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BUGSPharmaSUG 2021 Papers - Index
175 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | "Bored"-Room Buster Bingo - Create Bingo Cards Using SAS ODS Graphics | Richann J. Watson, Louise S. Hadden |
Let's admit it! We have all been on a conference call that just … well to be honest, it was just bad. Your misery could be caused by any number of reasons - or multiple reasons! The audio quality was bad, the conversation got sidetracked and focus of the meeting was no longer what it was inten ded, there could have been too much background noise, someone hasn't muted their laptop and is breathing heavily - the list goes on ad nauseum. Regardless of why the conference call is less than satisfactory, you want it to end, but professional etiquette demands that you remain on the call. We have the answer - SAS-generated Conference Call Bingo! Not only is Conference Call Bingo entertaining, but it also keeps you focused on the conversation and enables you to obtain the pertinent information the conference call may offer | proc step, sample, proc surveyselect, rand, graph template language, proc template | source | 12 |
| 2 | "Packages" in R for Clinical Data Analysis, Let's Demystify | Nagalakshmi Kudipudi | R is a high-level, interpreted and an open -source programming language-based software which is known for statistical analysis and graphical reporting. R offers a Plethora of 'Packages' for data import, wrangling, visualization and analysis. The packages are extensions to the R statistical programming language and fun damental units of reproducible R code. They include reusable R functions, the documentation that describes how to use them, sample data in a standardized collection format that can be installed by users of R, typically via a centralized software repository such as CRAN (the Comprehensive R Archive Network), GitHub. GitHub, Inc. provides access control and several collaboration features such as bug tracking, feature requests, task management, continuous integration and Wikis for every project. Its more adv anced professional and enterprise services are commercial. Free GitHub accounts are commonly used to host open-source projects | data science, dataset | source | 8 |
| 3 | -Who wrote this!?- Challenges of replacing Black Box software with more dynamic supportable processes utilizing Open-Source Languages | Franklin E. Menius, Dennis Sweitzer, Terek Peterson, Monali Khanna |
Good News - the software program that was written and put into production years ago still works. Challenge is dealing with the "Black Box" whereas input is entered, and output extracted correctly but no one truly understands the processes and transformations that take place inside the box. For validated applications, a company may be able to operate like this for a long time but what if a bug is located or the needed output format changes? These applications then must be enhanced or replaced. This paper is about the experience gathered in the effort to replace a SAS macro program utilized in data transfers with a more dynamic and supportable program in R. We will focus both on the technical challenges of converting a solution from one language to another and the inherent challenges of change management. We will share steps and lessons learned that can expedite a similar process | data cleaning, quality control | source | 4 |
| 4 | 100% Menu Driven SAS Windows Batch Job Runner and Log Analyzer | Sathish Saravanan, Kameswari Pindiprolu |
SAS is the widely used software in clinical research industries for clinical data management, analysis & reporting. Every clinical study requires numerous SAS programs to be developed for generating Datasets (D), Tables (T), Listings (L) and Graphs (G). The program rerunning is evident due to programming specification changes for DTLG and Ongoing Studies. At the same time for each rerun, it is mandatory to ensure the logs of every submitted program is free from SAS defined potential messages. Various methods have been discussed about bulk rerunning of programs and analysing the log and none of them is menu driven approach. Companies are investing so much of money to develop and maintain such a menu driven tool that performs bulk rerunning of the SAS programs along with the log an alysis | batch job, sas batch | source | 6 |
| 5 | A Diagnostic Report for SAS Submission Programs | Yang Gao, Christine S Teng |
To submit a data package to a health authority for regulatory approval, sponsors must ensure analysis programs meet regulatory compliance requirements and good business practices. Each company has their own standard operating procedure (SOP) as well as checking tools that assist in the development of regulatory-compliant SAS programs. The checking tools are used to ensure these SAS programs are ready for internal and external audits. This paper introduces a SAS utility macro with multiple features that provides detailed information for each SAS program. The key features include: (1) checking correspondence between SAS programs and associated log, lst, programs (in submission ready format) as well as analysis outputs, (2) retrieving a SAS program's last modified date (system date), program version date and last revision date in development and production area to identify inconsistency, (3) producing a summary of output counts (SAS log, lst, etc.) for each SAS program | data cleaning, ods rtf, excel, clinical trial | source | 6 |
| 6 | A Lead Programmer-s Guide to a Successful Submission | Pranav Soanker, Santosh Shivakavi |
A successful NDA (or other submissions) depends upon a robust Biostatistics/Biometrics department which usually consist of few statisticians and many statistical programmers among others. It requires great co- ordination with all the stake holders (CDM, External vendors, Medical Writing, CROs etc.,) and great understanding of core competencies of each group to achieve a high-quality submission ready package. The programmers usually have a wide and diverse educational background, with overwhelming majority without a statistical degree, and particularly with junior team members the clinical trial knowledge and experience can be limited. Thus, the role of a Lead Programmer is very critical as a liaison between programmers and statistician | raw data, outlier, proc step, adam dataset, clinical trial, sdtm domain, data cleaning, cdisc | source | 8 |
| 7 | A Macro for Point Estimate and Confidence Interval of the Survival Time Percentiles | Hengwei Liu | The PROC LIFETEST in SAS creates a table called quartiles. It contains the point estimate and confidence interval for the 25, 50 and 75 percentiles for the survival time. PROC LIFETEST doesn't have an option to calculate the point estimate and confidence interval for any other percentiles for the survival time. In this paper a macro is set up to calculate the point estimate and confidence interval for the survival time percentiles. The development is done with SAS version 9.4_M4 with SAS/STAT 14.2 in Linux |
confidence interval, proc step, macro parameter, macro definition, in=, output delivery system | source | 8 |
| 8 | A metadata driven approach to mock TFL generation | Anne Petersen, Cristina Boschini, Tobias Kröpelin |
At Novo Nordisk A/S we developed a tool to create specifications for tables, figures, and listings (mock TFL) utilizing the same tools and metadata as when the actual tables, figures, and listings (TFL) are produced. The mock TFL generator consists of a repository of table, figure or listing templates in .txt or .png format (called unique shells) as well as output metadata, including instructions on content and layout for TFL programming. The mock TFL are created by combining the unique shells with output metadata, such as titles, footnotes and programming instructions through a SAS script. The final mock TFL in .docx format are generated by the same tool used to generate the actual trial TFL. The main advantage of this tool is the single point of definition for the mock TFL and the actual trial TFL. This assures that the programmer only needs to maintain a single repository containing output and programming metadata | mock shell, clinical trial | source | 5 |
| 9 | A Process to Validate Internal Developed R Package under Regulatory Environment | Madhusudhan Ginnaram, Amin Shirazi, Simiao Ye, Yalin Zhu, Yilong Zhang |
The use of R in regulated environments has been on the rise and is playing a role in the submission process of clinical trial analysis results to regulatory agencies. Validation is an important part of R package development and is an es sential step in the regulated environment. In this paper we propose a detailed and practical validation procedure for testing R packages under regulatory environment especially for Analysis and Reporting (A&R) deliverables in clinical trials. In general, R package testing involves 3 major stages: planning, implementation and reporting. Our proposal follows the same principles that can enhance the traceability and robustness of an R package development within an organization. The proposed procedure helps an organization to develop an R package following a risk-based approach to assess R package within a validated infrastructure (https://www.pharmar.org/white-paper/) suggested by R Validation Hub | data cleaning, clinical trial, independent qc, clinical data, r package | source | 7 |
| 10 | A Programmer's Experience Researching Real-World Evidence (RWE) COVID-19 Data | Ginger Barlow | In early 2020, when the COVID-19 pandemic hit the US, researchers started gathering and analyzing data to help understand who was vulnerable to the infection, make major policy decisions to reduce the spread of the disease and start the race for an effective vaccine. At UBC, a team of researchers used the Electronic Health Record (EHR) data from Cerner Health Systems' Real-World DataTM (CRWD) to analyze records of 14,371 inpatients with a COVID-19 diagnosis. The primary objective of the study was to use real-time data from the CRWD to assess how variation in coding algorithms to classify cases of COVID-19 impacted the number and types of patients identified | data science, baseline characteristics | source | 5 |
| 11 | A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software | Stephen B. Sloan, Kirk Paul Lafler |
Data comes in all forms, shapes, sizes and complexities. Stored in files and datasets, SAS users across industries recognize that data can be, and often is, problematic and plagued with a variety of issues. Data files can be joined without problem when each file contains identifiers, or "keys", with unique values. However, many files do not have unique identifiers and need to be joined by character values, like names or E -mail addresses. These identifiers might be spelled differently, or use different abbreviation or capitalization protoco ls | proc step, proc sql, character variable, proc sort, data cleaning, sas function, missing value, numeric variable, data transformation, proc format, order by, proc contents, proc print, character functions, proc transpose | source | 28 |
| 12 | A SAS Macro to Generate Slim Version of Define for RTOR | Jeff Xia | FDA Real-Time Oncology Review (RTOR) pilot program provides a channel for agency reviewers to access study data earlier on to explore more efficient review of oncological submissions, which could lead to safe and effective treatments for patients as early as possible. One of the required components in RTOR is corresponding ADaM datasets for key efficacy and safety tables/figures for pivotal study, which might be a subset of the entire ADaM datasets (i.e., excluding datasets and variables that are not involved in key efficacy and safety analysis). A corresponding smaller (slim) version of define documents (define.xml and define.pdf) is also required. This paper introduces a SAS macro that can be used to generate a smaller (slim) version of define document using Pinnacle 21(P21) Enterprise Version. 1. This macro reads the full version of define specification in EXCEL spreadsheet format into SAS datasets. 2 |
excel, adam dataset, dataset, macro definition, macro loop, macro conditional, cdisc | source | 8 |
| 13 | A Sassy Substitute to Represent the Longitudinal Data - The Lasagna Plot | Soujanya Konda | Data interpretation becomes complex when the data contains thousands of digits, pages, and variables. Generally, such data is represented in a graphical format. Graphs can be tedious to interpret because of voluminous data, which may include various parameters and/or subjects. Trend analysis is most sought after, to maximize the benefits from a product and minimize the background research such as selection of subjects and so on. Additionally, dynamic representation and sorting of visual data will be used for exploratory data analysis. The Lasagna plot makes the job easier and represents the data in a pleasant way. This paper explains the tips and tricks of the Lasagna plot | spaghetti plot, longitudinal data, trend analysis | source | 13 |
| 14 | A Strategy to Develop Specification for R Functions in Regulated Clinical Trial Environments | Aiming Yang, Yalin Zhu, Yilong Zhang |
A well-written programming requirement specification is an essential document for a regulatory compliant environment and software development life cycle (SDLC). It is a technical document outlining the requirement and usage of a specific computer program. With the growing interest in using R for regulated clinical trial data analysis, there is a need to document specifications of R functions within an R package. The specification generation has direct implications on source code and file management. In this paper, we propose a strategy of using Roxygen to develop the specification of an R function. We will introduce concepts around R functions and Roxygen. We will also conduct demos for the specification generating process and present simple examples. We hope the proposed specification documentation process can help the pharmaceutical industry standardize the SDLC for internal R package development | confidence interval, clinical trial, macro definition, data cleaning | source | 11 |
| 15 | A TransCelerate Initiative - how can you modernize your statistical environment? | Vincent J. Amoruccio, Min Lee, Daniel Woodie |
A lack of evolution within the pharmaceutical industry's analytic capabilities has given rise to inefficiency and a failure to leverage modern statistical analysis techniques within the clinical development space. Moreover, a lack of regulatory perspective on this matter has become a barrier in implementing and leveraging newer analytical software capabilities. A TransCelerate initiative has developed a foundational framework that increases confidence in use of modernized statistical analysis and efficient technology across the industry. This has created an opportunity to provide clarity and instill confidence in the use of modern software technologies, including those that generate outputs that support regulatory submissions. This framework is based upon a methodology that establishes the principles of accuracy, traceability, and reproducibility for a modern analytical software environment to demonstrate that any output it generates is reliable | regulatory submission, data science, data cleaning, clinical research, clinical trial | source | 16 |
| 16 | A Unique Way to Identify the Errors in SDTM Annotations Using SAS | Xingxing Wu, Bala Dhungana |
The annotated Case Report Form (aCRF) is a vital file of SDTM development and FDA submission. Therefore, it is very important to maintain the correctness of SDTM annotations in the aCRF. However, a single study would usually have several thousands of SDTM annotations and integrated database (IDB) would have over ten thousands of SDTM annotations. This makes it very difficult and time-consuming to check all the annotations manually. In addition, some errors in the SDTM annotations are not obvious and it is very easy to overlook these errors if checking them manually. To overcome these issues, this paper proposes a unique way to check the errors in SDTM annotations using SAS. This approach uses SAS to extract all the SDTM annotations from aCRF and then compares them with the pre -defined rules. The SDTM annotations with issues could then be identified if they do not follow these rules. The proposed approach can greatly improve the quality of aCRF in an efficient way | dataset, sdtm domain, proc sort, cdisc | source | 9 |
| 17 | ADaM Implementation Guide for Medical Devices | Julia Yang, Silvia Faini, Karin LaPann |
This paper presents the soon-to-be published ADaM Implementation Guide for Medical Devices-v1.0 (ADaMIG MD). The guide is intended to address the typical statistical analysis needs for clinical trials using medical devices. Medical device data can be analyzed alone, or along with subject data. This drives the need for a new Class referred to as the Device Level Analysis Dataset (ADDL). The ADDL plays a role similar to the role of the ADSL, except that the identifier variable is the device identifier instead of the subject identifier. In the case of a device study, ADDL should be included. ADDL allows basic device-level information to be collected and merged with any other dataset containing the device identifier. Medical device analysis also requires two other new Classes: Medical Device Basic Data Structure (MDBDS), and Medical Device Occurrence Data Structure (MDOCCDS) | adam dataset, data science, adamig, clinical trial, bds, proc contents, sdtm domain, cdisc | source | 11 |
| 18 | ADaM-like Dataset: how to do big things in a short time | Oksana Mykhailova, Andrii Klekov |
There are long-term studies where either interim, futility analysis, conference membership with an intermediate result, or both of them are applicable. Let's name these requests as an Ad Hoc request. In the case of such requests for the study, there is no concrete scope of work during the trial and we should do repeat work from time to time. By definition of CDISC, ADaM dataset is a type of analysis dataset that follows to fundamental principles described in ADaM model document. As you know ADaM dataset goe s hand in hand with SDTM datasets but frequently SDTMs weren't finished and Ad Hoc requests coming. In this case, we propose to generate ADaM-like datasets. This paper describes the concept of ADaM-like datasets, different types of them and approaches to create them for Ad Hoc requests | raw data, adam dataset, efficacy summary, cdisc | source | 7 |
| 19 | Adding Rows to a BDS Dataset: When to Use DTYPE in Addition to Metadata | Sandra Minjoe | In simple ADaM Basic Data Structure (BDS) datasets, each SDTM row is the basis of one ADaM row. Things get more complicated when you need to add rows, such as to impute missing data or to create an entirely new parameter. In these more complicated cases, metadata is the primary way to explain how these derived rows differ from other rows. Sometimes BDS variable DTYPE (Derivation Type) can also be used, in addition to metadata, to describe a derivation within the data itself. This paper describes how different levels of metadata can be used for explaining derived rows, gives some examples of when to use and not use variable DTYPE, suggests what to do when variable DTYPE isn't appropriate, and provides automatable ways to determine if variables DTYPE and PARAMTYP are being used correctly | bds, missing value, cdisc | source | 11 |
| 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 | 10 |
| 21 | Agile Project Management in Analysis and Reporting of Late Stage Clinical Trial | Sarad Nepal, Uday Preetham Palukuru, Peikun Wu, Madhusudhan Ginnaram, Ruchitbhai Patel, Abhilash Chimbirithy |
Analysis and reporting (A&R) in clinical trials requires careful planning especially when multiple projects are ongoing at the same time in a clinical trial. The current practice typically uses a waterfall project management structure that assumes one phase to be completed before another can start. When a clinical trial nears data base lock, the time demands from statisticians and programmers increases due to both planned and unplanned activities. Clinical study reports (CSR) that are required to be delivered in a short period of time after database lock also contribute to the increase in challenges and time put towards the project. Several agile management tools such as Confluence and Jira can be used to streamline the project plan in an agile way. In this paper, we present details of how agile project management tools were utilized to enhance team collaboration and ensure timely project delivery | clinical trial, data science, database lock, csr, data cleaning, regulatory submission, resource allocation | source | 11 |
