Lesser Known Features of the ODS statement | Michael Stout | It is no allusion, the ODS output destination is a powerful tool that every SAS programmer should know how to use. This paper will provide details on lesser known aspects of ODS that are magical. With a slight of hand, this paper will show how to send desired SAS output to multiple output destinations. Learn techniques to combine tables, listings and figures to a single output file. What you thought was impossible, may be easy using lesser known features of ODS |
file statement, ods rtf | source | 6 |
| 152 | One Macro to Produce Descriptive Statistic Summary Tables with P-Values | Rajaram Venkatesan, Harinarayanan Gopichandran |
In clinical trial reporting, the most popular type of tables is those that have descriptive statistics (n, mean, SD, median, min and max, CI, and p-values) or tables having a frequency (%) count and descriptive statistics of categorical and continuous variables. These are the bread and butter of reporting. However, producing these tables is simple yet trivial, and sometimes cumbersome and time -consuming, as many variables and many conditions might be requested. The solution is to create a simple macro and easy to understand macro, which allows the user to develop and produce descriptive summary tables within minutes. This can be used to produce or validate most safety tables without any problems. This allows users to create many types of tables (dem ographics and baseline characteristics, laboratory, vital signs, and ECG data) with minimal effort. It also means that when statisticians would like to change the table afterward, it can be done with the minimum of effort | confidence interval, proc means, geomean, interval variable, standard deviation, proc calis, fisher exact test, nominal variable, exact test, anova | source | 14 |
| 153 | One More Paper on Dictionary Tables and Yes, I Think it Is Worth Reading | Vladlen Ivanushkin | Before writing this paper on dictionary tables I made some research on what was already out there so that I won't duplicate someone else's work. I found quite a number of papers, but I still decided to write my own and to concentrate on how programmers can benefit from using dictionary tables in their everyday life. In this paper I would like to share with you the tasks I actually faced during my work as a statistical programmer and how using dictionary tables makes it so much easier to deal with them. There is quite a variety of them from creating macros to programming STDMs | dictionary tables, macro variable, proc sql, proc step, create table, macro definition, full join | source | 11 |
| 154 | Order, Order! Four Ways To Reorder Variables with SAS, Ranked by Elegance and Efficiency. | Louise S. Hadden | SAS practitioners are frequently required to present variables in an output data file in a particular order, or standards may require variables in a production data file to be in a particular order. This paper and presentation offer several methods for reordering variables in a data file, encompassing both DATA STEP and procedural methods. Relative efficiency and elegance of the solutions will be discussed |
dataset, proc sql, proc step, length statement, proc contents, array, attrib statement, informat statement, sample | source | 22 |
| 155 | Pacemaker Guy: De-Mystifying a Business Use Case for SDTM Standard and Medical Device Domains | Carey G. Smoak, Donna Sattler, Fred Wood |
Medical-device standards can be applied to even the most complicated clinical research studies with medical devices. There are many papers written on how to map certain kinds of data like exposure data or lab data using SDTM-based standards. Not has been written however, on how to represent medical-device data using these standards. In developing medical-device standards, considerations were made for simple and complex data points when mapping to the SDTM standards. Just like in pharmaceutical and biological products; one needs to plan for the unexpected with medical device studies. This paper will take the reader through a subject experience by showing the mappings of the data but also illustrate the procedure(s) and how to visually map the data. The goal is to leave the participant/reader with a curiosity to want to map their own medical device data to standards sooner than what the current expectation is | sdtm domain, adverse events, clinical trial, cdisc | source | 10 |
| 156 | Pains and Gains in software development - from PoC to Market | Reshma Rajput, Charan Kumar Kuyyamudira Janardhana |
Software development in clinical domain has its challenges in developing products and deploying new technologies. The challenges could be internal such as hiring, training, managing internal expectations within the development, testing and related teams. A lso, challenges could be external such as meeting customer expectations. During the Proof of Concept (PoC) phase of a project, extensive demonstrations of the product and technologies used are presented to prospective clients. Major issue here is aligning the product knowledge with prospective clients who have very less knowledge of the use cases of the product and in few cases, the domain too, and business takes priority weakening the stance of technological value addition in the long run post implementati on. Another momentous challenge is lack of enough documentation during the different phases of the project, from PoC to deployment | excel, data cleaning | source | 6 |
| 157 | Patient Profile with Color-Coded Track Changes Since Last Review | Himanshu Patel, Jeff Xia |
Patient profile is a summary of events experienced by patients during the conduct of a clinical trial. It gives a clear understanding of patient encounters and offers a more focused on methodology by recognizing abnormalities in the data. To ensure patient safety and monitor the significant clinical event, study clinical scientists have the responsibility to review subject patient profiles periodically during the conduct of the trial. However, the existing patient profiles generated in the data management system display s subject data in an accumulative way, which means clinical scientists have to review many data records again even they have reviewed them multiple times in previous rounds. It becomes a more and more time -consuming and attention-demanding task for clinical scientists to find the information of interest, such as the new emerging data, data altered/updated since last round of review or records that have been removed in the data cleaning process | clinical trial, adverse events | source | 6 |
| 158 | Patterns of risk factors and drug treatments among Hypertension patients | Youngjin Park | The most common comorbidities of hypertension are heart disease, kidney disease, diabetes, and hyperlipidemia. It is possible for two of these diseases to occur simultaneously. Monitoring the characteristics of hypertension patients is one way to prevent the co-occurrence of two diseases. This paper compares the comorbidities of hypertension patients in two age groups. One group is under 65 years old and the other group is 65 years old and over. The paper shows that the younger group has diverse risk factors and comorbidities, while the older group has primarily heart- and kidney-related risk factors. ACE inhibitors and beta blockers are commonly used to treat hypertension. This paper shows which order of taking these medications lowers cost and reduces either length of stay or heart disease events | insurance claims, odds ratio | source | 14 |
| 159 | Performing Analytics on Free-Text Data Fields: A Programer-s Wurst Nitemare | Michael S Rimler, Matt Pitlyk |
Case report forms (CRFs) often contain free-text fields for collecting patient information when standard responses do not apply, e.g. 'Other specify' or 'Reason for ...'. Furthermore, the analysis plan may require analyses to be performed on this non-standardized information. Free-text fields are renowned for their difficulty to be programmatically incorporated into analyses due to human nature injecting spelling/grammatical errors or differences in languages across global sites. These fields also tend to be very difficult to monitor. Requesting sites to 'modify' content to support cleaner analyses is time consuming, even if the site is responsive and agreeable. We compare methodologies for classifying a record into a binary response based on information collected via a free-text field. For example, identifying all collected concomitant medication records taken for Chronic Obstructive Pulmonary Disease (COPD) when 'Reason for Therapy' is collected via free-text | logistic regression, proc hpforest, regression analysis, logistic distribution, type i error, unsupervised learning, training set, adam dataset, type ii error, machine learning, test set, data cleaning, if-then-else, auc, proc freq, concomitant medications, proc hpsplit, supervised learning, clinical trial, clinical data | source | 18 |
| 160 | Pharmacokinetic Parameters for Sparse and Intensive Sampling - Nonclinical and Clinical Studies | Shallabh Mehta | Sparse sampling is very common in toxicokinetic studies, where a single blood sample can be collected on a given study day from each animal in a treatment group. Similar case can be seen in a clinical study where not more than one sample can be taken from a human on each study or study day mainly because of the nature of the study for example pediatric or because of difficult procedures involved in obtaining the samples. The purpose of this paper is to present how the process of transforming pharmacokinetic (PK) parameters to Clinical Data Interchange Standards Consortium (CDISC) Standard for Exchange of Nonclinical Data (SEND) Pharmacokinetics Parameters (PP), can be used for CDISC Study Data Tabulation Model Version 1.5 (SDTM) PP, specifically how the pooled PK parameters are formatted to SEND and SDTM PP domains using SEND Implementation Guide 3.1 and SDTM Implementation Guide 3.2 | sample, pk parameter, clinical trial, study day, cdisc | source | 5 |
| 161 | Practical Guidance for ADaM Dataset Specifications and Define.xml | Jack Shostak | The goal of this paper is to provide practical guidance for how to specify ADaM analysis datasets within the confines of Define-XML nomenclature. This paper's guidance is presented in a tool and software agnostic manner. The audience of this paper is for individuals that specify and produce ADaM datasets and the associated define.xml file and who have little to moderate prior experience in doing so. A number of practical issues are explored. Source/derivation/comment text rendering is examined. Then contrasting define file specifications versus programmer ETL specifications is addressed. ADaM source and origin type is explored, followed by a deeper dive into parameter value level metadata. How to reconcile variable and parameter value level metadata is discussed. Further issues around comments, controlled terminology, dates, lengths, object order, and results metadata are explored to close out the paper | adam dataset, controlled terminology, format statement, character variable, adamig, bds, proc transpose, where statement, adverse events, variable, sample, proc step, cdisc | source | 14 |
| 162 | Practical Guide for Creating ADaM Datasets in Cross-over Studies | Neha Sakhawalkar, Kamleshkumar Patel |