| 22 | An Application of Flexible Operation Page Number Positioning Method for RTF Outputs | WenHsuan Wu | In clinical trials, Rich Text Format (RTF) is a general format that is used to create outputs like tables, figures, and listings (TFL). After preparing these outputs, combine multiple RTF outputs into one, then maintain the original content, title, footnote and page number is an arduous challenge for programmers. However, there are various methods to combine RTF outputs and there are no specific information or details on the operations of the page number. Hence, this paper summarizes an application of page numbering codes in RTF documents, including strength and weaknesses. In the meantime, to overcome all the drawbacks of the existing page numbering method, this paper presents a page number positioning (PNP) method that uses bookmarks to position the page number accurately with editing to RTF outputs, it can keep the operation flexible in various SAS generated RTF outputs | ods rtf, dataset | source | 7 |
| 23 | An efficient method to review annotation and create bookmark for case report form (acrf.pdf) using SAS program, FDF files, excel and Javascript in Acrobat | Chen-Jung Wu | A common method to annotate case report form (acrf.pdf) is to use Acrobat comment tool to draw box and add text; however, this method is time consuming. If the case report form is changed due to protocol amendment, it may need another round to annotate manually. Besides, regular SDTM process involves several people to prepare the package including case report form so it always has some inconsistent issues related to typo, font size and font style. Therefore, converting annotation to EXCEL can help to fix typo issue and update wording quickly; comparing the EXCEL with the metadata in xpt and define.xml. Moreover, making use of EXCEL to remap annotation to migrate page to new version of case report form is another efficient method if there are any changes for case report form due to the protocol amendment |
crf, excel, vital signs, protocol amendment, clinical research, txt, cdisc | source | 12 |
| 24 | An R Shiny Application: Validated Analysis Results Using R and SAS | Abu Zafar Mohammad Sami | The production of a validated statistical manuscript in an accurate and timely way is a very challenging task when analyzing a clinical trial. Due to its sustainability and reliability, SAS has been the programming tool of choice over many years. However, R is gaining importance in this arena because of the flexible usage of packages. The scope of this paper is to illustrate the process of an application tool that interactively produce s the validated industry standard TLGs (Tables, Listings and Graphics) as well as data traceability for various statistical methods such as Regression and Survival Analysis, 2 -way Tests, and HY's law. The user can interactively choose parameters to produce the desired outputs simultaneously through R and SAS scripts and thus cross check the results from the same input data. In this way, platform independent and accurate results are achievable in a quick manner | clinical trial, data cleaning, data lineage, macro parameter, proc step, proc iml | source | 9 |
| 25 | Analysis and Reporting in Regulated Clinical Trial Environment using R | Peikun Wu, Uday Preetham Palukuru, Yiwen Luo, Sarad Nepal, Yilong Zhang |
Analysis and Reporting(A&R) of clinical data is an essential part of clinical trials. The use of R is popular in various stages in drug development. In particular, there is a growing interest in analyzing clinical trial data using R and its associated software under a regulatory environment. To ensure the analysis and reporting is regulatory compliant, a formal A&R process using R is required to be defined within an organization. In this paper, we propose an A&R workflow for data analysis and reporting using the R package folder structure. This proposed workflow will cover aspects of development and validation of Table, Listing, and Figure (TLF) using R within a regulatory environment. The proposed workflow is illustrated by using a dummy study. We hope this proposed A&R workflow can help the industry build guidance on utilizing R for data analysis and reporting for clinical trials | data cleaning, clinical trial, clinical data, data science | source | 11 |
| 26 | Annotating CRFs More Efficiently | Noory Y. Kim, Bhagyashree Shivakumar |
Annotating a CRF entirely by hand can be tedious and time-consuming. Also, the result can be fragile: A change in the CRF, such as the addition or deletion of pages, can require a lot of rework (as it is not possible to drag and drop annotations between pages of a PDF file). How can we streamline our process and make its output more robust? This paper presents a way to update the metadata of CRF annotations iteratively, in which SAS and SAS XML Mapper process and transfer the metadata between EXCEL and PDF files. The metadata can be adjusted either by resizing boxes within a PDF file or by replacing values in an EXCEL spreadsheet. This approach assumes users have access to SAS XML Mapper and a v ersion of Adobe Acrobat that can import and export XFDF files. Knowledge of a programming language other than SAS is not required |
excel, dataset | source | 20 |
| 27 | Application to Automate Clinical Study Report Generation using AI | Farha Feroze | Creating Clinical Study Report (CSR) highly manual and time consuming, which require other medical writer to perform the quality control of numbers on the Table, safety narratives and information from other documents. Significant amount of information to the CSR comes from other sources such as Protocol, SAP, Safety Narratives, In-text tables etc. The appendices section is constructed from CRF, TLF etc. Automating the CSR creation by utilizing emerging technology such as machine learning, Natural language processing and Natural language generation (ML/NLP/NLG) will effectively reduce the manual efforts. This paper will discuss about one such Algorithm of Machine learning which was implemented in a tool. The tool is designed to produce pre-filled CSR with information from Protocol, SAP and other sources and interpretation of in-text tables in the respective sections of the template that follows ICH- E3 guideline | csr, machine learning, clinical trial | source | 5 |
| 28 | Archival Tool: Automation using PowerShell | Sumit Pratap Pradhan | In clinical trials, patient data is organized into standard format using SDTM. Further, Statistical analysis is performed using Analysis dataset (ADaM) and reporting (table, listing and figure). Several deliveries (e.g. dry run, draft run etc.) are performed for each clinical study. For each delivery, Biostat programming team performs following tasks at minimum: 1. Datasets (SDTM and/or ADaM) and/or TFLs (tables, listings and figures) are sent to sponsor 2. Backup is created to save files related to each delivery. For example - Specification, Programs, Outputs (datasets and reports), log files etc. 3. Upload documents on Trial Master File (TMF) which is needed for effective monitoring and supervision (audit). All above mentioned tasks can be performed manually but it will consume a lot of unnecessary time, and consistency can not be ensured. For example - Programmers might choose different sets of files as backup based on their judgement | error message, adam dataset, final analysis, define-xml, sdtm domain, excel, clinical trial, cdisc | source | 9 |
| 29 | Assembling Reviewer-friendly eSubmission Packages | Shefalica Chand | One thing 2020 has taught us and reenforced is the importance of getting novel treatments out faster to the patients. Pharma and Biotech companies are constantly racing against time to develop new therapies as fast as possible, but once the drug is submitted for regulatory review, the clock switches hands to FDA for review and decision-making. A fair question, then, is what Biometrics staff preparing submissions could do in advance to help facilitate and expedite this review and decisioning process. FDA regularly shares guidelines, specifications, recommendations, and general considerations for sponsors to submit standardized study data for animal and human trials in the form of documents like the Study Data Technical Conformance Guide, Electronic Common Technical Document (eCTD), FDA Data Standards Catalog, etc | inspection readiness, excel | source | 11 |
| 30 | Automated CONSORT Flow Diagram Generation by SAS Programming | Ryan Yu | Consolidated Standards of Reporting Trials (CONSORT) flow diagrams are often included in the Clinical Study Report to provide a bird's eye view of the flow of patients through different stages of clinical trials. In the past these diagrams were often created manually. In this project, a SAS macro (SAS version 9.4) is developed to automate CONSORT flow diagram generation from analysis data, ADaM ADSL (subject level analysis dataset). The SAS macro is designed to create and display three basic elements (box, arrow and text) in the diagram by SAS SGPLOT procedure. ADaM ADSL dataset is used as the input of the macro. ADSL includes all the information needed to create the diagram. The disposition table in Clinical Study Report (CSR) is usually generated from ADSL. Using same data source will ensure consistency between CSR tables and the CONSORT diagram |
treatment period, numeric variable, macro call, macro definition, character variable, macro parameter, csr, sample, clinical trial, screening period, cdisc | source | 10 |
| 31 | Back to Basics: Running an Analysis from Data to Refinement in SAS | Deanna Naomi Schreiber-Gregory | Data Science is the new Space Race, launching us into a world of immeasurable possibility, but with only a few people to help us navigate it. As we dig deeper, discover more, and risk more, we can be simultaneously led to both great insight and loss. If we do not know what we are doing, Data Science can be a very dangerous thing. It is important for us all to learn at least a little bit about the possibilities and risks of this field of study, so we can navigate it together. This paper was written to give individuals new to SAS and/or Analytics a gentle nudge in the direction of the possibilities available through Data Science and SAS. It is designed to help you navigate through the process of data exploration by using publicly available COVID 19 data. We have all seen how fragile this data reporting can be, an d this paper uses this fragility to help explain the dangers of an inappropriately implemented analytic process | data science, nominal variable, missing value, null hypothesis, response variable, predictor variable, numeric variable, sample, sample size, logistic distribution, randomization, regression analysis, binary variable, interval variable | source | 21 |
| 32 | BAYESIAN Approach on COVID-19 test results | Vidya Srinivas, Ankita Sharma |
Clinical trials are very expensive and their outcomes are crucial to the target population, concerned stakeholders and, hence, there is considerable pressure to optimize them. One route of optimization is to make better use of all available information, and Bayesian statistics provides this opportunity. Considering the current situation with Polymerase Chain Reaction (PCR) testing for COVID-19, false negative tests are particularly concerning, potentially leading to an inappropriate sense of security regarding infectivity. To accurately interpret test results, one needs to know the positive and negative predictive values of a test in the setting applied, which depend on its sensitivity and specificity, along with prevalence or pre-test probability. Bayes theorem can be applied to the interpretation of negative PCR | clinical trial, bayesian statistics, likelihood function | source | 4 |
| 33 | Better to Be Mocked Than Half-Cocked: Data Mocking Methods to Support Functional and Performance Testing of SAS Software | Troy Martin Hughes | Data mocking refers to the practice of manufacturing data that can be used in software functional and performance testing, including both load testing and stress testing. Mocked data are not production or "real" data, in that they do not abstract some real-world construct, but are considered to be sufficiently similar (to production data) to demonstrate how software would function and perform in a production environment . Data mocking is commonly employed during software development and testing phases and is especially useful where production data may be sensitive or where it may be infeasible to import production data into a non -production environment (such as where production data contain sensitive PII or PHI, company secrets, or federal government super -secrets) | flat file, numeric variable, character variable, dataset, if-then-else, cpu time, key performance indicator, macro definition, sample, proc freq, %if-%then-%else | source | 17 |
| 34 | BIMO SAS Macros and Programming Tools | Mi Young Kwon, Rohit Kamath |
As part of the regulatory review process, FDA conducts site inspections to ensure t hat clinical investigators, sponsors, and Institutional Review Boards (IRBs) comply with regulations. The current submission format for study data in NDA and BLA packages does not facilitate efficient site selection for FDA because these data are submitted as subject-level data. Therefore, FDA has requested that pharmaceutical companies submit data that describes the characteristics and outcomes of clinical investigations at the site level. FDA uses this data to plan their site inspections. The submission o f data for this request is also sometimes referred to a Bioresearch Monitoring Program (BIMO) submission because the data is placed in the BIMO section of Module 5 in the eCTD. FDA BIMO develops guidelines for inspections of clinical investigators, sponsors, and IRBs | excel, sample, adverse events, clinical database, primary endpoint, concomitant medications | source | 12 |
| 35 | Biomedical Concepts - An Emerging CDISC Data Standard: A Stepwise Approach for Building a Library of Data Definitions | Kirsten Langendorf, Johannes Ulander |
In the Quarter Four 2020 CDISC Newsletter it was announced that as part of the 360 project "Starting in January 2021, CDISC will kick off the CDISC 360 Implementation focused on Biomedical Concepts and collaborative curation." CDISC is introducing a new standard - the Biomedical Concepts (BCs) - in and iterative manner with the goal of showing specific and tangible value to the community. To get this work started (Collaboration with CDISC) we developed a mining tool that will provide the first draft of the BCs from reading a set of define-xml files. A set of define-xml redacting scripts were developed to ensure that those who contributed define-xml files were comfortable sharing this information. The redacted define-xml files were read into the BC mining tool to provide the superset of information to create BCs to form a first version of a BC Library. In this presentation we will present: What is a biomedical concept and what are the benefits in using the BCs for data collection | vital signs, data cleaning, cdisc | source | 15 |
| 36 | Biomedical Informatics and Safety Analytics in the Office of New Drugs, CDER/FDA - A 2021 Update | Vaishali Popat | source | 21 | ||
| 37 | Bookdown and Blogdown: Deep dive of the R packages and components needed to create documentation and websites. | Benjamin Straub | This is a companion paper to the paper Bookdown and Blogdown: Using R packages to document and communicate new processes to Clinical Programming from the Strategic Implementation section. Here I will focus on some of the core programming components of building documentation and websites from the packages bookdown and blogdown. The reader will be given a detailed walked through of some of the core code that can help create simple documentation or a website. A public GitHub Repository and website have been established that uses both packages and will be available for the foreseeable future. Most of the example code discussed in this paper are housed in the GitHub Repository Deployment and use of these products is in the context of clinical programming but could be modified to fit any domain | clinical programming, stage 2, r package | source | 10 |
| 38 | Bookdown and Blogdown: Using R packages to document and communicate new processes to Clinical Programming | Benjamin Straub | The landscape of Clinical Reporting is changing fast. Open source languages that used to be off-limits or seen as only niche hobbies are now being embraced industry wide. Providing documentation and communications surrounding the use of an open source language can be a herculean task for any Clinical Programming department. At GSK, the Clinical Programming (GSK-CP) department has decided to embrace the use of the R language as its open source language of choice. The choice of R has allowed the GSK-CP department to make great use of two packages, bookdown and blogdown, to help document and communicate the use of R for Clinical Reporting. This paper will focus on GSK-CP's journey with using R and how bookdown and blogdown have helped to accelerate and codify our use of R for Clinical Reporting | stage 2, clinical programming, proof of concept, sample, clinical research, independent qc, data science, r package | source | 11 |
| 39 | Build a model: Introducing a methodology to develop multiple regression models using R in oncology trial analyses | Girish Kankipati, Jai Deep Mittapalli |
Regression analysis is a widely used modeling tool to estimate the relationship between two variables in statistics: a predictor variable whose value is observed through experiments and a response variable whose value is derived from the predictor. The general mathematical equation for linear regression is Y = aX + b, where Y is the response or dependent variable, X is the predictor or independent variable, and a and b are constants called coefficients. If two or more predictor variables have a linear relationship with the dependent variable, the regression is called a multiple linear regression. In R, the lm function is widely used to build such regression models. This paper discusses a step-by-step process to build a multiple regression model in R using an example oncology trial data set: a) Get the regression equation on each predictor using the lm function. b) Check if multicollinearity exists per a pairwise Pearson's coefficient | regression analysis, correlation, predictor variable, proc reg, outlier, response variable, residual, feature selection, significance level, sample, multiple regression | source | 20 |
| 40 | CDISC ADaM Phases, Periods, and Subperiods: A Case Study | Jennifer K. Fulton | Many typical clinical studies, especially Phase 1 or 2, are comprised simply of Screening, Treatment, and Follow-up time periods Occasionally the Treatment portion of the study needs to be divided further into analysis periods, and even sub- levels within one or more of the analysis periods. Two examples are crossover studies, and studies that include a break or "wash out" period between doses of study medication. If it is important, for example, to know what the study circumstances were at the time when a particular adverse event started, then the CDISC ADaM permissible Phase, Period, and Subperiod variables should be utilized. This paper will present a case study on an actual clinical trial that presented interesting challenges for the correct implementation of the ADaM guidelines for these variables. In this study the route of administration was being investigated. Rather than comparing study drugs, the route changed for the same subject from one set of visits to the next | follow-up period, clinical trial, character variable, numeric variable, phase i, macro definition, study day, treatment period, cdisc | source | 9 |
| 41 | Challenges and Solutions for an ISM Development | Si Ru Tang, Yanhong Li |
The integrated summary of microbiology (ISM) is the special integrated summary of efficacy (ISE) in antibacterial drug submission for regulatory approval. Designing integrated datasets and presenting microbiology results are very challenging for programmers due to the complexity of data structures and logics. This paper discusses the challenges we met recently when working on an ISM. It elaborates how we transferred external EXCEL data to internal SDTM format data, and how we adopted the data standards and pooled multiple studies' data sets together. It provides the method about how to merge treatment groups for a better display format in the reports. It also shares some tips about how we designed ADMB and ADMS datasets to include microbiological responses, pathogen names, and treatment groups so that we could generate them all in one table. It then introduces how we composed the submission document - ADRG for this ISM |
adam dataset, class variable, phase iii, excel, phase ii, cdisc | source | 9 |
| 42 | Chasing Master Data Interoperability: Facilitating Master Data Management (MDM) Through CSV Control Tables that Contain Data Rules that Support SAS and Python Data-Driven Software Design | Troy Martin Hughes | Control tables are the tabular data structures that contain control data -the data that direct software execution and which can prescribe dynamic software functionality. Control tables offer a preferred alternative to hardcoded conditional logic statements , which require code customization to modify . Thus, control tables can dramatically improve software mai ntainability and configurability by empowering developers and, in some cases, nontechnical end users to alter software functionality without modifying code. Moreover, when control tables are maintained within canonical data structures such as comma - separated values (CSV) files, they facilitate master data interoperability by enabling one control table to drive not only SAS software but also non -SAS applications. This text introduces a reusable method that preloads CSV control tables into SAS temporary array s to facilitate the evaluation of business rules and other data rules within SAS data sets |
dataset, macro definition, master data management, macro variable | source | 5 |
| 43 | Collaborative Review of CDISC QRS Instruments | Steven A. Wilson, Steve Kopko, Dana Booth |
cdisc | source | 54 | |
| 44 | Common Dating in R: With an example of partial date imputation | Teckla Akinyi | R programming language and RStudio are increasingly being adopted in the pharmaceutical industry as part of submission packages which include ADaM data sets. It is imperative that clinical p rogrammers upskill to handling dates within RStudio. As an open source software there is a sprawl of information o n the internet on this topic covering several ways to manipulate dates in RStudio | imputation, character variable, sample, adverse events, cdisc | source | 14 |
| 45 | Containers You Can Count On: A Framework for Qualifying Community Container Images | Eli Miller | Virtualization is being explored by many industry groups as it presents a solution to several challenges that some organizations have struggled with. Container runtimes allow for system architects to design reproducible, immutable environments that can be portable, easily qualified, and hosted on a variety of different hosts. Despite these benefits, many organizations do not take advantage of the developing use of containers. This paper will discuss certain aspects of containerization that solve unique challenges the industry faces and explore aspects of containerization that may present challenges for their use in regulatory submissions | clinical research, pharmaceutical industry | source | 4 |
| 46 | Creating Hyperlinks in SDTM Specifications | Carey G. Smoak | SDTM Specification documents (usually EXCEL files) can be difficult to navigate due to the large number of worksheets within the EXCEL file. In this paper, I show two methods for creating hyperlinks that all the user to more easily navigate the EXCEL file. One method involves using PROC FORMAT and the other method involves using a COMPUTE BLOCK in PROC REPORT. I have only shown two types of hyperlinks, but you can probably think of other hyperlinks that would be useful in navigating SDTM Specification documents. I given you to tools to add your own hyperlinks |
excel, proc step, proc format, proc report, cdisc | source | 5 |
| 47 | Creating Patient Profiles Using SAS Software: Proc Report & SAS ODS | Ballari Sen | Patient profiles provide comprehensive information for a single subject participating in a clinical study. The profile template includes relevant clinical data for a subject that can help understand adverse events, concomitant medications, exposure, lab findings and other significant events and findings as a narrative or a visual report. The Reports are constructed to convey information about a single patient in a concise output within a company for data review. It acts as a key to identifying why a subject experienced adverse event, why the subject took concomitant medication or the effects of dosage on a subject for the investigational treatment. These different requirements and specifications could only be possible through certain amount of customized programming . SAS provides rich procedures to efficiently create these important reports. This paper will discuss how to produce patient profiles utilizing SAS software PROC procedures: PROC REPORT with the DATA STEP |
proc report, proc step, adverse events, left join, clinical trial, proc sql, concomitant medications, ods pdf | source | 13 |
| 48 | Customize and Beautify Your Kaplan-Meier Curves in Simple Steps | John Henderson, Lili Li |
Kaplan-Meier curves are among the most frequently employed graphs to visualize and demonstrate the estimate of survival patterns in clinical trials. SAS software offers simple tools for generating survival analysis results using PROC LIFETEST. Default graphical outputs by PROC LIFETEST are adequate for assessing simple outcomes; however, presentation of analysis results often requires various and complex customizations. Specifically, confidence interval is a very commonly requested addition to aid the interpretation of survival analysis data. Among different ways of displaying confidence intervals, confidence interval bracket is one particularly helpful feature that can provide a better representation of analysis at specific increments of interest |
proc step, proc sgplot, confidence interval, survival analysis, clinical trial, kaplan-meier plot, sample | source | 7 |
| 49 | Customizing define.xml files | Steffen Müller | A metadata description following the Define-XML standard is a key component of the electronic data submission package that is sent to the health authorities in drug approval processes. Since those authorities, e.g., FDA and PMDA, have differing requirements, it is useful to have a process in place that is able to convert a submission package from one standard to the other in a mostly automated way. An update of the contained controlled terminology or other components could also be a reason to update an existing submission package. This paper shows how such a conversion could be done using standard tools such as SAS, EXCEL, and Pinnacle 21 Community, focusing on the update of the contained define.xml file |
excel, where statement, dataset, controlled terminology, proc step, cdisc | source | 9 |
| 50 | Data Visualization in Real World Data (RWD) and Health Economics Outcomes Research (HEOR) using (Statistical Analysis Software) SAS | Swapna Ambati | Real-world data (RWD) analytics in Health economics outcomes research (HEOR) generates real -world evidence (RWE) to provide useful information that helps in decision -making in the healthcare setting and healthcare-related investment. RWE compliments clinic al trial findings and may help fill knowledge gaps related to how a medication is used in the real world. Data visualization techniques help explore, synthesize, and communicate the results of research studies. In this paper, we will explore data visualization methods using SAS analysis software specifically for standard H EOR analysis such as patient cohort extraction steps, demographics, comorbidities, cost, utilization, treatment patterns, medication, adherence, incidence, prevalence, and survey related analysis | proc step, sample, proc sgplot, bar chart, create table, proc sql, proc gchart, proc gmap, group by, histogram, scatter plot, proc freq, interval variable, line chart, data visualization, vertical bar chart, disposition table, proc format, proc sgpanel, proc sgscatter, proc surveymeans, bubble plot, odds ratio, numeric variable, proc surveyfreq, heatmap, order by, subject-level data, proc template, series plot, waterfall chart | source | 45 |
| 51 | Data Visualization Using GANNO, GMAP and GREMOVE to Map Data onto the Human Body | Rui Huang, Toshio Kimura, Jennifer McGinniss |
In many therapeutic areas, symptom severity is captured separately for different body regions. Reporting these data through traditional summary tables by body regions and symptoms will be difficult for readers to understand and interpret. In contrast, data visualization tools can communicate complex information in an easily digestible format. This paper demonstrates two visualization methods in SAS to map data onto the human body where colors and symbols represent severity and symptoms in different body regions. The first approach uses the SAS GANNO function on a background image of the human body to overlay symbols on impacted body regions via X, Y coordinates with Unicode symbols indicating symptoms, size representing proportion of patients and color representing severity. The second approach handles the human body as geographic regions. This method requires a map dataset of the body regions which can be generated in SAS JMP through the Custom Map Creator add-on | proc step, data visualization, proc gmap, proc freq | source | 12 |
| 52 | Delight the Customer using agile transformation in Clinical Research | Aman Bahl, Steve Benjamin, Hrideep Antony |
In the new era of drug development, the pharmaceutical industry needs new solutions to meet rapidly changing client and patient necessities. The healthcare industry is under increasing pressure to increase the customer experience, improve quality, cost efficiency, and come up with innovative solutions to keep up in rapidly evolving markets. Applying agile methodologies will allow clinical organizations to collaborate, focus swiftly, and diligently prioritize the innovations and developmental tasks that matter the most, resulting in a shorter developmental timeframe. Agile methodology was created as a response to the shortcomings of traditional development methods such as the waterfall method that is traditionally used in the drug development process | clinical trial, clinical research, adaptive design, data science | source | 9 |
| 53 | Design ADaM Specification Template that Simplifies ADaM Programming and Creation of Define XML in CDISC Era | Xiang Wang, Daniel Huang |
Regulatory agencies such as FDA require sponsors to implement CDISC standards for any clinical studies started after December 17, 2016, thus it is essential if companies can automate the process from ADaM specifications to Define.xml and ensure they are fully CDISC compliant. To our knowledge, many companies still conduct heavily manual work on this process. In this paper we exhibited a well -designed ADaM specification template that is user-friendly for ADaM datasets programming, and then using R tool to convert the specification in the format that is ready to be imported into Pinnacle 21 Enterprise (P21E) to create ADaM define.xml. This new approach helps statistical programmer for creating ADaM datasets more smoothly and generating Define xml more efficiently | adam dataset, excel, define-xml, macro definition, statistical programmer, data cleaning, cdisc | source | 11 |
| 54 | Developing SAS Macro to Find Subject-s Latest Available Date from Entire Database | Raghava Pamulapati, Sandeep Meesala |
In Data Analysis and Reporting, programmers often need to find the subject's latest available date for analysis. To find this date, we need to check every possible date variable from the entire study database. In general, to find the latest available date from multiple date variables, a programmer needs to either sort the data by the date variable and take the latest date by FIRST.VARIABLE or LAST.VARIABLE or using GROUP BY Statement and MAX Function in PROC SQL. This process is time-consuming and hard to debug. The programing challenge is writing simple code to execute each data variable's above steps in all datasets in the study database efficiently and accurately |
date variable, call execute, proc step, proc sql | source | 9 |
| 55 | Differentiate Yourself | Kirk Paul Lafler | Today's job, employment, contracting, and consulting marketplace is highly competitive. As a result, SAS professionals should do everything they can to differentiate and prepare themselves for the global marketplace by acquiring and enhancing their technical and soft skills. Topics include describing how SAS professionals should assess and enhance their existing skills using an assortment of valuable, and "free", SAS-related content; become involved, volunteer, publish, and speak at in-house, local, regional and international SAS user group meetings and conferences; and publish blog posts, videos, articles, and PDF "white" papers to share knowledge and differentiate themselves from the competition | statistical software, big data | source | 18 |
| 56 | Don-t send the Macros, send the Catalog! The SAS Macro Catalog: a perfect way to send your macros to the client without sharing the code. | Steven C. Black | If you've ever gotten squeamish about sending a macro or a lot of macros to a client, SAS has created a way to help ease the tension. A SAS Macro Catalog allows you to send your macro along with the option of hiding the code or not. Saved as single file, the catalog is easy to transfer and apply to any study. A simple call will activate all the macros stored and then they can be used just as they are set up in your system. In this paper I will show how to create a SAS Macro Catalog, add macros to the catalog, and manage the catalog once it has been set up. I'll then demonstrate how to create a macro which adds a large number of macros to the catalog that have already been created. With this newfound skill , you can create and send out one file and rest easy knowing that your super fancy macro logic is safe and secure | macro catalog, macro definition, proc sql, proc step, infile statement, data step, macro loop, cdisc | source | 6 |
| 57 | Double Waterfall Plot Creation for Comparing Target Lesions Data | Anilkumar Anksapur, Raghava Pamulapati |
We use a waterfall plot to present individual patient's best overall percentage change and response to a treatment based on a parameter, such as the tumor burdens at each evaluation time point in Oncology Clinical Trials. A double waterfall plot accommodates two treatment best overall percentage change for each patient in a single plot for multi-treatment studies and crossover studies. We can also use double waterfall plots for intra-tumor (IT) studies. In an Intra-tumoral injection study, each patient has multiple target lesions with some lesions injected with treatment. A double waterfall plot presents the best overall change of injected target lesions and noninjected target lesions separately for each patient. We can use SAS PROC SGPLOT to create double waterfall plots |
waterfall chart, change from baseline, treatment period, percent change from baseline, proc sgplot, proc step | source | 9 |
| 58 | Dressing Up your SAS/GRAPH and SG Procedural Output with Templates, Attributes and Annotation | Louise S. Hadden | Enhancing output from SAS/GRAPH has been the subject of many a SAS paper over the years, including my own and those written with co-authors. The more recent graphic output from PROC SGPLOT and the recently released PROC SGMAP is often "camera-ready" without any user intervention, but occasionally there is a need for additional customization. SAS/GRAPH is a separate SAS product for which a specific license is required, and newer SAS maps (GfK Geomarketing) are available with a SAS/GRAPH license. In the past, along with SAS/GRAPH maps, all mapping procedures associated with SAS/GRAPH were only available to those with a SAS/GRAPH license. As of SAS 9.4 M6, all relevant mapping procedures have been made available in BASE SAS, which is a rich resource for SAS u sers, and in SAS 9.4 M7, further enhancements were provided |
proc step, proc sgplot, sas base, proc greplay, histogram, sample, bubble plot, proc gmap, output delivery system | source | 16 |
| 59 | Dynamic macro using the convergence dataset to generate the Odds Ratio (OR), Risk Ratio (RR) and Risk Difference (RD) per SOC/PT using Genmod procedure | Jagadish Katam | This paper talks about the displays, where we are supposed to present the OR, RR and RD per SOC and PT across the treatment using the GENMOD procedure. Sometimes, when using this genmod the OR, RR and RD will not be created per SOC and PT when there are missing/less subjects in any of the treatment group. AE displays will have many SOCs and PTs. If a particular SOC or PT does not have the sufficient subjects across the treatments, the GENMOD procedure will throw an error and do not generate the OR, RR or RD results/outputs. In these cases, the macro that I have created will be useful to dynamically handle the situation to produce the results. This macro is based on the convergence criteria. Using this macro, the convergence criteria will be checked using the GENMOD procedure for each SOC/PT for the OR, RR and RD values and store the convergence criteria in a separate dataset |
proc genmod, call execute, relative risk, mock shell, ods output, keyword parameter, %macro parameters, macro call, macro parameter, output, output delivery system, if-then-else, proc step, response variable | source | 20 |
| 60 | e-Submissions key difference of FDA and PMDA | Giri Prasad, Hareeshkumar Gurrala |
Recent drug developments are done in a more globally integrated way and a similar clinical data package should be used for regulatory submissions. Preparing regulatory submissions in the pharmaceutical industry can be a very stressful experience due to the large amount of work and the need to research and understand regulatory and e-submissions requirements. The FDA (Food and Drug Administration) and PMDA (Pharmaceuticals and Medical Devices Agency) are playing a pivotal role and are responsible for securing public health by assuring that marketed and new medical products are safe and effective, and assessment of potential new therapies are done properly. The FDA and PMDA have started accepting electronic study data (e-study data) submissions from 2016. Most of the requirements in e-submissions of PMDA are similar to the FDA's requirement. However, there are some key differences in the requirements between both health authorities | data transformation, adam dataset, cdisc | source | 8 |
| 61 | Easing the Process of Creating CRF Annotations | Samadi Karunasundera, Jun Wang |
According to FDA and CDISC guidance, annotated case report form (aCRF) is a mandatory document that need to be submitted to authorities. This paper addresses two ways of creating aCRFs. The first method involves the manual consolidation of distributed work on distinct CRFs into a single collaborative pdf file. The second method involves replicating the annotations programmatically by creating a dictionary of annotated standard forms which the program will use to match up the annotations to the correct CRFs | crf, annotated crf, cdisc | source | 7 |