Analysis datasets are categorized into Subject Level Analysis Data (ADSL), Basic Data Structure (BDS), Occurrence Data Structure (OCCDS) and Other ADaM data structures. The first three are most common and are used to analyze data in most of the studies. Pa rallel studies are very common and hence implementation of the ADaM data structures for those studies is relatively straightforward for experienced programmers and biostatisticians. Cross-over study design on the other hand is less commonly utilized and is more complex as compared to parallel study design. Hence, creating ADaM datasets for cross-over study design can be quite challenging. This paper aims at focusing on ADaM datasets for cross-over studies with details regarding variables, difference between derivations of these variables across the various data structures (ADSL, BDS and OCCDS) as well as an example of each data structure | crossover study, treatment period, bds, occds, adam dataset, cdisc | source | 10 |
| 163 | Practical Perspective in Sample Size Determination | Bill Coar | Experiments advance science. They are designed to answer specific questions. A well designed experiment accompanied by appropriate statistical methodology should yield scientifically sound results to answer specific questions with a reasonable amount of certainty. To achieve this, the concepts of type 1 error, power, and sample size are introduced into the experimental design. While these abstract concepts are based solely on assumptions, they are critical to the integrity of study results. SAS provides numerous procedures to assist with these parts of experimental design. The designs in clinical research range in complexity, as do the SAS procedures that support them. This presentation will approach sample size determination from a practical perspective. Even if a set of assumptions are reasonable, they may not result in a feasible sample size. We use a controlled clinical study to demonstrate that sample size and study design can evolve due to practical considerations | sample, sample size, conditional power, proc power, proc step, adaptive design, null hypothesis, clinical trial, effect size, interim analysis, alternative hypothesis, final analysis, treatment effect | source | 16 |
| 164 | Practices in CDISC End-to-End Streamlined Data Processing | Chengxin Li | From a programming perspective, principles and practices in end-to-end streamlined data processing are described under the CDISC umbrella. Besides compliance, there are several common practices across the clinical data processing lifecycle : traceability, controlled terminology, end-in-mind philosophy, structured design, and reusable solution s. The components of end -to-end streamlined data processing are also introduced: data collection, SDTM transformation, ADaM development, and TLF generation. The ISS/ISE programming model, MDR, and production harmonized with submission are depicted as well. To illustrate concepts, two exampl es are discussed, one on a specific data element (AE start date) , and the other on efficacy analyses in pain therapeutic areas. The end-to-end streamlined data processing with CDISC is the optimized programming model to achieve high-quality and efficient deliverables | data wrangling, adam dataset, adverse events, controlled terminology, imputation, efficacy summary, raw data, sdtm domain, data cleaning, cdisc | source | 14 |
| 165 | PROC MIXED: Calculate Correlation Coefficients in the Presence of Repeated Measurements | Qinlei Huang, Radha Railkar |
In drug and medical device development, it is often needed to evaluate the correlation between two technologies, platforms, assays, or devices, in a setting of a clinical trial with repeated measurements. The solution to this problem has been studied by many authors. Bland and Altman (1995) considered the problem by comparting the correlation into two components: between-subject and within-subject correlations. Lam, Webb and O'Donnell (1999) approached this problem by using maximum likelihood estimation in the case where the replicate measurements are linked over time. Roy (2006, 2015) solved the problem by considering different correlation structures on the repeated measures | covariance, correlation, repeated measures, covariance structure, proc mixed, proc step, response variable, sas output, fixed effects, dataset, within-subject correlation, likelihood function | source | 16 |
| 166 | PROC SORT (then and) NOW | Derek P. Morgan | The SORT procedure has been an integral part of SAS since its creation. The sort-in-place paradigm made the most of the limited resources at the time, and almost every SAS program had at least one PROC SORT in it. The biggest options at the time were to use something other than the IBM procedure SYNCSORT as the sorting algorithm, or whether you were sorting ASCII data versus EBCDIC data. These days, PROC SORT has fallen out of favor; after all, PROC SQL enables merging without using PROC SORT first, while the performance advantages of HASH sorting cannot be overstated. This leads to the question: Is the SORT procedure still relevant to any other than the SAS novice or the terminally stubborn who refuse to HASH? The answer is a surprisingly clear "yes". PROC SORT has been enhanced to accommodate twenty-first century needs, and this paper discusses those enhancements |
proc sort, sample, proc step, proc sql, sas programmer, order by, options statement | source | 10 |
| 167 | Proc Sort Revisited | Alex Chaplin | PROC SORT can do more than sort your data. Revisit PROC SORT to see how you can select records and fields, rename and format fields, compress the output to save space, reuse space in your input dataset, remove and save off duplicate records and keys. Understand the difference between using the nodupkey and noduprecs options and how they affect your results at the aggregate and detail level. Software is base SAS. Example code I've written to accompany the presentation can run in SAS University Edition or SAS On Demand for Academics because it uses SASHELP datasets as inputs. Intended audience is beginner to intermediate level SAS programmers |
proc sort, proc step, where statement, options statement, proc sql | source | 13 |
| 168 | PROC SQL to the Rescue: When a Data Step Just Won-t Do Anymore | John R. O'Leary | This paper is written for beginning and intermediate Base SAS users who are more comfortable using the traditional DATA STEP but would benefit from efficiencies gained by PROC SQL, particularly when it is used explicitly. When processing datasets with 100 million records or more, there reaches a point when a programmer is forced to try new tools if they want to get home in time for supper. This paper provides some PROC SQL examples and other time-saving programming tips with emphasis on the SQL Procedure Pass-Through Facility. Although PROC SQL can be run implicitly within the SAS Application Server, it also can interface directly with various relational database management systems (RDBMS) such as ORACLE, DB2, MySQL and Microsoft SQL Server so that the SQL code runs directly on the RDBMS, thereby reducing the amount of data that needs to be transferred back to SAS |
proc sql, sql server, proc step, sql pass-through, data step, libname statement, data standard, where statement | source | 11 |
| 169 | Process optimization for efficient and smooth e-data submissions to both FDA and PMDA | Eri Sakai, Malla Reddy Boda, Akari Kamitani, Yoshitake Kitanishi |
The data submission of NDA to FDA has already been mandatory, application to PMDA will be also mandatory after April 2020. We need to consider and to construct the process for e- data submission to FDA/PMDA simultaneously with a single data package. SHIONOGI has headquarters in Japan, recently, we established group of companies in U S and Europe to promote drug development globally. As a result, we will be doing e-data submission to not only either FDA or PMDA but also both authorities simultaneously. Currently, we are preparing SDTM, ADaM datasets and the other related submission deliverables (e.g., define.xml, SDRG and ADRG) according to CDISC standards in all our clinical trials. We established global programming process to ensure compliance to CDISC standards and regulatory submissions. As we all know that, some rules for e-data submission are different between FDA and PMDA. Therefore, we prepare separate data package for the submission to FDA and PMDA | data cleaning, adam dataset, data validation rules, clinical data, clinical research, sdtm domain, cdisc | source | 13 |
| 170 | Programmatically mapping source variables to output SDTM variables based upon entries in a standard specifications Excel file | Frederick A. Cieri, Rama Arja, Zev Kahn, Ramesh Karuppusamy |
Based upon programmatically reading the entries from standard worksheets of a SDTM (Study Data Tabulation Model) specifications EXCEL file workbook, this paper details a macro application to map source variables to output SDTM variables. After learning how to properly fill out the EXCEL file, the macro should reduce errors and programming time by directly implementing the mappings of the source variables to the output variables. To create the output variables, the macro first reads the EXCEL file worksheets to create the metadata of the variable mappings. With the variable metadata, the macro reads the raw source data files, maps raw source variables to output variables by variable rename or format transformation, and creates an output data set of merged and appended raw source data files |
excel, macro loop, macro definition, sample, proc freq, cdisc | source | 16 |
| 171 | Progression-Free Survival (PFS) Analysis in Solid Tumor Clinical Studies | Na Li | Progression-free survival (PFS) is commonly used as a primary endpoint in Phase III of solid tumor oncology clinical studies. PFS is defined as the time from randomization or start of study treatment until objective tumor progression or death depending on study protocol. This paper describes the PFS concept and its analysis methods following ADaM standard to generate ADDATES and ADTTE analysis data sets. This paper also discusses some of the challenges encountered to define progression disease (PD) and censoring events. In addition, this paper explains some statistical methods that are commonly used to estimate the distribution of duration of PFS. Such methods include PROC LIFETEST procedure to provide Kaplan-Meier estimates and PROC PHREG to provide Hazard Ratio estimate |
hazard ratio, raw data, proc step, randomization, sample, proc phreg, cdisc | source | 14 |
| 172 | Project Management Fundamentals for Programmers and Statisticians | Jennifer Sniadecki | In the clinical trials environment, nearly everyone from individual contributors to team leaders and managers have the need to utilize project management skills. Because most programmers and statisticians lack formal training in the project management field, it can be challenging to apply and effectively integrate project management principles as part of their day-to-day job duties. This paper will extract the fundamental concepts from A Guide to the Project Management Body of Knowledge (PMBOK Guide) that are most relevant to programmers and statisticians and provide real world examples where applicable | likelihood function, standard deviation, adam dataset, clinical trial, data cleaning, cdisc | source | 19 |
| 173 | Put on the SAS Sorting Hat and Discover Which Sort is Best for You! | Louise S. Hadden, Charu Shankar |
Sorting in SAS is an expensive process in terms of both time and resources consumed. In this session, prepare to explore some of the common and lesser known sorts that SAS provides. Become like the sorting hat in Harry Potter! Instead of waiting with baited breath for your team (or data) to be sorted, get the inside scoop and learn about the dynamic processes that go on behind the scenes during sorting that will enable you to pick the very best sort for your circumstances. Learn about some fantastical, mag ical SAS sorting teams: bubble sort, quick, threaded and serpentine. Behold the effervescent bubble sort! In a hurry? Take a look at the quick sort. Looking for superior efficiency? Consider the threaded sort. See how the hissing serpentine sort in SAS, like the slithering serpent Nagini sliding surreptitiously through walls, can come in handy! Which sort will you choose - or which sort will choose you? | proc step, proc sort, proc surveyselect, proc sql, sample, order by | source | 8 |
| 174 | Python-izing the SAS Programmer | Mike Molter | More and more, Biostats departments are contemplating and asking questions about converting certain clinical data and/or metadata processing tasks to Python. For many who have spent a career writing SAS code, the learning curve for this vast new frontier may appear to be a daunting task. In this paper we'll begin a gradual transition into Python data processing by looking at the Python DataFrame, and seeing how simple SAS DATA STEP tasks such as merging, sorting, and others work in the Python environment |
data step, proc step, if-then-else, sas programmer, excel, macro definition, macro facility, set statement, dataset, data wrangling, sas function, by group processing, proc sort, class variable, macro variable, missing value, do loop, %macro parameters | source | 19 |
| 175 | Quick Tips and Tricks: Perl Regular Expressions in SAS | Pratap Kunwar, Jinson J. Erinjeri |
Programming with text strings or patterns in SAS can be complicated without the knowledge of Perl regular expressions. Just knowing the basics of regular expressions (PRX functions) will sharpen anyone's programming skills. Having attended a few SAS conferences lately, we have noticed th at there are few presentations on this topic and many programmers tend to avoid learning and applying the regular expressions. Also, many of them are not aware of the capabilities of these functions in SAS. In this presentation, we present quick tips on these expressions with various applications which will enable anyone learn this topic with ease | proc sql, character variable | source | 12 |
| 176 | Raising the Bar: CDASH implementation in a biometrics CRO | Julie Barenholtz | Since its first publication in 2008, CDASH has dramatically improved reliability in clinical trials data. 67% of current CDASH can map directly to SDTM, providing increased traceability between data collection and data analysis. One advantage of having data management and statistical programming under the same roof is the creation of efficient processes that lead to timelier submissions and faster patient access to novel treatments. There are many reasons to implement a CDASH library: having a standard set of CDASH compliant CRFs can greatly reduce the time for CRF design and time to go-live; the CDASH CRF questions and their intentions are comprehensive and widely understood among the industry ; and the variable naming is designed in a way that is understood by an SDTM statistical programmer , allowing for a more efficient annotation of the aCRF.pdf and accurate placement of data into the right SDTM domain | statistical programmer, sdtm domain, clinical programming, adverse events, concomitant medications, cdisc | source | 4 |
| 177 | Raw data sets tracker: Time and project management based on the volume of available clinical data using SAS software | Girish Kankipati | Time management and availability of clinical data play an important role in the successful execution of a project. In order to plan programming activities and resources, it is very important to understand the availability of clinical data. The volume of raw data depends on a few aspects including the enrollment speed and the type of clinical trial (Phase I, II, or III). Sometimes, enrollment rates can be slow and can cause data unavailability issues, hindering the programming activities, as programming could be challenging with limited data. To address this issue, it is important to establish a robust procedure to track raw data for the successful completion of project within a given timeline. This paper will discuss how to track raw data availability by creating a raw data set tracker using a SAS program. This dynamic SAS program will be demonstrated to create this tracker | raw data, clinical data, phase i, bar chart, interim analysis, proc step, clinical trial | source | 10 |
| 178 | Real-world data as real-world evidence: Establishing the meaning of data as a prerequisite to determining secondary-use value | Jennifer R. Popovic | The U.S. Food and Drug Administration (FDA) defines real world data (RWD) as data about patient health status that are routinely generated and collected through a variety of sources, such as through provision of clinical care. Real world evidence (RWE) is defined as the clinical evidence gathered through analysis of RWD (U.S FDA 2018). Data cannot become evidence until their meaning and value have been established. This is especially true when making secondary-use of data gathered for other primary purposes, as is often the case for use of RWD. 'Meaning' and 'value' are distinct constructs and should be evaluated as such. 'Meaning' is objective, factual and agnostic to data's use. 'Value' is subjective and situational, pegged to data's intended use | insurance claims, health insurance | source | 7 |
| 179 | Reducing the Space Requirements of SAS Data Sets without Sacrificing any Variables or Observations | Stephen B. Sloan | The efficient use of space can be very important when working with large SAS data sets, many of which have millions of observations and hundreds of variables. We are often constrained to fit the data sets into a fixed amount of available space. Many SAS data sets are created by importing EXCEL or Oracle data sets or delimited text files into SAS and the default length of the variables in the SAS data sets can be much larger than necessary. When the data sets don't fit into the available space, we sometimes need to make choices about which variables and observations to keep, which files to zip, and which data sets to delete and recreate later. There are things that we can do to make the SAS data sets more compact and thus use our space more efficiently. These things can be done in a way that allows us to keep all the desired data sets without sacrificing any variables or observations. SAS has compression algorithms that can be used to shrink the space of the entire data set |
character variable, numeric variable, dataset, proc means, proc step, macro loop, %if-%then-%else | source | 7 |
| 180 | Regulation Techniques for Multicollinearity: Lasso, Ridge, and Elastic Nets | Deanna Naomi Schreiber-Gregory, Karlen Bader |
Multicollinearity can be briefly described as the phenomenon in which two or more identified predictor variables are linearly related, or codependent. The presence of this phenomenon can have a negative impact on an analysis as a whole and can severely limit the conclusions of a research study. In this paper, we will briefly review how to detect multicollinearity, and once it is detected, which regularization techniques would be the most appropriate to combat it. The nuances and assumptions of R1 (Lasso), R2 (Ridge Regression), and Elastic Nets will be covered in order to provide adequate background for appropriate analytic implementation. This paper is intended for any level of SAS user. This paper is also written to an audience with a background in theoretical and applied statistics, though the information within will be presented in such a way that any level of statistics/mathematical knowledge will be able to understand the content | regression analysis, ridge regression, elastic net, lasso regression, proc step, predictor variable, proc reg, response variable, feature selection, estimate, proc glmselect, correlation, bootstrap, proc calis, sample, quadratic programming, proc univariate, logistic distribution, condition index, proc corr | source | 23 |
| 181 | Reimagining Statistical Reports with R Shiny | Sudharsan Dhanavel, Harinarayanan Gopichandran |
Generating TLF outputs is one of the primary task for most of the statistical programmers across organizations. Generally, SAS programs and macros are involved to produce the outputs. Although there are multiple software packages or programming languages that could achieve this task. The focus on this paper is a solution based on R. This paper provides a brief background and overview of R Shiny package and its ability to solve the output generation and reviewing it for a statistical programmer. A statistical programmer quite often receives multiple requests that results in several programming hours spent in altering the report parameters. Would it be cool if you point your output in dashboard and say, "Here you go it's an interactive Shiny app, you can alter the parameter or filter or subset as you wish to see and report right away appears in screen" | statistical programmer, numeric variable | source | 6 |
| 182 | Running Parts of a Program while Preserving the Entire Program | Stephen B. Sloan | proc step, macro variable | source | 3 | |
| 183 | Sample Size Determination with SAS Studio | Bill Coar | Experiments are designed to answer specific questions. In order for the results to have a reasonable amount of certainty and statistical validity, a sufficient number of observations are required. To achieve this, the concepts of type 1 error, power, and sample size are introduced in to the experimental design. Real world constraints such as budget and feasibility are equally as important. Thus, statisticians often re-evaluate sample size under varying sets of assumptions, sometimes on the fly. SAS Studio provides a variety of tasks associated with determining sample sizes using a point-and-click approach to enter assumptions, yet it also provides the ability to save the underlying SAS code so that it can be either refined or used at a later time. The purpose of this Hands-on-Workshop is to introduce some of the features of SAS Studio for sample size determination. A number of examples will be introduced, including tests associated with proportions, means, and survival analysis | sample size, sample, sas studio, survival analysis, clinical trial, alternative hypothesis, hazard function, null hypothesis, hazard ratio, binomial distribution | source | 13 |
| 184 | SAS V9.4 MNAR statement for multiple imputations for missing not at random in longitudinal clinical trials | Lingling Li | Missing data is a common problem in longitudinal clinical trials. The primary analysis used in clinical trials usually rely on the untestable assumption of missing at random (MAR) and the sensitivity analysis under plausible assumption of missing not at random (MNAR) is needed to examine the robustness of the statistical inference obtained from the primary analysis. Multiple imputations with pattern-mixture models (PMMs) is widely used for implementing sensitivity analysis under the assumption of MNAR. Two MNAR assumptions, control-based pattern imputation and delta-adjusted pattern imputation are regarded as clinically plausible, transparent, and easy to implement. SAS Version 9.4 PROC MI provides a MNAR statement, with two options MODEL and ADJUST, that allows implementation of the two assumptions conveniently. MNAR statement is used along with MONOTONE statement to handle monotone missing data and FCS statement to handle arbitrary missing data |