| 62 | Effective Approach for ADaM Submission to FDA and PMDA | Ranvir Singh | Regulatory Authorities are continuously updating their submission requirements, like Food and Drug Administration (FDA) released the Study Data Technical Conformance Guide V4.6 in November 2020 and Pharmaceuticals and Medical Devices Agency (PMDA) revised the Technical Conformance Guide on Electronic Study (e-Study) Data Submissions in January 2019. Accordingly, CDISC Analysis Data Model Implementation Guide (ADaMIG) versions are being continuously evolved. Currently FDA is accepting the Analysis Data Model (ADaM) datasets as per ADaMIG V1.1 whereas PMDA requires as per V1.0. Hence, it is a challenge to adhere to the differences between the guidelines of these two agencies. If any Sponsor needs to get approval for a Drug in both the US and Japan, then the Study data must be in compliance with both the regulatory requirements and CDISC standards | data cleaning, adam dataset, data validation rules, clinical trial, cdisc | source | 6 |
| 63 | Essential Programming Techniques Every SAS User Should Learn | Kirk Paul Lafler | SAS software boasts countless functions, algorithms, procedures, options, methods, code constructs, and other features to help users automate and deploy solutions for specific tasks and problems, as well as to access, transform, analyze, and manage data. This paper identifies and shares essential programming techniques that a pragmatic user and programmer should learn. Topics include determining the number of by-group levels that exist within classification variables; data manipulation with the family of CAT functions; merging or joining multiple tables of data; performing table lookup operations with user-defined formats; creating single-value and value-list macro variables with PROC SQL; examining and processing the contents of value-list macro variables; determining the FIRST., LAST. and Between by-group rows; processing repetitive data with arrays; and using metadata to better understand the contents of SAS datasets |
proc step, proc sql, hash table, proc freq, proc print, if-then-else, dictionary tables, character variable, missing value, excel, macro variable, order by, dataset, proc append, lookup, character functions | source | 26 |
| 64 | Evaluating anthropometric growth endpoints with Z-Scores and Percentiles | Snehal Sanas, Pradeep Umapathi |
Disease conditions in children can potentially affect a child's growth and development, hence an effective treatment for them is required to sufficiently demonstrate improvement, not only in disease condition but also in growth pattern. Unlike adults, assessment of drug safety and efficacy in children requires specific consideration of the impact of treatment on child growth. Measuring overall child development can be a 'complex task', as it involves evaluating anthropometric measures like weight, head circumference and mid-upper arm circumference at different stages of development. Clinical trials involving pediatric populations need to have composite efficacy endpoints to account for the impact on growth. WHO and CDC have established worldwide anthropometric data standards against which a child's growth can be validated and conclusions can be drawn as to whether the child is growing correctly or has any growth issues | numeric variable, character variable, sample, clinical trial, data science | source | 11 |
| 65 | Extracting Data Standards Metadata and Controlled Terminology from the CDISC Library using SAS with PROC LUA | Lex Jansen | The CDISC Library is the single, trusted, authoritative source of CDISC Data Standards metadata and Controlled Terminology. It uses linked data and a REST API to deliver CDISC Data Standards metadata and Controlled Terminology in a machine-readable format to software applications that automate standards-based processes. This paper shows how metadata can be extracted in SAS through the CDISC Library API using a REST API request with PROC HTTP. PROC LUA will be used in SAS to manage the PROC HTTP requests. PROC LUA will also be used to parse JavaScript Object Notation (JSON) response strings with a JSON library to extract data before storing them in SAS data sets. The last part of the paper will focus on loading the extracted data sets into the Data Standards and Controlled Terminology areas of the Clinical Management module of SAS Life Science Analytics Framework |
rest api, proc step, proc lua, dataset, proc http, excel, life sciences, macro variable, cdisc | source | 23 |
| 66 | Fast, Efficient, and Automated Real-time Analytics for Clinical Trials | Aditya Gadiko, Christopher C. Hurley, Anish Iyer |
Opportunities are boundless for real-time visual analytics to provide rapid, actionable insights from essential clinical trial information. Dashboards based on standard CDISC data domains empower trial stakeholders to quickly react to the tens of thousands or even hundreds of thousands of data points collected throughout the lifecycle of a clinical trial. Operational data can be displayed in dashboards to significantly improve the efficiency of the clinical trials. Data visualization can be very helpful when looking for hidden trends in the data and assessing risks from any aspect of the trial. Dashboards can be used to augment the establishment of efficacy, tolerability, and safety profiles of investigational therapies. Application Programming Interface (API) functionality with near -real-time visibility into the EDC domains provides frequent data updates when time is of the essence | clinical trial, sas visual analytics, extract, data review, cdisc | source | 5 |
| 67 | FDA Study Data Technical Rejection Update | Ethan Chen | Presentation | data cleaning, data validation rules, clinical trial, business intelligence, data standard, trial summary, sample | source | 36 |
| 68 | First Time Creating a Submission Package? Don-t Worry, We Got You Covered! | Lyma Faroz | Creating a clinical trial data package for electronic submission to a regulatory agency is a daunting task. There are many steps that must be executed with precision and efficiency to create a good quality submission package. If you are working on a submission study for the first time, then this paper is for you. Each electronic submission contains 5 modules; however, this paper will focus on steps involved in creating data set components for Module 5. This includes validating SDTM and ADaM data sets using Pinnacle 21 Enterprise software, generating reviewer's guides (cSDRG and ADRG), creating the define.xml for SDTM and ADaM, and much more! We will also look at important points from FDA's Study Data Technical Conformance Guide and sprinkle in various lessons learned from earlier submissions in pulling all this together to create a high-quality submission package | annotated crf, excel, raw data, clinical data, cdisc | source | 10 |
| 69 | Forward Planning: How to get the most of your eCOA data | Monali Khanna, Franklin E. Menius |
The use of electronic clinical outcome assessments (eCOA) offers numerous advantages for clinical trials, such as, increased patient involvement, better understanding of patient experience, undisrupted data collection, and higher patient compliance. Clinical outcome assessments are, generally, assessed using standard validated questionnaires, where the responses to items are collected and stored in a database. It is important to understand how collected questionnaire data will be utilized for the final statistical analyses. Once it is decided that clinical outcome assessments are to be used in a clinical trial, early planning as to how captured data is stored, transferred, and used for reporting purposes becomes important. Engaging in discussions early can help achieve efficient data capture, expedite database lock and regulatory approvals | clinical trial, missing value, audit trail, data governance, proc freq | source | 7 |
| 70 | From %Let to %Local; Methods, Use, and Scope of Macro Variables in SAS Programming | Jayanth Iyengar | Macro variables are one of the powerful capabilities of the SAS system. Utilizing them makes your SAS code more dynamic. There are multiple ways to define and reference macro variables in your SAS code; from %LET and CALL SYMPUT to PROC SQL INTO. There are also several kinds of macro variables, in terms of scope and other ways. Not every SAS programmer is knowledgeable about the nuances of macro variables. In this paper, I explore the methods for defining and using macro variables. I also discuss the nuances of macro variable scope, and the kinds of macro variables from user-defined to automatic |
macro variable, call symput, macro put, proc sql, into, automatic macro variable, sas log, proc step, macro definition, symbol table, cpu time, macro facility, macro parameter, call symputx, local symbol table, macro quoting function, compilation phase, put statement, macro trigger, global symbol table, variable, %macro parameters | source | 23 |
| 71 | From SAS Dataset to Structured Document | Hengwei Liu | A structured document is an electronic document whose contents are organized into labeled blocks according to a schema. This is done through a mark-up language such as HTML or XML. If the information to be displayed in the structured document are saved in a SAS dataset, the document can be generated through programming. In this paper we discuss how to use SAS and some other programming languages to do that | dataset, where statement | source | 7 |
| 72 | GCIG Criteria, Programming Makes It Easy | Liming Xie, Wei Wei |
Ovarian cancer is a heterogeneous and genomically unstable cancer and as technologies advance, there is a great potential to incorporate biomarkers into ovarian cancer diagnosis, prognosis, and therapy choices. Carcinoma antigen-125 (CA-125) is the most commonly used biomarker in ovarian cancer and has been examined in many pivotal clinical trials for drug approval in ovarian cancer. The Gynecological Cancer Intergroup (GCIG) proposes the criteria for CA-125 response and progression and specified the situations which criteria could be used in. In addition to Response Evaluation Criteria in Solid Tumors (RECIST) criteria, GCIG CA-125 response and progression criteria have become more and more popular in clinical trials of ovarian cancer and usually serves as one of the secondary endpoints. However, GCIG criteria are more complicated than RECIST criteria and not programming-friendly, especially when combined with RECIST criteria Version 1.1 | sample, clinical trial, secondary endpoint, standard deviation | source | 9 |
| 73 | Generating .xpt files with SAS, R and Python | YuTing Tian, Todd Case |
The primary purpose of this paper is to first lay out a process of generating a simplified Transport (.xpt) file with RStudio and Python to meet study electronic data submission requirements of the Food & Drug Administration (FDA). The second purpose of this paper is to compare the .xpt files created from three different languages: R, Python and SAS. The paper is the expansion of the original FDA guideline document "CREATING SIMPLIFIED TS.XPT FILES", published in November, 2019. Transport files can be created by SAS, as well as open source software, including R and Python. According to the FDA guideline document mentioned above, .xpt files can be created by R and Python. This may allow Pharmaceutical companies to expand use of R and Python beyond data visualization and statistical analysis currently being generated by these two languages. Hopefully, readers can use the process shown in the paper as a template to create .xpt files | dataset, trial summary, data science | source | 11 |
| 74 | Generating FDA-ready Submission Datasets directly from EHRs | Jozef Aerts | The use of Real-World-Data (RWD) in clinical research is growing rapidly, especially due to the rise of the HL7-FHIR standard [1] for electronic health record (EHR) exchange, with its standardized API. FDA however requires study data to be submitted as tables using the SDTM standard and in the outdated SAS Transport 5 format [2]. A new way to automatically generate SDTM data sets with embedded source FHIR records directly from the EHR system is presented. It mostly uses existing and by us developed LOINC-CDISC mappings [3] and a by us developed RESTful web service[4]. We also discuss aspects of the new FHIR-to-CDISC project [5], and how it can further boost the use of RWD in clinical research. Furthermore, the by the FDA so much desired "real rolling data review" is discussed in the scope of APIs, RESTful web services and modern exchanges formats such as XML, JSON and RDF | web service, clinical research, vital signs, sdtm domain, adverse events, controlled terminology, sample, cdisc | source | 9 |
| 75 | Generating Synthetic CDISC Clinical Trial Data. | Jose C. Lacal | This paper presents a Python-based platform that generates Synthetic CDISC Clinical Trial Data at scale. The platform programmatically generates realistic synthetic persons (SynthPerson) that have a complete synthetic Personal Health Record (SynthPHR). Each SynthPerson lives in a series of geographically-accurate synthetic cities (SynthCity). Groups of SynthPerson are then randomly enrolled in a synthetic Clinical Trial (SynthTrial). Platform users define the parameters of each synthetic clinical trial and the platform generates all CDISC SDTM domains for the desired number of subjects | clinical trial, sdtm domain, adverse events, controlled terminology, life sciences, cdisc | source | 14 |
| 76 | Generating the Demographics and Adverse Event Tables Using Excel and VBA | David W. Franklin | Microsoft EXCEL is not regarded as a package to do statistical analysis for a number of reasons, namely a spreadsheet is not technically advanced enough to ensure proper data/documentation management and workflows, and statistical functions are lacking or not beyond the capabilities of serious statistical computational needs. Despite this, EXCEL is still the leading software package used to do statistics. This paper looks at two distinct table types, the Demographics and the Adverse Event tables; and three distinct categories of data, the categorical, continuous and occurrence categories, and shows that EXCEL with VBA can indeed be used to produce these tables. While not used for publication or serious reporting, these could be used as a way to look at the data before 'official' reports are available. Along the way, we will also see how an Adverse Event Timeline Plot may be created. and ADAE |
excel, adverse events, name range list | source | 9 |
| 77 | Global Vaccine Minimum Dataset | Gabriela Lopez Mitnik | Presentation | source | 3 | |
| 78 | Graphical Representation of Clinical Data using Heat Map Graph | Pavani Potuganti, Sree Nalluri |
A heat map graph is a visualization technique of representing data in a two- dimensional form using variations in colors to show different data values like extreme colors represents extreme values. The goal of heat map is to provide a colored visual summary of information and giving a better insight. Heat Maps are commonly used to monitor region wise weather, thermal values, geographical heights, and migrations of people etc. In clinical trial domains heat map graphs can be used to represent adverse events by treatment, metabolic abnormalities, infection rate in children vs young adults or male vs female etc. This paper demonstrates the relative efficiency of treatment groups versus placebo with a practical example using SAS procedure and techniques enhancing the graph | heatmap, adverse events, proc freq, system organ class, proc sgplot, class variable, response variable, proc step, sample | source | 10 |
| 79 | Graphs Made Easy Using SAS Graph Template Language | Manohar Modem, Bhavana Bommisetty |
In clinical domain, we used to create graphs using traditional SAS/GRAPH procedures like PROC GPLOT. To create better graphs, SAS developed Graph Template Language (GTL). GTL has many significant advantages over traditional SAS/GRAPH procedures like more control over visual attributes of the graph, less annotation and minimal coding. Statistical Graphics (SG) procedures like PROC SGPLOT , SGPANEL etc., which require minimal coding use GTL to create commonly used graphs. But, writing graphs in GTL has more advantages compared to SG procedures and traditional SAS/GRAPH procedures. GTL code might look a little lengthy and intimidating, but once you understand how to read GTL code, it will be very useful to create not only simple graphs but also complex graphs. The purpose of this paper is to help the programmer understand and utilize various elements of GTL to create wonderful graphs |
proc step, proc template, line chart, scatter plot, bar chart, box plot, class variable, proc lifetest, adverse events, forest plot, histogram, confidence interval, output delivery system, if-then-else, proc freq | source | 31 |
| 80 | Harness Analytical Strengths: SAS, R, Python Web Services | Sandeep Juneja, Ben E. Bocchicchio |
We all have our favorite system to code in and every programming language has its own inherent strengths and weakness. Programmers develop a firm understanding of the programming language they use to become proficient in problem solving. There are a few programmers that can code proficiently in more than one language, they are the lucky few. When it comes to problem solving, t he question is, are you using the best coding language to solve the problem? What if there was a way to submit your "problem solving need" to another program language tha t can easily solve the problem? Upon submission, it returns your desired solution. This would allow you to harness capabilities of various programming languages for problem solving | dataset, web service, problem solving | source | 4 |
| 81 | How I became a Machine Learning Engineer from a Statistical Programmer | Kevin Lee | One of the most popular buzz words 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 to seek experts who can implement Machine Learning into their businesses. The paper will be written for statistical programmers who want to explore Machine Learning career, add Machine Learning skills to their experiences or enter a Machine Learning fields. The paper will discuss about personal journey to become to a Machine Learning Engineer from a statistical programmer. The paper will share my personal experience on what motivated me to start Machine Learning career, how I started it, and what I have learned and done to be a Machine Learning Engineer. In addition, the paper will also discuss the future of Machine Learning in Pharmaceutical Industry, especially in Biometric department | machine learning, statistical programmer, deep learning, pharmaceutical industry, clinical programming, data science, feature engineering, neural network | source | 4 |
| 82 | How simulation will impact the future of Healthcare & Life Sciences. | Pritesh Desai, Allison Sealy, Shawn Tedman, Lois Wright, Bahar Biller |
Simulation is a way to model a real-life or hypothetical process so that it can be studied to understand how the system works. By changing the parameters in simulation, predictions may be made about the behavior of the system. Current simulation models in Healthcare and Life Sciences lack the details needed to glean valuable insights. This paper will review the current state of simulation in the Healthcare and Life Sciences space and provide relevant examples of recent projects completed in partnership with the SAS Operations Research Center of Excellence. A roadmap of future trends, developments, and discussion of how machine learning will play a role and improve patient outcomes | clinical trial, machine learning, life sciences, scenario analysis, key performance indicator, artificial intelligence, clinical research, adaptive design, sample, capacity planning | source | 14 |
| 83 | How to handle decision making in the CRO / sponsor relationship in matters that require expertise | Sarah McLaughlin | Programming for clinical trials requires an ever-expanding skill set. Many of us are working on trials with team members from both a sponsor company and one or more CROs. In order for these partnerships to be both efficient and successful it is important to assess what expertise exists within each organization and at what level it can be found | clinical trial, data science | source | 3 |
| 84 | How to ingratiate yourself with the "old-timers" | Kirsty R. Lauderdale, Kjersten Offenbecker |