imputation, missing value, clinical trial, proc step, clinical data, sensitivity analysis, proc mi, proc mixed, probability distribution, proc calis, missing not at random, posterior probability, inferential statistics, nominal variable, predictor variable, multiple imputation, change from baseline, proc mianalyze, sample, proc sort, regression analysis, response variable | source | 11 |
| 185 | Saving Taxes | Margaret Hung | premium, savings account, excel | source | 12 | |
| 186 | Schoveing Series 4: Inspirational Leadership: Grow Yourself into a Class Act and an Unforgettable Leader! | Priscilla Gathoni | Inspirational leadership is the human side of leadership with effects that draw people to appreciate passion, tenacity, and enthusiasm around them. Would you like to discover this proven approach to leadership characterized by traits such as business ethics, values-leadership, corporate social responsibility, and sustainability? What are the distinguishing qualities or characteristics that typically belong to inspirational leaders? What kind of leader is needed in geographically dispersed teams? Why are inspirational leaders more likely to be successful today in all industries? Is emotional intelligence a catalyst for inspirational leadership? What are the secrets to developing and applying unique ideas and new leadership methods to achieve higher performance and excellence within your company? As a leader, are you able to translate broad strategies into specific objectives and action plans? What are the keys to assuming leadership, arousing enthusiasm with people you work with, develo | statistical programmer, adverse events | source | 7 |
| 187 | SDSP (Study Data Standardization Plan) Case Studies and Considerations | Kiran Kumar Kundarapu | This poster is intended to cover SDSP case studies focusing on different stages over a drug development lifecycle. Drug development lifecycles and stage gates considered include following 3 phases: New Program Phase (pre-IND, IND) Retrospective/Ongoing Program Phase (End of phase II/pre-NDA/pre-BLA) Already approved /Supplement Program Phase (sNDA/sBLA). In addition, scenarios where single versus multiple SDSPs for a program will be covered based on IND, population and indication. Additional SDSP topics and considerations will cover: Versioning the SDSP covering updates and checks Consistency checks between SDSP and other submission documents Nuances in CDER and CBER requirements CBER Appendix key recommendations for completion SDSP Authoring & Reviewing Responsibility Chart The scope of this poster is to share the data standards information scenarios of SDSP Sections 4 & 5 | data standard, clinical research, sample | source | 8 |
| 188 | SHIONOGI Global SAS System Renewal Project - How to Improve Statistical Programming Platform | Yura Suzuki, Yoshimi Ishida, Malla Reddy Boda, Yoshitake Kitanishi |
SHIONOGI Global SAS (G-SAS) System is statistical programming platform to support Shionogi & Co., Ltd. (Japan) and Shionogi Inc. (US) to develop and validate statistical programming deliverables such as SDTM, ADaM and TLFs. G-SAS is the core system to collaborate programming activities between Japan and US SHIONOGI locations, and it operates around the clock. As increasing Clinical Database, Datasets size, Multiple languages and various statistical analyses are causing insufficient memory and hard disk drive. And also to upgrade current SAS 9.2 version to SAS 9.4 version to a new physical server with higher specifications with 64-bit architecture, to handle both SAS versions, and larger datasets, SHIONOGI G-SAS Server Renewal Project was established | clinical programming, macro definition, data science, data cleaning, access control, clinical trial, dataset, cdisc | source | 10 |
| 189 | Six Useful Data Tool Macros | Ting Sa | In this paper, six macros are introduced that can work as helpful tools for some common data tasks. The macro "HelpConsistency" can detect those fields that have same name but different data lengths or data types among the SAS data sets and fix the data length inconsistencies automatically. The macro "SelectVars" can select any variables in batches from a SAS data set, for e.g, the variables have similar naming patterns like the same suffix or the middle parts. The macro "ExportExcelWithFormat" can export formatted SAS data to EXCEL files without losing the formats. The macro "FindFiles" can help users find and access their folders and files very easily. The macro "SearchReplace" can search and replace any string in the SAS programs. The macro "checkCharVarType" can provide more information for your character variables, like if the variable only contains numeric values, or contains no values or contains date and DATETIME values |
create table, excel, dataset, character variable, macro variable, macro parameter, sample, variable list, proc sql, variable, macro definition, macro put, proc step, date variable, group by, in=, %if-%then-%else | source | 24 |
| 190 | SMQ SAS Dataset Macro | Mi Young Kwon, Ishan Shah |
A Standardized MedDRA Query (SMQ) is a grouping of terms from one or more SOCs that relate to a defined medical condition or area of interest. SMQs are created to standardize identification and retrieval of safety data. SMQs are part of each new MedDRA release, which is maintained by MSSO and JMO, and correspond to the terms present in that version of MedDRA. SMQs have been applied in safety and medical reviews, focused searches, signal detections, case alert, and periodic updates for clinical trials and post-marketing analyses and reports. At each new MedDRA release, we will have MedDRA and SMQ ASCII files downloaded from MedDRA dictionary. These ASCII files are with a well-defined hierarchy structure. However, SMQs with child SMQs may not directly link to Preferred Term (PT)/Lower Lever Term (LLT) codes. These files cannot be utilized directly in SAS programming for queries. It is necessary to covert the original SMQ files to SAS datasets with a user-friendly structure | standardized meddra query, clinical trial | source | 12 |
| 191 | Something Old, Something New: A little programming management can go a long way | Janet J. Li | We propose a statistical programming project tracking tool that will help manage and track programming progress, deliverables, and timelines at the study level. The tool can be used across studies to prepare aggregate reports for upper management and can be updated to fit the needs of any statistical programming organization or team | clinical programming, excel | source | 8 |
| 192 | Sponsor Considerations for Building a Reviewer's Guide to Facilitate BIMO (Bioresearch Monitoring) Review | Kiran Kumar Kundarapu, Janet Low, Majdoub Haloui |
CDER's Bioresearch Monitoring (BIMO) team has responsibility for verifying the integrity of clinical data submitted in regulatory applications and supplements and for determining compliance of trial conduct in accordance to FDA regulations and statutory requirements. In the F DA Draft Guidance for Industry, CDER's BIMO inspectors and Office of Regulatory Affairs (ORA) identifies sites of interest from all major pivotal studies within the submission. BIMO released a Technical Conformance Guide (TCG) in 2018 to facilitate site selection and review, but gave limited information for sponsors to consider when building a BIMO Reviewer's Guide. There is no available reviewer's guide template in industry. In addition, there is insufficient guidance on types of information that should be included in a reviewer's guide. This paper will share a suggested structure and considerations when authoring BIMO Reviewer's Guide | clinical trial, crf, csr | source | 5 |
| 193 | Sponsor oversight: Proof is in the documents | Shailendra Phadke | Increasing number of biotech and pharmaceutical companies are conducting clinical trials by either partially or completely outsourcing clinical trial activities to third party organizations (e.g. Contract Research Organizations (CROs) and Clinical Trial Units (CTUs)). There is a clear requirement according to ICH GCP (Good Clinical Practices) E6(R2) that the sponsor must have systems and procedures in place to ensure adequate sponsor oversight. Failure to comply in this area can result in critical findings in regulatory inspections and also prevent the organization from sponsoring any further trials until the issues are resolved. This paper will discuss in detail about the different documents that the biostatistics and statistical programming team at the sponsor can use as evidence to prove sponsor oversight. These documents will help the sponsor in conducting oversight as well as will improve the inspection readiness of the sponsor | clinical trial, statistical programmer | source | 4 |
| 194 | Statistical application in Image Processing by Integrating C and SAS | Vidhyavathi Venkataraman, Srinand Ponnathapura Nandakumar, Anupama Datta |
Analysis and manipulation of image data after being decoded into a numerical format is known as image processing. Several filters utilizing statistical principles are applied to this numerical data to improve the quality of the image. Beginning with SAS 9.2, a unique procedure is available to incorporate the flexibility of C programs into SAS. In this paper, we discuss the procedure of customizing C functions and integrating them in SAS. An illustra tion of this feature is used in addressing common issues for image analysis like image size standardization, magnification and pixel transformation/contrasting. Statistical tools like weighted regression (bi -linear interpolation), Expectation Maximization (EM) algorithm in the Region of Interest (RoI) and the Histogram Equalization techniques are detailed for further image enhancement | likelihood function, proc step, clinical trial, proc fcmp, estimate | source | 11 |
| 195 | Statistical Assurance in SAS: An Introduction and User-s Guide | Jonathan L Moscovici, Milena Kurtinecz |
A long-standing concept in the planning of clinical trials has been that of statistical power. However, traditional calculation of power implies requires specification of several unknown quantities such as measured treatment effect between groups. In this way, the power obtained is a conditional quantity that can be misleading if the underlying assumptions in the calculation are inaccurate. This leads to a miscalculation of the required sample size and other important trial determinants. Statistical assurance is a method of calculating the unconditional probability of success of a trial by assigning probability distributions to unknown parameters such as treatment effect, rather than just one "best guess" estimate. These assigned probability distributions can be based on expert opinion, available pilot data and clinical considerations | treatment effect, sample, sample size, prior probability, power analysis, probability distribution, clinical trial | source | 8 |
| 196 | Statistical Programming Roles - Time to Reevaluate Job Profiles & Career Ladders | Vijay Moolaveesala, Ajay Gupta |
Be it a CRO or Pharma industry, traditional statistical programming/analyst roles have always been centered around programmer supporting from data programming to TLF (Tables, Listings and Figures) programming. Job profiles, job postings, screenings and hiring have always been focused on evaluating individuals on their well-rounded experience on all expected tasks to be performed by programming department. The job profiles have always been created to hire a generalist who has experience in all the aspects programming to support diverse programming needs of the department. Hiring specialists into the generic roles has been perceived as bottleneck for the department. On the contrary, drastic computing environment changes, Industry wide data standard implementa tions, complex study designs, and regulatory process requirements can make the generic programmers & programming skills more of bottleneck in the future | clinical programming, sas programming, proc sql, data science | source | 4 |