Ever doubted that your program code would stand up to the review of some of the great programmers, ever wondered if your logic process makes sense and that your programming style would pass the "white - glove" test. This paper will help guide you through some of the steps that long-time programmers use to ensure their program codes are readable, transferrable, and robust. Start your journey to becoming a well-known name in the industry by following some of these handy tips and tricks, and instantly elevate your programming code to previous unattained levels. Definition of great may be self-proclaimed | proc step, data step, proc sql, data cleaning | source | 9 |
| 85 | How to Translate RTF Documents | Jundong Ma, Zhiping Yan |
Conducting clinical trials across multiple countries is becoming increasingly common for clinical research. SPs (Statistical Programmers) may need to produce multiple packages of TLFs (tables, listings, and figures) with different languages to meet requirements from different health authorities. For such tasks, SPs normally produce one package in English and then do the translation. It's important to maintain consistency between different packages. There are 2 options for the translation task: one is to translate all the datasets and programs and re-generate another package, the other is to translate the TLFs directly. We prefer the latter one for efficiency and consistency. Sometimes it's the only option when vendor only provides a single combined Docx or RTF file. This paper briefly discusses a SAS macro tool to automate the translation of RTF files from English to Chinese. Docx files need to be re-saved as RTF files before the translation | cross join, clinical trial, sample | source | 9 |
| 86 | How to Write a SAS Open Code Macro to Achieve Faster Updates | Frederick A. Cieri | This paper demonstrates how to write a SAS macro using mostly open code statements to make a macro easier to debug, run, and understand. A macro is not an open code item, but open code techniques can be used to simplify macro code. Instead of having to run a whole macro to get results, an open code macro can be submitted by running just one DATA STEP or procedure at a time which saves time in running code and making updates. Examples are given to convert nested macros with '%if %then' and '%do' loops code to macros using primarily open code statements. This topic would benefit any macro programmer from beginner to advanced programmer |
macro variable, character variable, numeric variable, %if-%then-%else, macro loop, data step, call execute, macro call, macro parameter, proc step, proc means, macro function, date variable, do loop, proc print, macro definition, length statement, data statement | source | 32 |
| 87 | Imputation for Missing Dosing Time in NONMEM PopPK Datasets | Shuqi Zhao | For late stage clinical trials with oral daily dosing regimen, complete dosing history data is generally not available. Following study design, patients get PK samples drawn at clinic sites, but between scheduled visits, patients are expected to take daily doses at home. Both date and time for PK samples are collected, while accurate time as to when the dose is taken for most of the doses is not available. When building NONMEM-ready PopPK datasets, however, both date and time for dosing records are required for derivation of actual relative time from first dose and actual relative time from last dose. Due to incomplete dosing history data, time imputation comes into play. Imputation methods can be different depending on study design and data collection design, but the general rule is that the relative time of dose to PK sample doesn't get changed | sample, imputation, key, elapsed time, out=, proc sql | source | 8 |
| 88 | Integrating Digital Images for Ophthalmology Trials using Machine Learning Techniques for Image Analysis. | Shubhranshu Dutta | Ophthalmology related clinical trials often involve high levels of risk due to issues concerning patient safety. With the advancement of imaging techniques, there i s an increasing abundance of patient data available for the purpose of research. For every patient, an ophthalmic visit consists of various data points co llected related to visual acuity, contrast sensitivity, ocu lar pressure, visual field tests, and retinal scans with the optic nerve. Measurements are observed and recorded in every visit while data may be processed and mapped for clinical research studies. While, the re st of the vision related parameters may be directly inputted by a physician, there is a lot of imaging data that can be standardized and made available fo r researchers. This paper would be focused on acquiri ng retinal scan data for patients, analyzing images , detecting abnormalities, and integrating them into clinical trials | machine learning, artificial intelligence, correlation | source | 13 |
| 89 | 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 |
| 90 | Intelligent Axis Scaling in SAS plots: %IAS | Sidian Liu, Toshio Kimura |
When creating graphs to visualize statistical analysis, the intuitiveness of the axis boundary and interval values can considerably impact the ease of interpretation. The default SAS plotting algorithm focuses primarily on structure where the figure is centered and covers a substantial proportion of the canvas. However, the SAS default approach may compromise interpretability. We generated figures from simulated data to compare SAS defaults and the proposed algorithm. In some cases, the SAS default axis values were outside of the axis labels being printed: for example, when the actual values range from 0.7 to 4.1, the printed tick marks by the SAS default algorithm were 1 to 4 whereas a more preferable range would have been 0 to 5. We propose an alternative intelligent axis scaling algorithm %IAS, implemented through a SAS macro that addresses this concern by focusing on the interpretability of the Y-axis | proc calis, proc sgplot | source | 9 |
| 91 | Interactive Clinical Dashboards using R Studio | Syam Prasad Chandrala, Chaitanya Chowdagam |
Data review listings are the standard way of reviewing data by Study Management Team (SMT) involving cross functional groups like clinical science, clinical operations, data managers and biostatisticians. These reports are produced in the form of pdfs, EXCEL or rtf files or sometimes combination all three file formats. Users in SMT team prefers these reports in formats such as EXCEL or pdf. Some user groups don't prefer to EXCEL since they are editable. Data Managers and clinical operations prefer EXCEL since it is easy to identify the data issues. Statistical programmer has to spend lot of time in generating these reports based on the preference of each user groups, which adds burden on statistical programmer during tight timelines. Flexdashboard package in R is a powerful tool to create these reports in the form of listings and figures and publish them in a webpage using RStudio Connect |
excel, adverse events, clinical data, demographics table, statistical programmer | source | 20 |
| 92 | Introducing and Increasing Compliance of Good Programming Habits | Alan Meier | SAS code is often shared within a task, project and organization. The better written the code is, the easier it is to understand, use, modify and validate. Improving programmers' coding habits increases the productivity of the entire team. The challenge is getting programmers to learn and use good habits. To achieve improvement, a manager first needs to define what good coding habits are. These include in- program documentation, style requirements, use of modular programming techniques, rules on hardcoding, and naming conventions, to name a few. These habits then need to become part of the organization's expectation, usually through SOPs or Work Practice documents. Most programmers have their own styles. Getting them to learn new habits can be challenging. Like any change, the manager needs to roll them out to the staff by explaining the benefits, building excitement around them, educating staff, allow time to practice and then require their usage | data step, proc step, macro variable, proc sql | source | 5 |
| 93 | IOT or IOMT? | Lekitha JK | IOT or IOMT? We have all heard about the impending era of the internet of things (IOT) where smart devices will allow our homes, our cars, our businesses and countless other attributes of daily living to be managed via a click of a mobile device. IoT has the capacity to change and simplify the process of clinical trial to be more cost -effective and efficient. Stakeholders are looking for reducing the time taken for research on new treatments and diagnostic methods while improving ways of conducting clinical trials. Implementing Electronic Clinical Outcome Assessments or eCOA can improve data collection, reduce costs and increase patient participation or retention rates. Adoption of digital health on IoT platform can create limitless opportunity for smart technology sensors and medical devices. The initial response from clinical researchers through patient experience is positive in collection of biometric data on trial subjects by using the devices that collect data more efficiently | iot, clinical trial | source | 4 |
| 94 | Laboratory Results in Relation to Concomitant Medications (CMs) | Scott Horton | In addition to reviewing laboratory (lab) data based on study treatments, clinicians are also interested in what concomitant medications (CMs) a subject might be taking or might have taken prior to such lab data. This review can be accomplished by the dates in a lab data listing and the dates in a CM listing-this type of review by its very nature is tedious. Combining lab data and CM data is complex when not limiting the CMs and their dosages being considered for such a clinical review. A recursive SAS macro will be examined that simplifies the preparation of CM data to a format that allows combining with lab data in a simple, straightforward manner. The resulting data set contains records of CMs for any given time point for each patient and allows the two types of data to be easily combined for clinical review in listings or tables | laboratory data, macro call, concomitant medications | source | 14 |
| 95 | Lazy Programmers write Self-Modifying code OR Dealing with XML file Ordinals | David B. Horvath | The XML engine within SAS is very powerful but it does convert every object into a SAS data set with generated keys to implement the parent/child relationships between these objects. Those keys (Ordinals in SAS speak) are guaranteed to be unique within a specific XML file. However, they restart at 1 with each file. When concatenating the individual tables together, those keys are no longer unique. We received an XML file with over 110 objects resulting in over 110 SAS data sets, which our internal customer wanted concatenated for multiple days. Rather than copying and pasting the code to handle this process 110+ times, and knowing that I would make mistakes along the way-and knowing that the objects would also change along the way-I created SAS code to create the SAS code to handle the XML. I consider myself a Lazy Programmer. As the classic "Real Programmers…" sheet tells us, Real Programmers are Lazy | proc step, proc contents, proc import, dataset | source | 9 |
| 96 | Leadership and Programming | Kriss Harris | The world is looking for leaders, and you can be a leader! As a Programmer or Statistician you have the power to lead and influence the clinical study, and essentially, you have the power to improve the quality of people's lives. As a leader, you need to be able to step forward and put your hand up, although this can be a challenge because as Programmers and Statisticians, a lot of us can be introverted and live in our head more, and shy away from taking the lead. The message here isn't implying that introversion is something that needs to be cured. The message here as quoted by Susan Cain is merely implying that "Introverts need to trust their gut and share their ideas as powerfully as they can | clinical programming, clinical trial | source | 6 |
| 97 | Life After Drug Approval... What Programmers Need to Know About REMS | Cara Lacson, Carol Matthews |
While most of the spotlight in the drug development process focuses on clinical trials and the effort to get drugs approved, the U.S. Food and Drug Administration (FDA) is turning to Risk Evaluation and Mitigation Strategy (REMS) as a way to approve drugs they may have safety concerns about while closely monitoring those potential safety issues once the drug is approved. A REMS often involves drugs that have a high risk for specific adverse events, and the FDA requires manufacturers to put a program in place to mitigate those risks. REMS are a growing sector of the market, and will only continue to grow in the future. Therefore, it is increasingly valuable to know the "ins and outs" of how to approach programming with REMS data, as it is very different from clinical trial programming | clinical trial, adverse events, data science | source | 11 |
| 98 | Looking for the Missing(ness) Piece | Louise S. Hadden | Reporting on missing and/or non-response data is of paramount importance when working with longitudinal surveillance, laboratory and medical record data. Reshaping the data over time to produce such statistics is a tried and true technique, but for a quick initial look at data files for problem areas, there's an easier way. This quick tip will speed up your data cleaning reconnaissance and help you find your missing(ness) piece. Additional tips on making true missingness easy to identify are included | proc step, proc freq, missing value, proc report | source | 4 |
| 99 | Machine Learning Programming Workshop - Natural Language Processing (NLP) | Kevin Lee | One of the most popular Machine Learning implementation is Natural Language Processing (NLP). NLP is a Machine Learning application orservice which are able to understand human language. Some practical implementations are speech recognition, machine translation andchatbot. Sri, Alexa and Google Home are popular applications whose technologies are based on NLP. Hands-on Training of NLP Machine Learning Programming is intended for statistical programmers and biostatisticians who want to learn howto conduct simple NLP Machine Learning projects. Hands-on NLP training will use the most popular Machine Learning program - Python. Thetraining will also use the most popular Machine Learning platform, Jupyter Notebook/Lab. During hands-on training, programmers will useactual Python codes in Jupyter notebook to run simple NLP Machine Learning projects. In the training, programmers will also get introducedpopular NLP Machine Learning packages such as keras, pytorch, nltk, BERT, spacy and others | machine learning, neural network | source | 10 |
| 100 | Macro Development from Design to Implementation Applied to Occurrence Data Frequency Analysis | Chengxin Li, Michael Pannucci, Toshio Kimura |
This paper demonstrates macro development from design to implementation detailing the macro design concept including dataset requirements, parameterization, statistical analysis, reporting and utility functions. While this paper uses occurrence data analyses as the motivating example, the concepts are generally applicable to macro development for any statistical analysis. The paper contains two main sections: 1) General macro design concepts and 2) organization and flow of macro implementation modules with specific considerations for occurrence dataset structure (OCCDS) analysis components. The macro design concept includes key decisions regarding the scope of the macro. One design concept is to leverage the analysis-ready principle thereby requiring analysis -ready datasets such as analysis data model ( ADaM) OCCDS. As such, the macro will not include data building steps. Another macro design decision is parameterization | data science, proc freq, macro parameter, system organ class, frequency analysis, macro call, proc step, in=, proc report, adam dataset, adverse events, analysis set, cdisc | source | 9 |
| 101 | Macro to Compare Titles and Footnotes in Produced TLF and Corresponding Shells | Igor Goldfarb, Ella Zelichonok |
The goal of this work is to develop a macro that automates an important part of the final review process of TLF (tables, listings and figures). Specifically, the proposed macro compares titles and footnotes which are residing in the approved shells with titles and footnotes in the corresponding actual outputs and identifies the differences. The proposed tool can significantly simplify review work for biostatisticians and lead programmers who have to verify that TLF were generated correctly according to the shells. Final review of the produced TLF represents an important task in a process of statistical programming. Comparing and making sure that the actual outputs were created strictly in accordance with the approved shells' document (typically MS Word file) is a tedious process requiring scrupulous work t hat is subject to human errors. The proposed macro (developed in EXCEL VBA) automates this process |
excel, clinical trial, safety population, statistical programmer, pharmaceutical industry, life sciences | source | 26 |
| 102 | Make Your Life Easy! How SpotFire and SAS together can help with better Data Review and Data Quality | Raj Sharma | Data quality and data review to identify data issues are both very important to ensure quality, accuracy and integrity of the data analysis in clinical studies. Often, depending on size of company and tools available, clinical data review could be very cum bersome, time consuming and manual. It leads reviewer to miss critical issues which leads to poor quality of data hence less accurate results. Spotfire along with SAS, with all these features, ensures very efficient data and timely data review. Many different functions can also use Spotfire visualization for their own benefits. For ex- SAS programmers can easily identify data issues critical for analysis or do quick informal qc on data, Clinical Scientist/Data Monitors can easily and optimally conduct data review by drilling down data using Spot fire features, Safety Science/Pharmacovigilance can easily identify safety issues with subjects and Data Managers can get a quick summary of outstanding data issues | data review, bar chart, adverse events, concomitant medications | source | 12 |
| 103 | Making Customized RTF Output with R Package r2rtf | Huei-Ling Chen, Heng Zhou, Madhusudhan Ginnaram, Yilong Zhang |
The Rich Text Format (RTF) file is a typical output file format for reporting, conventionally created with SAS procedures. However, with R's increasing popularity in the pharmaceutical industry, there is also a growing need to generate RTF files with R. Among many existing R packages to create RTF files, a newly developed open-source package, r2rtf, stands out, with easy-to-use features and powerful functions. It allows R users to produce various RTF outputs effortlessly. This paper uses three examples to demonstrate different methods of creating RTF tables using this package. We have papers presented in PharmaSUG 2019 (https://www.lexjansen.com/pharmasug/2019/ST/PharmaSUG-2019-ST-149.pdf) and 2020 (https://www.pharmasug.org/proceedings/2020/DV/PharmaSUG-2020-DV-135.pdf) that showed two of these same outputs produced using SAS macros | treatment arm, dose, group by, adverse events, sample, ods rtf, proc print, laboratory data, r package | source | 12 |
| 104 | Making Your SAS Results More Meaningful with Color | Kirk Paul Lafler | Color can help make your SAS results more meaningful. Instead of producing boring and ineffective results, users are able to enhance the appearance of their output, documents, reports, tables, charts, statistics, and spreadsheets to highlight and draw attention to important data elements, details, and issues, including using color in headings, subheadings, footers, minimum and maximum values, ranges, outliers, special conditions, and other elements. Color can be added to text, foreground, background, rows, columns, cells, summaries, totals, and traffic lighting scenarios. Topics include using color to results, documents, reports, tables, charts and spreadsheets can be enhanced with color, effectively add color to PDF, RTF, HTML, and EXCEL spreadsheet results using PROC PRINT, PROC FREQ, PROC REPORT, PROC TABULATE, and PROC SGPLOT and Output Delivery System (ODS) with style |