| 197 | Strategy to Evaluate the Quality of Clinical Data from CROs | Charley Z. Wu | More and more pharmaceutical and biotech companies (Sponsors) are using CROs for data management. H i g h q u a l i t y o f d a t a i s t h e k e y f o r s t a t i s t i c a l a n a l y s i s , F D A / Global Regulatory submissions and product approval. As most of Clinical Data Management work is done by CROs now, a key obligation of the Sponsor lies in the evaluation of the quality of clinical data produced by CROs. This oversight is a multi-faceted challenge that requires substantial planning to ensure a positive outcome. This paper introduces a comprehensive approach that includes bo th automatic and manual data review efforts. Automated review (Auto-review) consists of 1) a data structure check, 2) a new data check, 3) an edit check, 4) a SAE reconciliation, 5) a PK reconciliation, 6) a lab data normalization and reports, 7) a critical variable check, 8) the usage of ad-hoc reports. Auto-review is primarily achieved by using targeted SAS progra mming | sample, data review, laboratory data, clinical trial, data validation rules, normalization, data governance, database lock, clinical data | source | 7 |
| 198 | Stratified COX Regression: Five-year follow-up of attrition risk among HIV positive adults, Bamako | Mamadou Dakouo, Kriss Harris, Seydou Moussa Coulibaly Coulibaly |
This study evaluated the association between longer distance to hospital and attrition (loss to follow -up and death) rate in a cohort of HIV positive adults initiating Highly active antiretroviral therapy (HAART) at the Teaching University Hospital of Point G in Bamako, Mali. We included all patients who initiated HAART between July 2004 and July 2009 at the Teaching University Hospital of Point G. Patients were considered to be in attrition if they did not show up for consultation within 90 days of their expected visit date. Th e Time Stratified COX model was used to estimate the risk of attrition among patients living further from the hospital. The analysis was adjusted for age at cohort entry, sex, and Marital status. All analyses were performed using SAS V.9.4 (SAS Institute). Of 3042 patients included, 79.5% experienced attrition with 60 months follow -up. Attrition was highest during the first six months of HAART (reference: Bamako; out of Bamako, HR 1.40[1.23;1.59]) | proportional hazards assumption, regression analysis, proc phreg, proc step, proc lifetest, hazard function | source | 12 |
| 199 | Streamlining the Metadata Management Process Using SAS Life Science Analytics Framework | Alex Ford | It was not long into my clinical programming career before I discovered that CDISC is truly an acronym for "Can Do It Somewhat Correctly". Each run of a validation report uncovered new warnings or errors followed by tracking down the source of those issues to log and report for a define.xml. The latest release of the SAS Life Science Analytics Framework (LSAF) provides a centralized framework where standards can be imported and live alongside a study and its data, managed by a graphical user interface. By associating a data standard , controlled terminology, and dictionaries with a study, team leads have the data and information necessary to produce a define.xml at the click of a button. Join us as we explore the metadata management features available in LSAF 5.1 which enable programmers of all leve ls to manage data standards correctly the first time, saving studies both time and money | metadata management, data standard, life sciences, data cleaning, cdisc | source | 6 |
| 200 | Study Data Topics at FDA/CDER | Sara Jimenez | source | 44 | ||
| 201 | Sum Fun with Flags! Sum Any Flagged Occurrence Data with FLAGSUM and Report It with FLAGRPT or PROC REPORT. | Brendan Bartley | In 2016, CDISC created the ADaM Structure for Occurrence Data Structure Implementation Guide, which describes the data structure and content for counting any events in the CDISC world. One of the most commonly flagged SDTM domains as it moves to ADaM is the Adverse Event (AE) domain, although other domains could be flagged as they move to ADaM such as CE, CM, and MH. The concepts behind flagged records in the AE/ADAE domain (AOCCFL, AOCCSFL, AOCCPFL, AOCCIFL, AOCCSIFL, and AOCCPIFL) can apply to the creation of flags for other ADaM dataset equivalent domains (CE, CM, MH, etc.). Instead of creating several reporting macros coded specifically for individual domains, let's be flexible. We describe and demonstrate two macros, FLAGSUM and FLAGRPT, which are flexible enough to handle occurrence data in various ADaM equivalent domains. These macros have the added bonus of being capable of handling non-CDISC data, which allow users to learn and adopt CDISC ways with non- CDISC data | proc step, proc report, proc freq, macro loop, flag variable, out=, proc sql, where statement, binary variable, in=, macro variable, macro conditional, output delivery system, ods output, adverse events, cdisc | source | 31 |
| 202 | Tame Your SHARE with a Python and SAS | Michael Stackhouse, Terek J. Peterson |
Staying up to date with standards is of the utmost importance, and subtle changes can cause your data to deviate from compliance. With a little bit of SAS, and a little Python, you can easily automate the extraction of CDISC standards metadata using the new SHARE API. These standards files can then be used in SDTM and ADaM development to ensure that you have access to the latest metadata available for specification writing, quality control, and custom conformance checks. Embedded CDISC metadata opens doors of possibilities when available directly to your Programming team. This poster will explore how you can implement and automate this information to make sure your team never falls behind | sas enterprise guide, quality control, cdisc | source | 6 |
| 203 | Tax Audit - Red Flags to Avoid, and What to Do with the IRS Notice | Robert Simpson | cards, credit card, calendar year, interest | source | 10 | |
| 204 | Text Analysis of QC/Issue Tracker Using Natural Language Processing (NLP) Tools | Huijuan Zhang, Todd Case |
Many Statistical Programming groups use QC and issue trackers, which typically include text that describe discrepancies or other notes documented during programming and QC, or 'data about the data'. Each text field is associated with one specific question or problem and/or manually classified into a category and a subcategory by programmers (either by free text or a drop -down box with pre -specified issues that are common, such as 'Specs', 'aCRF', 'SDTM', etc.). Our goal is to look at this text data using some Natural Langua ge Processing (NLP) tools. Using NLP tools allows us an opportunity to be more objective about finding high -level (and even granular) themes about our data by using algorithms that parse out categories, typically either by pre -specified number of categorie s or by summarizing the most common words | training set, proc freq, sample, cdisc | source | 8 |
| 205 | The Anatomy of Clinical Trials Data: A Beginner's Guide | Venky Chakravarthy | We are so caught up in our own work that we sometimes lose sight of the big picture. It is worthwhile to look at the Drug Discovery and Development process at a high level. This will serve as a refresher of the complexities at various stages and highlight the role of SAS programming in the wider context of this important human health field. An audience that wants to learn more about clinical trials will appreciate this comprehensive introduction to the role of a SAS programmer in Clinical Trials Phases I -III. The presentation will cover the role of Clinical Data Interchange Standards Consortium (CDISC) and the implications for capturing and reporting clinical trials data. Those who have just begun their careers in the Pharmaceutical industry will also benefit from attending this presentation. If you have been working in the Pharma industry for some time this will be a refresher and could lead you to discover hidden gems | sample, clinical research, sas programmer, phase iii, crf, data science, clinical trial, adam dataset, phase i, phase ii, adverse events, data standard, dataset, baseline, clinical database, laboratory data, ctcae grade, proc step, pharmaceutical industry, cdisc | source | 14 |
| 206 | The Application of SAS-Excel Handshake in DDT | Maggie Ci Jiang | To execute the SAS code embedded in EXCEL is always challenging due to the fact that the handshake takes place between the two different SAS and EXCEL software utilities. In clinical trial, when we do CDSIC data conversion, for example, instead of writing separate SAS programs to convert the Define Definition Table (DDT) to SAS datasets, we would like to have the SAS code written in EXCEL so we can compare side by side the SAS code mapping to the rules and definitions recorded in EXCEL file which serves as the DDT. This approach is particularly convenient for users in the clinical programming development as it allows the code mapping to be more visible; and to provide an easier way for the reviewers to trace the consistency and accuracy for the conversion between the rules and the real -time SAS code. This paper is to present the challenge of this SAS-EXCEL handshake with a complete real-world CDISC SDTM data conversion example |
excel, data transformation, sdtm domain, clinical trial, cdisc | source | 7 |
| 207 | The Art of Defensive Programming: Coping with Unseen Data | Philip R. Holland | This paper discusses how you cope with the following data scenario: the input data set is defined in so far as the variable names and lengths are fixed, but the content of each variable is in some way uncertain. How do you write a SAS program that can cope appropriately with data uncertainty? "Code doesn't care who wrote it, but it does matter how well it was written!" -- Gavin Winpenny (2017) | txt, missing value, numeric variable, macro variable, select, unique key, proc step, proc sql | source | 8 |
| 208 | The Evolution of FDA Regulatory Submissions in the PDUFA Era | Benjamin Vali | source | 65 | ||
| 209 | The human side of programming: Empathetic leaders build better teams. | Bhargav Koduru, Balavenkata Pitchuka |
People management is an important aspect of project management. Motivation and passion of team members play a key role in the success of a project. An empathetic leader can understand the emotional needs of the team better, to keep members motivated and productive. Employees who are emotionally satisfied will work harder and are more likely to stay put. In turn, companies will benefit from the members with higher productivity and lower turnover. Besides productivity, compassion also drives innovation. If employees are concerned about the potential consequence of their mistakes, they may feel hesitant to take risks and to derive new solutions. An open environment encourages innovation and experimentation, without fear of failure. Like any other skills, empathy can be developed by constant practice | source | 3 | |
| 210 | The Knight's Tour in 3-Dimensional Chess | John R. Gerlach, Scott M. Gerlach |
Three dimensional chess typically uses three chess boards such that a chess piece can traverse the several boards according to the rules for that piece. For example, the knight can remain on the board where it resides or move to another successive board, then move in a perpendicular fashion. In three- dimensional chess, the Knight's Tour is a sequence of moves on multiple 8x8 chess boards such that the knight visits each square only once. Thus, for three boards, there would be 192 squares visited only once. The paper, The Knight's Tour in Chess - Implementing a Heuristic Solution (Gerlach 2015), explains a SAS solution for finding such tours on a single chess board, starting from any square. This paper discusses several scenarios and SAS solutions for generating the Knight's Tour using multiple chess boards | macro definition, macro variable | source | 17 |
| 211 | The Need for Therapeutic Area User Guide Implementation | Michael Beers | As regulatory agencies, specifically PMDA at the moment, begin to do cross-product analysis of their accumulated study data, the need for standardization increases. CDISC standards cover much of the data common to clinical trials, but gaps exist. Therapeutic Area User Guides (TAUGs) are created to fill some of these gaps. However, consistent implementation of these provisional guides is lacking, and this will impact the ability of regulatory agencies to analyze data across products. This paper will discuss some possible reasons for the slow adoption of TAUGs, attempt to show why it is increasingly important that the industry implement the TAUGs, and show how the implementation of the TAUGs could be enforced | trial summary, clinical data, clinical trial, cdisc controlled terminology, data standard, reviewer guide, data cleaning, cdisc | source | 8 |
| 212 | The New Tax Law on Business | Richard Furlong | calendar year, financial services | source | 27 | |
| 213 | The Power of Data Visualization in R | Oleksandr Babych | The ability to build beautiful and meaningful graphs is a valuable skill for the data analyst. R has become a popular programming language in the field of data analysis. Among many of its advantages, it has two essential ones: it is easy to learn and it has a powerful visualization package - ggplot2. Therefore, it wouldn't require a great amount of time learning how to make high-end graphs in R. In this article we will take a look at how to build graphs via ggplot2 and we will consider its underlying concept called the Grammar of Graphics. The central idea of ggplot2 consists in constructing plots by combining different layers on top of each other and using aesthetic mappings to define the graph. The main objective of this paper is to highlight the advantages of this approach by providing various examples and to demonstrate how R can be a powerful tool in the skill set of any data analyst | nominal variable, interval variable, scatter plot, bar chart, data visualization, missing value, data science, data analyst, histogram | source | 21 |
| 214 | The Power of PROC FORMAT | Jonas V. Bilenas, Kajal Tahiliani |
The FORMAT procedure in SAS is a very powerful and productive tool, yet many beginning programmers rarely make use of it. The FORMAT procedure provides a convenient way to do a table lookup in SAS. User - generated FORMATS can be used to assign descriptive labels to data values, create new variables, and find unexpected values. PROC FORMAT can also be used to ge nerate data extracts and to merge data sets. This paper provides an introductory look at PROC FORMAT for the beginning user and provides sample code that illustrates the power of PROC FORMAT in a number of applications. Additional examples and applications of PROC FORMAT can be found in the SAS Press book titled "The Power of PROC FORMAT." |
proc format, proc step, lookup, dataset, proc freq, missing value, proc tabulate, char., put statement, time series analysis, sample, format statement, date variable, macro definition | source | 13 |
| 215 | The Shape of SAS Code | Charu Shankar | There are many languages that co-exist in the ecosystem of your SAS toolbox. This Hands- On Workshop teaches you how to use four SAS languages - Base SAS, PROC SQL, Perl language elements, and the SAS Macro Language - to help you manipulate and investigate your data. Learn to leverage these powerful languages to check your data with simple, yet elegant techniques such as Boolean logic in PROC SQL, operators such as the SOUNDS-LIKE operator in the DATA STEP and PROC STEP, functions such as the SCAN function in the DATA STEP, efficient checking of your data with Perl regular expressions, and last but not least, the amazing marriage between PROC SQL and the SAS Macro Language to hold data you just found in a variable that you can use over and over again. This workshop focuses on coding techniques for data investigation and manipulation using Base |
proc sql, proc step, macro variable, data step | source | 14 |
| 216 | This Paper focuses on CDISC Questionnaires, Ratings and Scales (QRS) supplements and types of FDA Clinical Outcome Assessments | Shrishaila Patil | CDISC develops SDTM (Study Data Tabulation Model) and ADaM (Analysis Data Model) QRS supplements that provide information on how to structure the data in a standard format for public domain and copyright-approved instruments. An instrument is a series of questions, tasks or assessments used in clinical research to provide a qualitative or quantitative assessment of a clinical concept or task-based observation. Controlled Terminology is also developed to be used with the supplements | clinical endpoint, clinical trial, clinical research, treatment effect, data science, cdisc | source | 10 |
| 217 | Thought Process and First Steps in Becoming an Independent Consultant | Venky Chakravarthy | commercial banking, clinical programming | source | 15 | |
| 218 | Throw Away the Key: Blockchain-ed Healthcare Data | Kathy Zhai | After following SAS blogs and other social media outlets that correspond to the latest pharmaceutical trends, the words "blockchain" and "bitcoin" have been prevalent. How are any of these words associated with healthcare and patient data? Like other programmers working in the pharma industry, my curiosity grew beyond just procs and data steps. Many can agree that the credibility of clinical outputs can be undermined by a plethora of common issues including incomplete, missing, or inaccurate data. After multiple layers of data manipulation, how is it certain that what is being submitted to publications is an undistorted version of the benefits and risks of these drugs? This paper will give the audience a glimpse of how blockchain technology, whose implementation is cryptographically validated by a network, has enough potential and momentum to emerge into the healthcare industry and stick around for quite some time | clinical trial, pharmaceutical industry | source | 5 |
| 219 | Time Since Last Dose: Anatomy of a SQL Query | Derek P. Morgan | Even though much of what we need to accomplish as statistical programmers can be accomplished using the DATA STEP, SQL can provide an alternative to large amounts of data manipulation. One such case is determining time since last (or first) dose. This paper will walk you through a SQL solution in place of multiple SORTs, MERGEs, TRANSPOSEs, and use of the LAG function. Not only is the code economical, but the execution is also economical in terms of execution. This may also help to increase your understanding of how the WHERE statement, and the SQL GROUP BY, and HAVING clauses work |
adverse events, proc sql, sample, where statement, group by, proc step, create table, order by | source | 12 |
| 220 | Time to COMPARE Programmer to Analyst: Examine the Differences and Decide Best Path Forward | Ginger Barlow, Carol Matthews |
There are almost as many titles for SAS programmers in the pharmaceutical industry as there are programmers: Statistical Programmer, Clinical Programmer, Scientific Data Analyst, SDTM Implementer, Study Lead Programmer, and many more. Regardless of the label, one thing all of these roles have in common is that these people are writing code to turn data into knowledge. In contrast, the same title in different organizations can have very different expectations on how much any given programmer (or analyst) is expected to contribute to the overall scientific process. Programmers and analysts actually have very different, distinct roles. We will explore what makes each unique, how to know which one you are, how to know which one you may be interviewing, and what the future could hold for each. We will conclude with a discussion on strategies for developing programming teams to meet business quality and financial objectives | pharmaceutical industry, adverse events, cdisc | source | 8 |
| 221 | Timing is Everything: Defining ADaM Period, Subperiod and Phase | Nancy Brucken | The CDISC Analysis Data Model Implementation Guide (ADaMIG) provides several timing variables for modeling clinical trial designs in analysis datasets. APHASE, APERIOD and ASPER can be used in conjunction with related treatment variables to meet a variety of analysis requirements, from single-period parallel studies to much more complicated situations involving multiple treatment periods and even different studies. The goal of this paper is to illustrate how some of these study designs may be handled in ADaM, and provide guidelines for selecting when to use the different timing variables that are available | treatment period, adam dataset, cdisc | source | 7 |
| 222 | Tips from Three Business Owners | Robert Howard, Joshua M. Horstman, Richann J. Watson |
source | 6 | ||
| 223 | Tool Development Methods for Implementing and Converting to New Controlled Terminology in SDTM datasets | Martha O'Brien, Keith Shusterman |
Controlled terminology (CT) for SDTM datasets allows for easier review for committee members, other programmers, consultants, consulting companies, the FDA, and many others. This may ultimately reduce the time it takes to get the drug or device to market. Ensuring a proper method of choosing and implementing a newer version of CT is not only necessary but vital to submission acceptance. Currently the FDA only requires CT versions of 2011-06-10 or later and with quarterly outputs there are many options to choose. This can make it difficult when starting a study or after a study starts a sponsor may decide that up versioning the CT is necessary. Sponsors will also need to harmonize the new CT with their own specific values that have been added to the extensible codelists prior to implementing new versions. Having a partially automated process to convert to a newer CT eases not only the time constraint but also reduces the possibility of human error | cdisc controlled terminology, excel, adverse events, sdtm domain, cdisc | source | 10 |
| 224 | Unleash the Power of Less Well Known but Useful SAS DATA Step Functions | Timothy J. Harrington | SAS Version 9.4 has over 400 documented SAS functions and procedures. Some SAS users may have grown accustomed to using only a few of the more widely and well-known of these, and have had to invest significant time and extra effort manipulating code to achieve a desired result. Thi s paper describes a selection of these SAS functions and how they can be used to obtain specific results with minimal coding. Solutions to a variety of programming challenges are demonstrated and the advantages of these techniques are discussed and compared to more traditional means. The functions included here are available in SAS v9.4, some may not be available in earlier versions | missing value, sample, index, sas function, character functions, sas programmer, proc sql, %if-%then-%else | source | 22 |