bar chart, proc sgplot, proc step, histogram, proc sgpanel, excel, scatter plot, vertical bar chart, box plot, bubble plot, proc freq, series plot, proc report, data science, class variable, correlation, outlier | source | 19 |
| 105 | Manage SAS Log in Clinical Programming | Yuping Wu | This paper introduces a method to manage SAS log in clinical programming. It is conducted by three macros. The first macro is placed at the beginning of the program. It automatically detects the type of the program that is being run and specifies the path to store the logs based on the program type. The second macro is placed at the end of the program. It prints the logs and lists the log issues in the current log window. Therefore, programmers can know the log status instantly when they are developing or running the individual program. The third macro is used to check log issues in batch. It scans stored log files of the selected processes such as SDTM or ADaM or Table, Figure and Listing (TFL) or all of them, and produces a status report that highlights the programs with log issues. This method presents an automatic way to manage SAS log and allows programmers to easily detect and fix log issues in current log window and identify programs with log issues in batch report | sas log, macro parameter, proc compare, proc step, clinical programming, data cleaning, proc printto, cdisc | source | 4 |
| 106 | Manage TFLs Development in LSAF 5.3 using SAS and R code | Stijn Rogiers, Jean-Marc Ferran |
TFL development and validation is often a labor-intensive task during the conduct of clinical trials . Collaboration between multiple programmers across the globe is often needed while oversight of progress can be difficult to tackle. Life Science Analytics Framework (LSAF) 5.3 enables users to manage and execute both SAS and R programs in the same flexible and open platform. In addition, LSAF provides integrated features around versioning, eSignature and use of Workflows. These features coupled with an advanced API enable users to customize and streamline the definition and production of TFLs across studies and projects. This presentation will feature, based on a prototype, how to specify TFLs, follow their development lifecycle, validation status using both SAS and R programs and keep control over development activities during the course of a study | data cleaning, life sciences | source | 10 |
| 107 | Management of Metadata and Documentation When Your Data Base Structure is Fluid: What to Do if Your Data Dictionary has Varying Number of Variables | Louise S. Hadden | A data dictionary for a file based on Electronic Medical Records (EMR) contains variables which represent an unknown number of COVID-19 tests for an unknown number of infants - there is no way to know in advance how many iterations of the COVID test variable will exist in the actual data file from medical entities. In addition, variables in this file may exist for three different groups (pregnant women, postpartum women, and infants), with PR, PP and IN prefixes, respectively. This presentation demonstrates how to process such variables in a data dictionary to drive label (and value label) description creation for iterated (and other) labels using SAS functions, as well as other utilities | data dictionary, label statement, sample, character functions | source | 7 |
| 108 | Managing Transitions so Your Life is Easier | Kirsty R. Lauderdale | Transitions can be a very troublesome time for everyone involved; managers, programmers and clients. It seems like a very simple thing to manage, however people still struggle with programming transitions, leaving a huge knowledge gap when a programmer departs the team. Effectively managing the information flow during a transition makes life much simpler, for both the transitioner and the transitionee. The most common programming complaint from programmers taking over code is that there are chunks of code that do things that we aren't quite sure they should be, and sometimes code that we have no idea what it is actually doing. In this paper, we will lay out an effective transition plan, which can be applied to single program codes, full studies, and used across multiple roles - management, programming and statistics | data cleaning, audit trail | source | 7 |
| 109 | Mashing Two Datasets Together | David W. Franklin | The MERGE statement is the most common way to merge one-to-one or one-to-many data. This works very well most of the time but there are other methods that are useful, and sometime more efficient, that should be every SAS programmers toolbox. This paper touches on four methods that can be more efficient; quick look at PROC SQL and some of the options that help, HASH tables and some of the considerations for using this format, PROC FORMAT, and the KEY= option in the SET statement |
proc step, proc sql, merge statement, proc sort, set statement, hash table, proc format, index creation, key= | source | 5 |
| 110 | Migration of SAS Data from Unix server to Linux server | Hengwei Liu | When a company decided to migrate SAS data from Solaris Unix server to Red Hat Enterprise Linux (RHEL) server, one issue was how to deal with the formats catalog files. The formats catalog files are not platform independent. If they are copied from Unix server to Linux server, they will not be of any use in the Linux server. On the other hand, SAS datasets can be used across different platforms. It was decided to convert the formats catalog files to SAS datasets in the Unix server, migrate those SAS datasets to Linux server, and convert them back to formats catalog files. In this paper we discuss the data migration project. Unix command and SAS macros are used in the project | dataset, proc format, macro definition, proc step, red hat | source | 4 |
| 111 | Mitigating Multicollinearity: Using Regulation Techniques to Minimize the Effect of Collinearity | Deanna Naomi Schreiber-Gregory | 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 training, we will explore how to detect multicollinearity, and once 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 training is intended for any level of SAS user. This training is designed for 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, response variable, proc reg, feature selection, estimate, proc glmselect, correlation, bootstrap, proc calis, sample, quadratic programming, proc univariate, logistic distribution, condition index, proc corr | source | 23 |
| 112 | Multiple Successful Submissions of Medical Device Clinical Trial Data in the US and China using CDISC | Phil Hall, Tikiri B. Karunasundera, Subendra Maharjan, Vijaya Vegesna |
It is not yet mandatory for medical device trial data to be submitted using CDISC but The Center for Devices and Radiological Health (CDRH) accepts clinical trial data in any format, including CDISC. This paper serves as a case-study of the successful regulatory submissions of four Edwards Lifesciences' trials, three in the US and one in China, which utilized SDTMS and ADaMs. There will be a review of the SDTM domains used for medical device-specific data and a general discussion of the submission approaches | clinical trial, adverse events, clinical data, sdtm domain, sdtmig, cdisc | source | 10 |
| 113 | Neoadjuvant and Adjuvant Oncology Clinical Trials and Considerations for Designing the Subject Level Analysis Dataset (ADSL) | Madhusudhan Reddy Papasani, Swathi Kotla, Elisabeth Pyle |
Current ADaM classes (ADSL, BDS, OCCDS) support most cases of analysis datasets involving various study designs. However, the implementation of ADaM standards in complex study designs such as neoadjuvant and adjuvant oncology clinical trials can be challenging. Because of different treatments in different periods, and possible period baseline definitions and treatment emergent flags, one must carefully design Subject Level Analysis Dataset (ADSL) and other analysis datasets with a comprehensive understanding of analysis needs with the right interpretation of ADaM standards. In this paper, we focus on ADSL design with an emphasis on using the right phasing, period, sub- period and treatment variables for the needs of a neoadjuvant and adjuvant study. Further ADSL metadata describes the derivations and data origins for the variables | treatment period, adam dataset, cdisc | source | 9 |
| 114 | New Requirements of Clinical Trial Data Submission for China Filing: An Implementation | Yuan Yuan Dong, Wang Zhang, Lily Ling |
The National Medical Products Administration (NMPA) has released the Guideline on the submission of clinical trial data on October 1st, 2020, which demonstrated the specific requirements of data submission to agency in China filings. This has brought new opportunities and challenges to all sponsors. This paper introduces the new requirements of China data submission guideline, as well as two types of execution process which were used in recent pilot filings as implementations to the guideline. Moreover, the paper summarizes the benefits and issues that we encountered during the preparation of submission packages | clinical trial, clinical data, label statement, adam dataset, data cleaning, cdisc | source | 5 |
| 115 | NHANES Dietary Supplement component: a parallel programming project | Jayanth Iyengar | The National Health and Nutrition Examination Survey (NHANES) contains many sections and components which report on and assess the nation's health status. A team of IT specialists and computer systems analysts handle data processing, quality control, and quality assurance for the survey. The most complex section of NHANES is dietary supplements, from which five publicly released data sets are derived. Because of its complexity, the Dietary Supplements section is assigned to two SAS programmers who are responsible for completing the project independently. This paper reviews the process for producing the Dietary Supplements section of NHANES, a parallel programming project, | dataset, proc step, proc sql, sample, create table, proc compare, quality assurance, if-then-else | source | 20 |
| 116 | No Black Boxes: An Utterly Transparent Batch Randomization System in R | Dennis Sweitzer | Randomized trials are a key part of clinical research, but are often implemented as somewhat of an after thought. In order to accelerate the delivery of high quality randomization schedules, we implement a batch stratified permuted block randomization system which includes: A flexible spreadsheet based syntax to structure the randomization as a series of 'superblocks' within each strata, cohort, or combination, easing the use of best practices such as variable block sizes, and relaxed or mixed permuted block methods; Complete computational transparency, with an audit trail from parameters and random numbers through block selection and unit assignments, and implemented in open-source R code; Simulations by default for testing, validation, or rerandomization analyses using pseudo-random number streams generated by the Wichmann-Hill 2006 algorithm for repeatable and independent parallel random number sequences; Reporting functions to measure quality of generated schedules on metrics such | randomization, sample, audit trail, clinical trial, phase i, data cleaning, excel | source | 12 |
| 117 | NOBS for Noobs | David B. Horvath | This mini-paper (and the resulting short session) is a focused discussion of the NOBS (number of observations) option on the SET statement. This includes one "gotcha" that I've run into with where clauses: NOBS is set before WHERE processing. If you have a reason to know the number of observations after the WHERE clause, another DATA STEP is needed |
where statement, set statement, proc step | source | 4 |
| 118 | Only Get What You Need - To Simplify Analysis Data Validation Report from PROC COMPARE Output | Wenjun He | Independent programming is a gold standard validation method to generate accurate analysis data sets in biotechnology and pharmaceutical companies. A timely and efficient validation of derivative analysis datasets from dynamic source data is demanding for clinical programmers in phase I trials. SAS software provides PROC COMPARE as a useful tool to identify differences between two data sets. In order to efficiently keep monitoring the validation status of the whole set of a study trial specific analysis datasets which are constantly updated to match the dynamic source data, this paper introduces the techniques to speed up identifying the discrepancies between each pair of production and validation datasets in various SAS libraries and simplify the generation of a straightforward validation report by extracting and reorganizing the output from PROC COMPARE |
data cleaning, proc compare, proc step, sas library, adam dataset, macro variable, raw data, proc sql, independent qc | source | 12 |
| 119 | Open-Source Development for Traditional Clinical Reporting | Michael Stackhouse, Nathan Kosiba |
As the pharmaceutical industry continues to embrace open-source solutions, gaps between the open- source ecosystem and the requirements of traditional clinical reporting pipelines continue to present themselves. These gaps are understandable, as regulatory requirements and decades of tradition cannot match up to the speed and freedom of software designed by consensus and open for review. Furthermore, with open-source as a novel concept within clinical reporting, contributors and consumers within this arena are sparse. Therefore, open-source solutions are not currently tailored to the specific needs of the clinical reporting pipeline. The way to close these gaps is not to wait or rely on the open - source contributors outside industry to close them for us, but rather to close them ourselves | length statement, clinical programming | source | 6 |
| 120 | Overcoming the Challenge of SAS Numeric Date Comparisons | Shikha Sreshtha | The outputs from the PROC COMPARE are tedious to work with especially when handling SAS numeric dates in huge datasets. The SAS dates are essentially numbers, that is, the number of days between 01JAN1960 to the input date and with the FORMAT statement, the date variables are only displayed in one of the standard date formats. When comparing numeric dates in clinical formats of standard e8601da10. using PROC COMPARE, all we see are frustrating little stars in the list file. To the rescue, are these huge array of options that the COMPARE procedure comes with. When faced with record-SAS date differences, most programmers turn to temporary data sub-setting and patches of code to determine the key variables to locate where the issue lies. All this effort to identify the one tiny needle in stack of hay and inform the 'base' programmer of the intended variable value |
proc compare, sysdate9, date variable, data cleaning, out=, proc step | source | 7 |
| 121 | Overlay Graphs: A Powerful Tool to Visualize and Simplify Complex Data | Manasa Gangula | SAS provides an extensive set of graphs for different needs and overlaying these graphs can be powerful in visualizing complex data. By overlaying graphs, we can display the relationship among multiple parameters in a single snapshot so the reviewer will get an explicit idea about the data without any further processing. In this paper, we will be looking at different ways to create an overlay of a vertical bar chart with scatter plot and bubble plot using sample data. We will be using Graph template language (GTL) and SAS/GRAPH procedures to achieve the result |
vertical bar chart, bar chart, proc step, bubble plot, proc sgpanel, proc sgplot, scatter plot, proc freq | source | 7 |
| 122 | Patient Registries - New Gold Standard for Real World Data | Neha Srivastava, Lavanya Peddibhotla, Sivasankar Konda |
Patient registries have been evolving in the last few years as our quest for Real World Data (RWD) is increasing. The current global movement towards innovative and patient-centered healthcare is enabling patient registries to increasingly emerge as valuable tool not just for collecting Real World Data but also for making meaningful inferences and insights from the collected data. In the last few years, The Food and Drug Administration (FDA), European Medicines Agency (EMA) and other regulatory bodies have initiated several frameworks to highlight the importance of patient registries. In Sep, 2020 EMA has issued a draft guidance for considerations on patient registries. The paper highlights some key points from the guidance. Patient Registries are highly recommended to determine the effectiveness of the investigational product by exploring various subgroup of patients for extensive time duration. It also helps in assessing the safety or harm caused by the approved drug | population, adverse events | source | 5 |
| 123 | Policy Frameworks for Submitting Study Data to FDA | Helena Sviglin | data governance | source | 14 | |
| 124 | Preserving the Privacy of Participant's Data by Data Anonymization | Chaithanya Velupam, Kaushik Sundaram |
While the global landscape of clinical data sharing has become prominent day by day, it is challenging to store, mine, and analyze heterogeneous data across multiple data sources and multiple regions. Proactive sharing of clinical trial data has been a key strategic aim since last few years. Data must be shared in such a way as to ensure the protection of participant's privacy. Whilst this is the foremost priority in any data sharing exercise, the changing technology is increasingly disabling the ability to share data in enough depth and detail. We explore how anonymization of internationally sourced clinical trial data may be achieved while maintaining the scientific utility of the data. We focus on anonymization, which plays an important role for the re-use of clinical data and for the sharing of research data | clinical data, clinical trial, data masking, response variable, outlier, concomitant medications | source | 10 |
| 125 | PROC FUTURE PROOF v1.1 - Linked Data | Amy Gillespie, Susan Kramlik, Suhas R. Sanjee, Jindřich Mynarz, Danfeng Fu |
As critical contributors to regulatory submissions, manuscripts, and statistical analyses, clinical trial programmers author programming code and leverage programming standards to produce deliverables in a validated, efficient, and reproducible manner. With the function having remained relatively constant for more than 20 years, there are potential opportunities and a need to transform the clinical trial programm ing role for continued success. Last year we published a paper that evaluated recent advances in technology and the clinical trial programming skillset to identify opportunities for improved programming efficiencies and compliance to regulatory requirements while ultimately optimizing the programming function. Use cases leveraging natural language processing and linked data were explored to determine potential value these digital technologies could add within clinical trial programming processes | proof of concept, clinical trial, adam dataset, sdtm domain, link analysis, dataset, csr, raw data, data lineage, cdisc | source | 12 |
| 126 | PROC IMPORT and more. Or: when PROC IMPORT just doesn't do the job | David B. Horvath | PROC IMPORT comes in handy when quickly trying to load a CSV or similar file. But it does have limitations. Unfortunately, I've run into those limitations and had to work around them. This session will discuss the original CSV specification (early 1980's), how Microsoft EXCEL violates that specification, how SAS PROC IMPORT does not follow that specification, and the issues that can result. Simple UNIX tools will be described that can be used to ensure that data hilarities do not occur due to CSV issues. Recommendations will be made to get around some of PROC IMPORT limitations (like field naming, data type determination, limitation in number of fields, separator in data). CSV, TAB, and DLM types will be discussed |
proc import, proc step, dataset, excel, macro variable | source | 8 |
| 127 | Productionalizing Shiny Deployments | Philip Bowsher, Sean Lopp |
Shiny is a powerful tool in the pharmaceutical spaceas it can help modernize many legacy processes andreportingworkflows. This paper will highlight many new toolsthat can help harden (productionalize) shiny apps.The audiencefor this paper are those who have wondered how toapproach production Shiny deployments. Using Shinyforsubmissions is a related topic but will not be thefocus of this paper. The R in Pharma gathering hashad related talkslike Joe Cheng's keynote here:https://speakerdeck.com/jcheng5/using-shiny-responsibly-in-pharmaThe examples for this paper are housed at the linkbelow:https://github.com/philbowsher/Advanced-Shiny-Deployments INTRODUCTIONFor many, it is a happy accident when a Shiny appbecomes popular. As the consumption increases, corporateIToften requires that additional steps be taken to putthe app into production | statistical programmer, clinical research | source | 6 |
| 128 | Programming Time Varying Concomitant Medication Covariates in NONMEM PopPK Dataset | Dan Xiao, Jing Su, Shuqi Zhao, Xiang Liu |
Concomitant medication (ConMed) covariates are very important time dependent variables for population PK (PopPK) modeling. Since concomitant medication may change the absorption, distribution, metabolism and excretion of the investigated medicine, it is required to construct the concomitant medication covariates and show the usages of ConMed before or on the dosing day. In this paper, we introduce three different strategies to program the time varying ConMed covariates into NONMEM PopPK dataset | concomitant medications, out=, retain | source | 11 |
| 129 | R & PYTHON FOR DRUG DEVELOPMENT | Philip Bowsher, Sean Lopp |
structured data, data lake, data transformation, proc sql | source | 117 | |
| 130 | R Reproducibility Best Practices for Pharma | Philip Bowsher, Sean Lopp |
Reproducibility of data analyses is essential in clinical work, but achieving it can be challenging in anytoolor language. Moreover, reproducibility is a spectrum,with greater degrees of reproducibility requiringmore investment from users. As these efforts advance, organizational, IT and user investments arecritical. This paper will introduce some simple toolsthat can be used with clinical R workflows to helpestablish a foundation. The audience for this paperare those that are new to reproducibility in R andwantto take the first steps towards reproducibility oftheir work. This paper will not discuss IT toolslike Dockerthat are also important in this space | statistical programmer, data science | source | 4 |
| 131 | R Validation: Approaches and Considerations | Philip Bowsher, Sean Lopp |
Recent R validation efforts in the clinical trialsprocess have brought a voice to pharma that is newandexciting. This paper will highlight some of theseefforts and discuss approaches that are arising forclinicalworkflows | data cleaning, clinical trial | source | 3 |
| 132 | R-Shiny based customized data viewer and search engine | Hrideep Antony, Aman Bahl |
The number one reason why the Google search engine is most popular among all search engines is its ability to deliver millions of results in less than 0.19th of a second. In other words, users can view relevant information almost on a real-time basis. However, when it comes to the study data, the database viewers that are regularly used do not facilitate the real-time search that produces results interactively, as and when the users search for a keyword within the data. This paper introduces an R-Shiny application that not only creates the results dynamically but also facilitates search at both database and variable level. The application allows the user to select the variables that need to be displayed. It also gives the user control over the number of records to be displayed beside the ability to search the data as shown in figure 1 below. This search application supports multiple source formats such as .CSV, .xls, .sas7bdat, etc |
proc means, input statement | source | 5 |
| 133 | Random Forest Machine Learning Application using R Shiny for Project Timeline Forecasting | Hrideep Antony, Aman Bahl |
Machine learning models can be used to build powerful tools to facilitate decision -making, especially when the outcome is dependent on numerous and varying factors. This paper introduces an R -Shiny based application called "Project Success Forecast' that can predict the possibility of a project's success in a clinical trial environment, using a random forest machine-learning algorithm. This application serves as a very robust AGILE planning tool to evaluate the project risk on an ongoing basis based on the historical data | proc hpforest, proc hpsplit, machine learning, sample, training set | source | 8 |
| 134 | Re-pagination of ODS RTF Outputs to Automate Page Breaks and Minimize Splits Across Pages | Jeremy Gratt, Aditya Tella |
One of the more tedious tasks of clinical study report table creation involves planning out page breaks for RTF outputs. To improve the review of table outputs, there are groups of rows we would like to keep together to avoid splitting across pages. For presentation of individual parameters our goal is to group statistics or categories within a parameter on the same page; for by-visit displays we group rows by visit or by visit/parameter; and for multi-level categorical displays such as AEs and conmeds we try to minimize the splits within hierarchical tiers. Avoiding these inconvenient splits requires manual work on the part of programmers to generate organized outputs that aid communication. An automated approach to page breaks would relieve programmers of this manual task and improve the quality of outputs | ods rtf, proc report, proc step, data step | source | 14 |
| 135 | Reduce Review Time: Using VB to Catch Spelling Errors & Blank Pages in TLFs | Corey Evans, Ting Su, Qingshi Zhou |
Checking blank pages and spelling errors is usually time-consuming and monotonous. We developed a macro using VB embedded in SAS that will assist with checking for unwanted blank pages generated from SAS ODS procedures. With one click, the macro will run and return an EXCEL file with the desired results populated for all the outputs under one folder. Also, the utility can help to check spelling errors and populate unique errors/counts in the EXCEL file |
excel, data cleaning | source | 4 |
| 136 | Restriction of Macro Variable Length - Dynamic Approach to Overcome | Kalyani Telu | Macro variables are one of the most powerful tools available in SAS with the maximum length of 65534 characters. Programmers sometimes face the situation to store data larger than this allotted limit, and it is difficult to overcome. This paper discusses the scenarios where the user needs to store large data and dynamic approaches to circumnavigate the maximum limitation | macro variable, txt, proc sql, proc step | source | 4 |
| 137 | Running Parts of a SAS Program while Preserving the Entire Program | Stephen B. Sloan | The Challenge: We have long SAS programs that accomplish a number of different objectives. We often only want to run parts of the programs while preserving the entire programs for documentation or future use. Some of the reasons for selectively running parts of a program are: Part of it has run already and the program timed out or encountered an unexpected error. It takes a long time to run so we don't want to re-run the parts that ran successfully. We don't want to recreate data sets that were already created. This can take a considerable amount of time and resources and can also occupy additional space while the data sets are being created. We only need some of the results from the program currently, but we want to preserve the entire program. We want to test new scenarios that only require subsets of the program | proc step, run, macro variable, sas enterprise guide | source | 4 |
| 138 | SAS Macro Efficiencies | Charu Shankar | macro definition | source | 154 | |
| 139 | SAS Proc Mixed: A Statistical Programmer-s Best Friend in QoL Analyses | Janaki Devi Manthena, Varsha Korrapati, Chiyu Zhang |
SAS PROC MIXED is a powerful procedure that can be used to efficiently and comprehensively analyze longitudinal data such as many patient-reported outcomes (PRO) measurements overtime, especially when missing data are prevalent. This paper illustrates the commonly used statements and options in this procedure when used in such analyses. We will present a statistical programmer's perspective on how to calculate Least Square (LS) Mean, Standard Error, difference in LS Means between treatment arms, and corresponding 95% confidence interval at each time point using this procedure. This will be demonstrated using examples of PROC MIXED focusing on both linear mixed models and pattern mixture models on imputed and original QLQ-C30 questionnaire data, respectively. Keywords: PROC MIXED, Lsmeans, Standard Error, Lsmean Difference, Confidence Intervals, p-value, Change from baseline |
proc mixed, proc step, proc calis, covariance, change from baseline, class variable, covariance structure, response variable, repeated measures, confidence interval, restricted maximum likelihood | source | 9 |
| 140 | SEND for CBER | Lisa Lin | proof of concept, clinical trial, controlled terminology, csv, data validation rules, data standard, reviewer guide, cdisc | source | 26 | |
| 141 | Shiny App for Generating Data Dependency Flowchart in ADRG | Simiao Ye, Yilong Zhang |
Analysis Data Reviewer's Guide (ADRG), which provides agency reviewers with context for analysis datasets and terminology, is an important part of a standards-compliant analysis data submission for clinical trials. Within the ADRG, the data dependencies section is used to describe any dependencies among analysis datasets, where a flowchart (diagram) is recommended to demonstrate the relationship concisely and intuitively. Shiny is an open-source R package for building web applications straight from R. This paper discusses how to utilize Shiny to automatically generate a data dependency flowchart through reading in ADaM define.xml and adapting user-input dataflows. The Shiny app simplifies manual steps to create the analysis datasets flowchart and ensures the flowchart matches with define.xml. Implementation of the app, relevant package/function details, and examples are provided in this paper | adam dataset, clinical research, cdisc | source | 8 |
| 142 | Shiny decisions - An R-shiny application for decision frameworks | Lasse Nielsen | Taking a drug from development to market is a costly and time-consuming activity for all pharmaceutical companies. Trying with a drug that will not go into market can hence cost billions before it is cancelled. We have developed a R-shiny tool at Novo Nordisk which is built upon the framework developed by Frewer et. al. . This framework is based upon the efficacy seen in literature and/or clinical trials beforehand. This is done by establishing a lower limit of clinical relevance and a value for what clinic al relevance that would like to be reached. Then provided the standard deviation of a clinical study, a probability of a stop and a go decision can be calculated using a selected distribution. The tool we have derived is a very user-friendly shiny application that takes this framework and produce both figures and tables. Together with this it takes multiple inputs to tailor to the users needs and updates it in real time | binomial distribution, clinical significance | source | 6 |
| 143 | Should I Wear Pants in the Portuguese Expanse? Automating Business Rules and Decision Rules Through Reusable Decision Table Data Structures that Leverage SAS Arrays | Troy Martin Hughes, Louise S. Hadden |
Decision tables operationalize one or more contingencies and the respective actions that should be taken when contingencies are true. Decision tables capture conditional logic in dynamic control tables rather than hardcoded programs, facilitating maintenance and modification of the business rules and decision rules they contain-without the necessity to modify the underlying code (that interprets and operationalizes the decision tables). This text introduces a flexible, data -driven SAS macro that ingests decision tables - maintained as comma -separated values (CSV) files -into SAS to dynamically write conditional logic statements that can subsequently be applied to SAS data sets. This metaprogramming technique relies on SAS temporary ar rays that can accommodate limitless contingency groups and contingencies of any content |
if-then-else, macro variable, array, predictor variable | source | 13 |
| 144 | Solution when Global Pandemic resurrects paper-based PROs creating collection headaches for Data Managers | Terek J. Peterson, Hayley Jeffery, Justin Jaeschke |
Unforeseen COVID-19, country wide lockdowns and site closures impacted data collection processes, shifting eCOA data collection to paper assessments. This paper explores the challenges of processing urgent paper assessments, estimated at 6 months to complete. Using dynamic worksheets, integrated with each study's SQL database, allowed accurate and efficient data entry. eCOA systems employ parent/child relationship database models, allowing dynamic question entry in logical order. The solution mirrored this approach for paper, maintaining data integrity and providing a logical/simple tool for Data Management. Dropdown selection boxes dynamically updated on selected choices, permitting only the correct options. Translations were implemented, applying the applicable language to reduce inaccuracies. Current processes leveraged existing verification step prior to committing, creating staging tables which were validated by another individual for accuracy | data integrity, proc sql | source | 6 |
| 145 | Standardizing Laboratory Data From Diverse RWD to Enable Meaningful Assessments of Drug Safety and Effectiveness | Irene Cosmatos, Michael Bulgrien |
The highly granular clinical details in electronic medical record (EMR) data are increasingly critical for active safety surveillance of marketed drugs. Many of the Food and Drug Administration's (FDA) Sentinel's drug safety queries were unsuccessful because administrative claims data were 'insufficient' due to lack of laboratory results. The study's objective was to standardize laboratory data from the United States (US) and non- US EMR databases into a common structure to enable active safety surveillance analyses to be performed and compared across data sources that do not use a standardized coding system such as Logical Observation Identifiers, Names, and Codes (LOINC). UBC's database analysts and clinicians developed an approach to transform source data into a cohesive and accurate dataset of laboratory results based on standardized units and test names, while minimizing loss of data | laboratory data, lookup | source | 11 |
| 146 | Submitting Scientific Data from Different Sources | Fang Li | source | 18 | ||
| 147 | Super Static SG Graphs | Kriss Harris | Would you like to know how to utilize ODS objects to produce graphs just the way you want them? Such as with coloring and annotation? This submission will show you how to create the graphs you want through using a Kaplan-Meier example |
kaplan-meier plot, hazard ratio, macro variable, median survival, proc step | source | 7 |
| 148 | Survival Statistics with PROC LIFETEST and PROC PHREG: Pitfall-Avoiding Survival Lessons for Programmers | Yun (Julie) Zhuo | Survival statistics play a critical role in the analysis of efficacy in clinical trials. In SAS, the LIFETEST procedure compares the survivor function between study arms, and the PHREG procedure estimates the effect of study treatments on hazard rates. This paper shares the lessons we have learned from programming survival analysis with SAS for multiple sponsor clients. Topics vary from P-values, handling warning messages, to some life-saving SAS options. The goal is to point programmers to potential pitfalls and show them ways around it. Geared towards programmers, this paper does not intend to explain any statistical underpinnings of the analysis |
proc phreg, hazard ratio, clinical trial, confidence interval, survival analysis, sample, proc step, proc means, treatment arm, predictor variable, median survival, right censoring, randomization, regression analysis, proc freq | source | 10 |
| 149 | The SASSY System: Making R Easier for SAS Programmers | David J. Bosak | The sassy system is an R meta-package that make R easier for programmers whose primary experience is with SAS software. These functions provide the ability to create data libraries, format catalogs, data dictionaries, and a traceable log. The system includes a datastep function that recreates the most basic functionality of the SAS DATA STEP. The package also includes reporting capabilities reminiscent of those found in SAS, and can output reports in text, rich-text, and PDF file formats. All combined, these functions allow you to write programs with an overall flow and thought-process that is more similar to a SAS program. This paper will provide a brief overview of the system |
data step, macro put, sample, if-then-else, libname statement, user-defined format, proc freq | source | 14 |
| 150 | The Tortoise and the Hare - Lessons Learned in Developing ADaM Training | Alyssa M. Wittle | A challenge: increase the number of outstanding ADaM spec writers and programmers. The reasons are obvious for why this is an essential part of our business. More outstanding ADaM programmers and spec writers mean less QC issues, more time & money saved, less Pinnacle21 findings, less time in review cycles, and more. Plus, outstanding programmers tend to make outstanding spec writers and eventually become subject matter experts who provide invaluable education and information to the rest of a team. There are a few routes to take in determining the most effective approach with the highest level of effectiveness. About a year into the redesign of our ADaM Mentoring Program, a few strategies have been applied and shown to have success. This paper will discuss the options considered, the strategy for picking candidates for training, and the key training aspects which have shown improvement and positive feedback from every trainee who has gone through the program | adam programmer, imputation, cdisc | source | 6 |
| 151 | Thwarting Nasties | David W. Franklin | When starting any program, some form of planning is involved, then the actual writing of the code, then finally the checks to see that it producing what was asked. This paper takes a brief and lighthearted look at each of these stages, providing a few tips for avoiding some of the many pitfalls and gives a few pieces of SAS code that are useful in development of your program, hopefully avoiding having to say that dreaded phrase, "I have to write it from scratch!" | proc step, adverse events, macro definition | source | 12 |
| 152 | Twenty Ways to Run Your SAS Program Faster and Use Less Space | Stephen B. Sloan | When we run SAS programs that use large amounts of data or have complicated algorithms, we often are frustrated by the amount of time it takes for the programs to run and by the large amount of space required for the program to run to completion. Even experienced SAS programmers sometimes run into this situation, perhaps through the need to produce results quickly, through a change in the data source, through inheriting someone else's programs, or for some other reason. This paper outlines twenty techniques that can reduce the time and space required for a program without requiring an extended period of time for the modifications. The twenty techniques are a mixture of space-saving and time- saving techniques, and many are a combination of the two approaches. They do not require advanced knowledge of SAS, only a reasonable familiarity with Base SAS and a willingness to delve into the details of the programs | proc step, dataset, proc sort, numeric variable, proc means, by group processing, character variable | source | 4 |