| 225 | Updates on validation of ADaM data | Sergiy Sirichenko | Analysis data is critical for regulatory review process. It helps reviewers understand the details of performed analysis and reproduce results reported by sponsors. Clinical study analysis data is required to be submitted in CDISC ADaM format to both FDA and PMDA. Therefore, validation of analysis data for compliance with CDISC ADaM standard and additional business rules from regulatory agencies is an important step in preparation of study data for regulatory submissions. In this presentation we will provide an overview of ADaM validation implemented by Pinnacle 21 and used by both FDA and PMDA. It will cover changes related to the new ADaM IG 1.1 standard and the updated version of validation rules from CDISC team. The presentation will also detail additional regulatory business rules, including data and define.xml consistency, validation of ADAM OTHER datasets, SDTM/ADaM traceability, and integrated data | data cleaning, data validation rules, regulatory submission, data lineage, label statement, adam dataset, adverse events, cdisc | source | 9 |
| 226 | Use of SAS Merge in Adverse Events Reporting for DSUR | Andrew Wang | In reporting adverse events for DSUR, stat istical programmers often need to separate out AEs reported already in previous years from AEs present in the current database. The task of identifying the adverse events already reported could present a challenge to the programmer since there is no easy way to ID an adverse event using the variables in the datasets. All types of data issues could be present in the data which may cause the values of many variables to change over time in the data query and cleaning processes. For example, an AE could get deleted from the database or be split into multiple AEs . For an ongoing study, data might naturally change with time as well. For instance, un- coded AEs could get coded, ongoing AEs as of now will likely have an end date later. Many scenarios could happen. This paper will detail the above challenges and talk about possible solutions | adverse events, statistical programmer, sas log, data step | source | 8 |
| 227 | Useful SAS techniques in Efficacy Analysis for Oncology studies | Joy Zeng | Oncology refers to the research on prevention, diagnosis and treatment of cancer. Oncology studies are, in general, more complex than studies in other therapeutic fields. This paper summarizes the primary sources of complexity, including endpoints, data collection, AE reporting, tumor assessment under RECIST, oncology-specific domains, and special statistical analysis for efficacy data. This paper also discusses multiple oncology-related statistical methods (e.g. Cox regression, Kaplan-Meier, log-rank tests) and graphical data representations (e.g. waterfall plots, bar charts, mean standard error plots , spaghetti plots, and forest plots). Finally, the relevant SAS code is given for all of these methods and representations, with the goal of providing statistical programmers the necessary knowledge and tools for creating and validating tables and figures from oncology studies | hazard ratio, proc calis, objective response rate, forest plot, spaghetti plot, statistical programmer, confidence interval, proc phreg, data science, waterfall chart, best response, regression analysis, bar chart, treatment arm, survival analysis, overall survival, time to progression, proc step, adverse events, adam dataset, efficacy summary, log-rank test, progression-free survival, rank test, randomization, sample, cdisc | source | 21 |
| 228 | User-Defined Multithreading with the SAS DS2 Procedure: Performance Testing DS2 Against Functionally Equivalent DATA Steps | Troy Martin Hughes | The DATA STEP 2 (DS2) procedure affords the first opportunity for developers to build custom, multithreaded processes in Base SAS. Multithreaded processing debuted in SAS 9, when built -in procedures such as SORT, SQL, and MEANS were threaded to reduce runtime. Despite this advancement, and in contrast with languages such as Ja va and Python, SAS 9 still did not provide developers the ability to create custom, multithreaded processes. This limitation was overcome in SAS 9.4 with the introduction of the DS2 procedure-a threaded, object -oriented ve rsion of the DATA STEP. However, b ecause DS2 relies on methods and packages (neither of which have been previously available in Base SAS), both DS2 instruction and literature has predominantly fixated on these object -oriented programming (OOP) aspects of t he language rather than DS2 multit hreading |
cpu time, if-then-else, proc freq, automatic variable | source | 25 |
| 229 | Ushering SAS Emergency Medicine into the 21st Century: Toward Exception Handling Objectives, Actions, Outcomes, and Comms | Troy Martin Hughes | Emergency medicine comprises a continuum of care that often commences with first aid, basic life support (BLS), or advanced life support (ALS). First responders, including firefighters, emergency medical technicians (EMTs) , and paramedics, are so called because they are the first to triage the s ick, injured, and ailing -rapidly assessing the situation, providing curative and palliative care, and transporting patients to medical facilities. E mergency medical services (EMS) treatment protocols and standard operating procedures (SOPs) ensure that, despite the singular nature of every patient as well as potential complications, trained personnel have an array of tools and techniques to provide varying degrees of care in a standardized, repeatable, and responsible manner. Just as EMS providers must assess patients to prescribe an effective course of action, software, too, should identify and assess process deviation or failure, and similarly prescribe its commensurate course of action | exception handling, sysrc, data validation rules, macro variable, proc sort, cpu time, automatic macro variable, if-then-else, return, macro put, key performance indicator, quality control, macro flow control, macro definition, missing value, sas log, abort, macro function, options statement, proc sql, goto, imputation, %if-%then-%else, data cleaning, macro facility | source | 24 |
| 230 | Using CDISC Standards with an MDR for EDC to Submission Traceability | Paul K. Slagle, Eric Larson |
CDISC Standards have long promised a way to add clarity to a submission through the use of traceability. This includes the ability of tracing the data collected in an EDC system through to the analysis provided to the regulatory authority. The challenge with doing this is that to manage all of the traceability is a documentation headache. With the use of a metadata repository, you can develop screens that can be pushed into an ODM compliant EDC system, such as Medidata Rave. From that push you can receive the raw data and transform that, using SAS, into a format compliant to SDTM standards while maintaining the connections of where the data came from in EDC. Adding Results Metadata, when entered into the metadata repository, then allows the creation of ADaM datasets which are built on SDTM data collected from the EDC system. This paper will demonstrate how an off the shelf MDR tool can be used with SAS to build these connections while maintaining compliance to CDISC standards | adam dataset, bds, cdisc | source | 4 |
| 231 | Using freelancers in the programming world - Challenges and opportunities | Vijay Moolaveesala | Ever since the industry has opened its gates to use of global resource pool, more than a decade ago, I have been wondering when would we embrace widely the concept of using freelancers in the programming area. Industry has been using freelancers in the areas of clinical, statistical and scient ific communications for some time. Even though it may not be wide spread, but the idea of using freelancers in these areas was given an ear and same can't be said about programming freelancing usage. But again, there are many challenges in using temporary work force whether it is onsite/offsite contractors or staff augmentation models and using freelancing resource options on top of these challenges can be more difficult one to navigate. At the same time, when industry is spending lot of time in recruiting, training and retaining top talent of programmers, every resourcing option should be on the table | service provider, health insurance | source | 3 |
| 232 | Using Pinnacle 21 Enterprise for define.xml Creation: Tips and Tricks from a CRO Perspective. | Franklin E. Menius | Creating a define.xml 2.0 document that is compliant and ready for regulatory review can be time consuming and fraught with hazards. Pinnacle 21 Enterprise is a great tool that can be used to speed up this process and ensure compliance with regulatory bodies, but how can a preparer make sure they are getting the most out of the product? What options are available that are not being taken advantage of? What are some common issues when using P21 Enterprise and what are the ways to work around them? This paper will detail an overview of define.xml creation with Pinnacle 21 Enterprise by highlighting tips, tricks, and work arounds | excel, where statement, data cleaning, cdisc | source | 14 |
| 233 | Using Real-World Evidence to Affect the Opioid Crisis | Sherrine Eid, Andrea Coombs |
The opioid crisis is growing daily. Overreliance on opioids for pain management has led to the worst drug crisis in American history. Of the nearly 64,000 American deaths in 2016 due to drug overdoses, nearly two-thirds (66%) involved a prescription or illicit opioid. Over 100,000 deidentified, state-level patient claims records were analyzed to assess the likelihood of an opioid complication after receiving a prescription for opioids. Descriptive statistics were reported on all variables and binary logist ic regression was used to determine the likelihood of opioid complications in patients prescribed at least one opioid adjusting for age, gender, race, and behavioral health diagnosis. Adjusted odd ratios with 95% confidence interval were reported. Our study showed that patients who have a behavioral health diagnosis are almost 55 times more likely to have an opioid complication if prescribed opioids than patients without such a diagnosis | proc hpsplit, neural network, likelihood function, logistic regression, machine learning, forest plot, logistic distribution, regression analysis, response variable | source | 10 |
| 234 | Using SAS ODS EXCEL Destination "Print Features" to Format Your Excel Worksheets for Printing as You Create Them. | William E. Benjamin Jr. | This presentation will demonstrate features of the SAS ODS EXCEL Destination that allow you to use various ODS EXCEL sub-options to prepare a worksheet to be printed, as it is written out by SAS. The sub-options that impact page level features are the following. "Choose to print in color or Black and White only"; "Center the output either horizontally or vertically"; "Print in draft quality or normally"; "Print horizontally or vertically"; "Arrange the data across the page differently"; "Select where to put the output"; "Print headers, footers, and row breaks"; "Adjust the size of headers and footers"; "Adjust the print scale to improve the look of lines in graphs" |
excel, xls, ods excel, output delivery system | source | 17 |
| 235 | Using the PRXCHANGE Function to Remove Dictionary Code Values from the Coded Text Terms | Lynn Mullins | SAS Perl regular expression (PRX) functions and CALL routines refer to a group of functions and CALL routines that use a modified version of the Perl programming language as a pattern-matching language to parse character strings. Some of the tasks that you can perform using PRX functions are: Search for a pattern of characters within a string Extract a substring from a string Search and replace text with other text Parse large amounts of text, such as Web logs or other text data You can write SAS programs that do not use regular expressions to produce the same results as you do when you use Perl regular expression functions. However, the code without the regular expression functions requires more SAS functions to handle character positions in a string and to manipulate parts of the string. Perl regular expression functions combine most, if not all, of these steps into one SAS statement. The resulting code is less prone to error, easier to maintain, and clearer to read | prxchange, raw data, concomitant medications | source | 4 |
| 236 | Validating Hyperlinks in SDTM define.xml Using Python | Brandon L. Welch, Gregory Weller |
As a one-stop location for a clinical trial's metadata, the define.xml file is a vital piece of an FDA submission. Held within this file are many hyperlinks. Some links are internally specific to the XML file, while others point to external locations. Of particular interest in SDTM submissions are the links that externally map to the study's annotated case report form (aCRF) - a PDF document. For a particular SDTM variable, a user clicks on the hyperlink and the annotated CRF opens to the variable's origin page. Depending on how these links are created in the define.xml, occasionally the page hyperlink fails to open the correct annotated CRF page. Manually testing each hyperlink is tedious and error prone. Fortunately, there are powerful Python modules for analyzing PDF and XML files. In this paper, we describe a technique using the Python programming language that checks each define.xml link against each page in the CRF PDF document | annotated crf, variable, cdisc | source | 8 |
| 237 | Value-Level Metadata Done Properly | Sandra Minjoe, Mario Widel |
Value-level metadata is nothing mysterious. It is simply a way to describe how a variable is derived when that derivation differs based on some circumstances. When done properly, value-level metadata make a define.xml more reviewer-friendly. A common use in ADaM for value-level metadata is when AVAL is derived based on PARAM, but this is not the only time to use value-level metadata. This hands-on training will include examples and exercises of value-level metadata in SDTM Findings, in ADaM BDS, and more. Additionally, it will provide guidance to help attendees decide when to use value- level metadata | format statement, adam dataset, adverse events, dataset, cdisc | source | 21 |
| 238 | Vendor's Guide to Consistent, Reliable, and Timely CDISC Deliverables | Dharmendra Tirumalasetti, Santosh Reddy Lekkala, Bhavin Busa |
When working in a CRO/FSP model, programmers have to work on multiple clinical studies across different sponsor companies and various data collection systems. In addition, even though the submission standards are common across the industry, the requirements and expectations for CDISC deliverables could differ from one sponsor to another. The differences could be based on multiple factors such as therapeutic areas, internal data standards, and study-specific needs. Also, the interpretation and handling of the data may differ between the sponsor companies. If not understood and documented earlier by the vendor, the differences could cause re-work at a later stage which adds up to delayed deliverables, rework time and extra cost. To meet the sponsor's specific needs and expectations, it is highly recommended to have and follow effective processes with-in the organization | data cleaning, raw data, sdtm domain, cdisc | source | 5 |
| 239 | Visualize Overall Survival and Progression Free Survival at the Same Time! | Kriss Harris | Usually we produce Kaplan-Meier plots to show the Overall Survival (OS) and/or the Progression-Free Survival (PFS) profile. It is rare that we see the OS and the PFS on the same graph; the endpoints are usually in two different files. This paper will show you how to produce an animated visualization that you can use to visualize the OS and PFS together which can help you to understand the treatment efficacy better | overall survival, kaplan-meier plot | source | 2 |
| 240 | What-s New in the SDTMIG v3.3 and the SDTM v1.7 | Fred Wood | The SDTMIG v3.3 and the SDTM v1.7 were released in November of 2018. These versions have been published as HTML documents, rather than the typical PDFs of the past. While the SDTMIG v3.2, which was published in 2013, contained 398 pages, the SDTMIG v3.3 would be more than 600 pages if formatted properly and printed to PDF. A number of new morphology/physiology domains have been added. Other new domains and new concepts have been added, and several domains that had undergone public review in 2014 were subsequently combined with existing domains. The Disease Milestones concept, introduced for the TAUG-Diabetes in 2014, is now included in the latest version of the SDTMIG. A new Section 9 includes Study References, with added models for Device Identifiers, Non-host Organism Identifiers, and Pharmacogenomic/Genetic Biomarker Identifiers. Three versions of the SDTM have been published since the release of v1.4 with the SDTMIG v3.2 | clinical trial, adam dataset, sdtm domain, disease response, cdisc | source | 13 |
| 241 | When biomarker drives primary endpoint: An oncology case study of SDTM design using multiple myeloma. | Girish Kankipati, Bhargav Koduru |
Oncology studies are often driven by imaging, which led to the creation of the tumor-specific TU and TR domains in the SDTM IG 3.1.2, where the capture of the scan details and results is described. These domains usually are linked to the RS Domain, which contains the overall tumor response in an oncology study. There are, however, a few oncology conditions like multiple myeloma, which are not driven by imaging but by specific biomarkers. This information would be captured in the LB Domain, in contrast to TU and TR. Biomarkers play an important role in indicating normal biological processes, pathogenic processes, or pharmacologic responses to a therapeutic intervention. In multiple myeloma, serum free light chain (SFLC), serum and urine protein electrophoresis (SPEP and UPEP), and immunofixation are the key biomarker-related tests that define the standard response criteria | sdtm domain, adam dataset, cdisc | source | 5 |
| 242 | Where Is the Information We Are Looking For? | Jeff Xia | Finding historical analysis and reporting artifacts in an efficient and effective way is an essential need for a statistical programmer. Identifying the source data, SAS programs or macros used to create a previously produced analysis, table, or figure can be challenging especially when facing a tight timeline and on old projects where institutional or historical knowledge may be limited. Statistical programmers need tools to trace back and search for these historical artifacts and effectively locate various inputs for analysis and reporting deliverables. This paper presents four innovative utilities to programmatically find the information of interest. 1) Finding a text string in all text files, including text files in subdirectories. This utility is extremely useful when there is a need to locate a certain CSR table out of hundreds tables; it is also useful in understanding which SAS program was used to generate a specific table, which macros were involved, etc | txt, sample, dataset, sas library, macro call, statistical programmer, macro variable | source | 8 |
| 243 | Why waiting longer to check log file when SAS program in Execution? Lets Find bugs Early!! | Prakash Subramanian, Thamarai Selvan |
Prompt error alerts through emails while an error is encountered, will help user to save the run time and restrict the creation of empty datasets from the subsequent codes. It also helps in monit oring the status of the SAS programs submitted without being in front of the computer. While running the program, if SAS finds any error in the current line, it will skip the current statement and will start executing the next statement and the process will be complete only after the execution of the end statement of the program. There are programs which takes more than a day to complete. In such cases, user has to open the log file in read only mode very frequently to check for errors, warnings and unexp ected notes. The user will have to terminate the execution of program manually if any such messages are identified else the user will be notified with the errors in the log file only at the end of the execution | error message, macro conditional, macro variable | source | 7 |
| 244 | Why we should learn Python | Kevin Lee | Python is one of the most popular language nowadays. Python can be used to build just about anything, and it is a great language for data analysis, scientific computing, application development, backend web development, especially machine learning and many more. Python is currently featured in 70% of introductory programming courses at US universities and the latest report from Forbes states that Python grew by more than 450 percent in 2017. We, statistical programmers, have been using SAS and because of the popularity of Python, we wonder if we should learn Python. The paper will start with the current Python implementation and the future of its implementation. The paper will also show basic concepts of Python programming, similarities with SAS programming and difference from SAS programming. The paper will also introduce the benefits of learning Python including the opportunity in Data Science and Machine Learning, career opportunities and a high salary | machine learning, data science, dataset, structured data | source | 11 |
| 245 | With a Trace: Making Procedural Output and ODS Output Objects Work For You | Louise S. Hadden | SAS procedures can convey an enormous amount of information - sometimes more information than is needed. Most SAS procedures generate ODS objects behind the scenes. SAS uses these objects with style templates that have custom buckets for certain types of output to produce the output that we see in all destinations (including the SAS listing). By tracing output objects and ODS templates using ODS TRACE (DOM) and by manipulating procedural output and ODS OUTPUT objects, we can pick and choose just the information that we want to see. We can then harness the power of SAS data management and reporting procedures to coalesce the information collected and present the information accurately and attractively |
proc step, proc contents, estimate, ods output, output, proc means, excel, sample, data step, proc surveyfreq, logistic distribution, output delivery system | source | 26 |
| 246 | Working from a Home Office Versus Working On-Site | Timothy J. Harrington | This paper is a discussion of the benefits and challenges of working remotely as opposed to working on site for an employer or a client. The primary issues addressed include communication, productivity, knowledge and skill sharing, financial considerations, health and well-being, and social and economic factors. The impacts of telecommuting are considered from the standpoint of each of the remote worker, their employer, and the environment and society as a whole | likelihood function, dropped call | source | 12 |
| 247 | Working Remotely - Home Office, Tools and Technology | Gary E. Moore | survival analysis, clinical trial, clinical programming, proc step | source | 25 |
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