| 153 | Types of Devices in Therapeutic Area User Guides | Carey G. Smoak | Thirty-seven unique Therapeutic Area User Guides (TAUGs) have been published as of 2020. Thirty of these TAUGs either have examples of device data or mention device data. Of the twenty -three TAUGs which have examples of medical device data, there are twenty-one types of device data in these TAUGs. The most frequent types of device data are imaging, diagnostic tests and lab tests. These examples of device data would be useful in the next Medical Device SDTM Implementation Guide (SDTMIG -MD), particularly for ancillary devices used in pharmaceutical studies. Better examples of device data from medical device companies is needed from medical device companies for the next SDTMIG -MD. It is also suggested that reference documents for imaging, diagnostic tests and lab tests be developed to provide better consistency of presentation of device data in the TAUGs | sdtmig, sdtm domain, cdisc | source | 7 |
| 154 | Under the Hood: The Mechanics of Various SQL Query Optimization Techniques | Kirk Paul Lafler | The SAS software and SQL procedure provide powerful features and options for users to gain a better understanding of what's taking place during query processing. This presentation explores the fully supported SAS MSGLEVEL=I system option and PROC SQL _METHOD option to display valuable informational messages on the SAS Log about the SQL optimizer's execution plan as it relates to processing SQL queries; along with an assortment of query optimization techniques |
proc sql, proc step, sas log, options statement, where statement, index, select, cpu time, error message, sas library, data merging | source | 9 |
| 155 | Understanding Administrative Healthcare Datasets using SAS programming tools | Jayanth Iyengar | Changes in the healthcare industry have highlighted the importance of healthcare data. The volume of healthcare data collected by healthcare institutions, such as providers and insurance companies is massive and growing exponentially. SAS programmers need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. There are various types and sources of administrative healthcare data, which include Healthcare Claims (Medicare, Commercial Insurance, & Pharmacy), Hospital Inpatient, and Hospital Outpatient. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. The workshop will engage attendees with a series of SAS exercises involving healthcare datasets | proc step, proc freq, sas programmer, proc sort, proc tabulate, insurer, health insurance, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc contents, proc report, sas programming, by group processing, dataset, proc means, lookup | source | 24 |
| 156 | Unnoticed Individual and Mean Line Plot Issue While Using the SGPLOT Procedure, and the Solution to Avoid | Jagadish Katam | This paper talks about the issue with overlaying time series for individuals and mean values in a single graph using PROC SGPLOT and the solution to it. Using the SGPLOT procedure to develop the overlaying time series plot, the individual as well as mean values lines in graph will not terminate to the end of X-axis. So, they will appear as unending lines circling back. This paper will have the images of the plots through which the issue will be detailed. In addition to this, I will talk about the changes that a dataset needs before passing into SGPLOT procedure and other required steps. This is an unnoticed issue in the SGPLOT procedure and the solution to it will help the peers to pay more attention while working on the individual/mean line plots |
line chart, proc sgplot, proc step | source | 10 |
| 157 | Untangling the Knot - Implementing Analysis Results Standards (Using SAS) | Hansjörg Frenzel | In his 2019 PhUSE paper1 Chris Decker suggested moving from traditional TFL programming to generating analysis results data. Analysis results standards, which define how results data are stored as well as the associated metadata, support the separation of results generation from results presentation. Some of the benefits include avoiding to create results multiple times for multiple outputs, better documentation, traceability to the underlying data, reusability, and potential for automation when creating outputs for multiple stakeholders. This paper attempts to present the metadata for a possible analysis results standard and to show how they could be used for the automated creation of tables | adverse events, data science | source | 10 |
| 158 | Updates from the Office of Biostatistics Analytics and Informatics Staff: Initiatives and Projects | Matilde Kam, Xiaofeng (Tina) Wang |
source | 30 | ||
| 159 | Using PROC FCMP to Create Custom Functions in SAS | Keith Shusterman, Mario Widel |
PROC FCMP is a powerful but somehow underused tool that can be leveraged for creating custom functions. While macros are of vital importance to SAS programming, there are many situations where creating a custom function can be more desirable than using a macro. Particularly, custom functions created through PROC FCMP can be used in other SAS procedures such as PROC SQL, PROC PRINT, or PROC FREQ. As an alternate approach to SAS macros, PROC FCMP can be used to create custom functions that process character and numeric date and DATETIME values. In this presentation, we will share two custom functions created using PROC FCMP. The first creates ISO 8601 character values from raw date inputs, and the second creates numeric SAS dates and datetimes from ISO 8601 inputs. We will also show how these newly created custom functions can be called in other SAS procedures |
proc step, proc fcmp, proc freq, out=, proc print, imputation, sas function, proc sql, where statement, macro definition, work library, date variable | source | 12 |
| 160 | Using Regex to Parse Attribute-Value Pairs in a Macro Variable | Rowland Hale | In some circumstances it may be deemed preferable to pass multiple values to a macro not via multiple parameters but via a list of attribute-value pairs in a single parameter. Parsing more complex parameter values such as these may seem daunting at first, but regular expressions help us make light of the tas k! This paper explains how to use the PRXPARSE, PRXMATCH and the lesser known PRXPOSN regular expression functions in SAS to extract in robust fashion the values we need from such a list. The paper is aimed at those who wish to expand on a basic knowledge of regular expressions, and although the functions are applied and explained within a specific practical context, the knowledge gained will have much potential for wider use in your daily programming work | macro variable, macro loop, macro definition, %if-%then-%else, macro function | source | 7 |
| 161 | Using SAS for Forest Plots in AMNOG Meta-Analysis Reporting | Jian-An Lu, Jinwei Yuan |
Making forest plots is an indispensable part of presenting study results in a Benefit Dossier for the AMNOG assessment. It allows one to intuitively and quickly display overall efficacy and safety outcomes in a comprehensive manner. This is particularly important given the scope of the assessment, as it involves a large volume of statistics with multiple endpoints, multiple studies and multiple subgroups at both individual and meta-analysis levels. This paper demonstrates, step by step, how to utilize PROC SGPLOT combined with the attribute map and annotation capabilities provided by SAS to create table-like graphic displays that present all required statistical information in one place as per the IQWiG guidelines. We hope this approach can serve as a primer that can aid in the creation of the sophisticated forest plots required in the AMNOG submission |
forest plot, relative risk, character variable | source | 10 |
| 162 | Using Stata for handling CDISC compliant datasets and outputs | Tong Zhao, Jin Shi |
In cases where SAS software is not capable or financially beneficial to many researchers, Stata may be a good substitute for generating CDISC compliant data and results for company's use. In this paper, we shall explain how Stata can simplify many of the routine tasks encountered in handling CDISC data sets / TFLs, and the great efficiencies that can result from using Stata | macro variable, character variable, proc sql, cdisc | source | 7 |
| 163 | Validation of CDISC specifications using VALSPEC utility macro | Hrideep Antony, Aman Bahl |
The define.xml is the cover section of the electronic common technical document (eCTD) submission and it provides a high-level summary of the metadata for all the data submitted for an NDA (new drug application). Define.xml will not only aid in the ease of regulatory review but also conveys a message on the overall quality of the submitted work to the reviewers and a good quality one can augment their trust in the results that are being submitted. Define.xml document quality is widely driven by the data and the specifications that are used to generate the file. The accuracy of the SDTM/ADaM specifications is determined by parameters such as da ta compliance and the usage of controlled terminology. However, the majority of the discrepancies are only identified after the pinnacle validation. Specification updates based on the compliance findings while creating the final CRT package may result in post-lock updates to the SDTM/ADaM and TFLs | controlled terminology, data cleaning, cdisc | source | 8 |
| 164 | Virtual human organs in clinical trails | Sumeet Subhedar | Today, technology has already shown its promising outcomes in different fields, and is making its way in the field of drug development (data collection and analysis). But the recent pandemic ha s brought the world on its knees and there was an urgent need for an effective vaccine/antivirals in fast and cost effective way. We do have a solution available today, but does it mark a beginning of wide usage of a technology: Virtual organs/body replication using computer simulation? In simple words, computer simulation is replacing actual humans with computer human. Developing a tool focusing on different data collection points that are important for any drug research, example demographics, will be a challenge, but seems to be an achievable task in upcoming years. This paper will focus on certain aspects that can be visualized if virtual organs or humans take over actual humans | clinical trial, clinical research | source | 3 |
| 165 | Virtual Recruitment and Onboarding of Mapping Programmers during Covid 19- Merck-s RaM Mapping Experience | Srinivasa Rao Mandava | Covid 19 has changed the way of life of human perspective due to lockdown of office work while work from home have become of new way of life. It also changed dramatically the business hiring process and remote hiring become a key tool with modernization of their recruitment processes rapidly and innovatively. Thus, virtual recruitment and onboarding originated out of necessity in this crisis, is actively become a go-to method for keeping the hiring ball rolling without suffering the new drug submission processes across the pharmaceutical industry especially tier 1 companies like Merck. Vendors of sponsor companies like ours following these processes for long years, due to pandemic lockdown situation big pharma is also following same methodologies with advanced technologies, tools, and connectivity | pharmaceutical industry, service provider | source | 3 |
| 166 | Visualization of Sparse PK Concentration Sampling Data, Step by Step (Improvement by Improvement) | Ilya Krivelevich, Simon Lin |
Pharmacokinetic (PK) data is collected for a variety of purposes in clinical trials. PK data is critical in understanding a drug's safety and determining its dosing frequency. Pharmacokinetic analysis is a major part of clinical trials to obtain information on drug disposition in humans. Graphics are always a powerful tool in reporting, and Concentration vs. Timeline graphs are standard for reporting PK data. Data visualization tools are widely used in PK analysis, so meaningful graphics play a very important role. There are many presentations and examples that deal with displaying results of rich sample collections; however, there is a significant shortage of similar presentations that cover the visualization of results of the widely used sparse PK data collection. This article may be interesting from a methodology standpoint; it presents examples and suggestions of step-by-step development of figures for sparse PK concentration using powerful ODS SAS tools (PROC SGPLOT) |
proc sgplot, proc step, box plot, sample, clinical trial, outlier | source | 14 |
| 167 | Visually Exploring Proximity Analyses Using SAS PROC GEOCODE and SGMAP and Public Use Data Sets | Louise S. Hadden | Numerous international and domestic governments provide free public access to downloadable databases containing health data. Two examples include the Demographic and Health Surveys which include data from Afghanistan to Zimbabwe and the Centers for Medicare and Medicaid Services' Compare data and Part D Prescriber public use files. This paper and presentation will describe the process of downloading data and creating an analytic data base which includes geographic data; running SAS's PROC GEOCODE (part of Base SAS) using Tiger street address level data to obtain latitude and longitude at a finer level than zip code; and finally using PROC SGMAP (part of Base SAS) with annotation to create a visualization of a proximity analysis |
proc step, sas base, proc rank, data visualization, excel | source | 18 |
| 168 | What Kind of WHICH Do You CHOOSE to be? | Richann J. Watson, Louise S. Hadden |
A typical task for a SAS practitioner is the creation of a new variable that is based on the value of another variable or string. This task is frequently accomplished by the use of IF-THEN-ELSE statements. However, manually typing a series of IF-THEN-ELSE statements can be time-consuming and tedious, as well as prone to typos or cut and paste errors. Serendipitously, SAS has provided us with an easier way to assign values to a new variable. The WHICH and CHOOSE functions provide a convenient and efficient method for data-driven variable creation | choose, if-then-else | source | 7 |
| 169 | When Should I Break ADaM Rules? | Sandra Minjoe | The ADaM Model document (ADaM v2.1), Implementation Guides (ADaMIG v1.x) and Occurrence Data Structure document (OCCDS v1.0) include many rules and requirements for a general ADaM dataset and for an ADaM dataset of class Basic Data Structure (BDS). We are often able to follow all of these rules, but it is not uncommon to run into issues here and there. This paper describes a few of the most common issues you might find when trying to design an ADaM BDS dataset, and offers recommendations on how to decide when to break the rules | adam dataset, change from baseline, raw data, data cleaning, cdisc | source | 8 |
| 170 | Where-s Waldo? An SDTM Crawler | Derek P. Morgan | This is a utility that will quickly find all records for a given USUBJID across the entire SDTM database and can be modified to display all common SDTM data. Originally created to detect false subject visits from a defective clinical data entry system, as written, it lists all --DTC variables from an SDTM data set, as well as VISIT and VISITNUM if present | macro variable, proc sql, dataset, excel, %if-%then-%else, proc step, sample, sdtm domain, macro loop, macro definition, cdisc | source | 9 |
| 171 | Why Data Scientists need leadership skills? Story of Cross-Value Chain Data Utilization Project | Yura Suzuki, Yuichi Koretaka, Ryo Kiguchi, Yoshitake Kitanishi |
Shionogi Data Science (DS) Office is a new organization established in April 2020. One of our mission is to contribute to planning and promotion, and to support from the aspects of statistics and data science across the value chain and to contribute to management decisions based on scientific evidence. Many members of the DS Office have lots of experience in statistical analysis, programming, and data management in clinical drug development. We are playing a new role as data scientists based on the skills that have cultivated up to now, while gaining further strengths and skills. We have more opportunities to interact with colleagues of each value chain, and we are promoting the solution of business issues based on various data | data science, clinical trial, clinical data, clinical research | source | 8 |
| 172 | Wildcarding in WHERE Clauses | David B. Horvath | This mini-paper (and the resulting short session) is a focused discussion of wildcarding within the Where clause and related functionality within SAS. There is wildcarding allowed within where clause like expressions by using special characters. But if you want to search for those specific characters, you have to override them. If you forget to escape the character, you'll get unexpected matches. Topics include: PROC SQL; select x from y where x like %a/_b escape /; _ matches 1 character % matches any characters / escapes the next character (says next is actual character, not special meaning) * set sorted_ddl (obs=max where=(name like '%/_ORD%' escape '/')); |
where statement, index, proc step, proc sql | source | 7 |
| 173 | Willing to Play with William-s Design: An Exemplification of Randomization Schedule Generation using SAS | Abhyuday Chanda | Crossover design is one of the most used design in the c linical trials. With 'n' treatments in a 'n' period crossover design, an imbalance of carryover effect of 1st order may be observed. To maintain the balance, one needs to make sure the number of times a treatment (Drug A) preceding another treatment (Drug B) is same as that treatment (Drug A) succeeding the other treatment (Drug B). A Williams design, which is a (generalized) Latin Square Design (LSD), balanced for first order carryover effects is applicable in these cases. If the number of treatments to be tested is even, the design is a Latin square (nXn), otherwise it consists of two Latin squares (2nXn). This paper will demonstrate the readers the step -by-step process of generating randomization schedules for both even (6X6) and odd ( 3X3) number of treat ments with a Williams design using SAS | randomization, proc step | source | 12 |
| 174 | Writing Out Your SAS Program Without Manually Writing it Out - Let Macros Do the Busy Work | Mindy Wang | Problem happened when we were asked to write out data to ASCII files using SAS datasets. The format is in EXCEL spread sheet, but there are hundreds of variables. It is tedious to write the program all by hand. By using macro variables, there is an easier way for SAS to write out the text string for you, rather than manually keying in the details. It only takes a few second to run the program and the program can be easily applied to other datasets, as long as we have the formats the EXCEL or in SAS metadata. With the same logic, you can also modify the code for other repetitive tasks. For the SAS users who are thinking about using macros, but are kind of intimidated to use macros, this will be the presentation to get you into the door of starting to use macros. Once you learn the concept, the possibility is endless. This presentation will make your life a lot easier. You will have less busy work and less headache |
macro variable, excel | source | 10 |
| 175 | Yo Mama is Broke Cause Yo Daddy is Missing: Autonomously and Responsibly Responding to Missing or Invalid SAS Data Sets Through Exception Handling Routines | Troy Martin Hughes | Exception handling routines describe processes that can autonomously, proactively, and consistently identify and respond to threats to software reliability , by dynamically shifting process flow and by often notifying stakeholders of perceived threats or fai lures. Especially where software (including its resultant data products) supports critical infrastructure, has downstream processes, supports dependent users, or must otherwise be robust to failure, comprehensive exception handling can greatly improve software quality and performance. This text introduces Base SAS defensive programming techniques that identify when data sets are missing, exclusively locked, or inadequately populated. The use of user-defined return codes as well as the &SYSCC (system curren t condition) automatic macro variable is demonstrated, facilitating the programmatic identification of warnings and runtime errors | exception handling, sysrc, sas log, macro function, macro put, missing value, abort, sas base, cpu time, macro variable, macro definition, proc sort | source | 11 |
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