HOME
QUICK SEARCH
AUTHORS
KEYWORDS
HELP
BUGSPharmaSUG 2020 Papers - Index
138 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | %MVMODELS: a Macro for Survival and Logistic Analysis | Jeffrey P. Meyers | The research field of clinical oncology heavily relies on the methods of survival analysis and logistic regression. Analyses involve one or more variables within a model, and multiple models are often compared within subgroups. Results are prominently displayed within either a table or graphically with a forest plot. The MVMODELS macro performs every step for a univariate or multivariate analysis: running the analysis, organizing the results into datasets for printing or plotting, and creating the final output as a table or graph. MVMODELS is capable of running and extracting statistics from multiple models at once, performing subgroup analyses, outputting to most file formats, and contains a large variety of options to customize the final output. The macro MVMODELS is a powerful tool for analyzing and visualizing one or more statistical models | hazard ratio, treatment arm, forest plot, overall survival, logistic distribution, proc report, proc phreg, output, logistic regression, numeric variable, regression analysis, proc logistic, macro parameter, odds ratio, subgroup analysis, proc sql, proc calis, proc means, survival analysis, label statement, ods output, binomial distribution, group by, proc template, graph template language, excel, character variable, clinical trial, confidence interval, binary variable, macro call, by group processing, ods pdf | source | 23 |
| 2 | 'This Is Not the Date We Need. Let's Backdate': An Approach to Derive First Disease Progression Date in Solid Tumor Trials | Girish Kankipati, Boxun Zhang |
A time-to-event analysis, such as duration of response or progression-free survival, is an important component of assessments in oncology clinical trials. Derivation of true response dates is a key feature in developing ADRS and ADTTE ADaM data sets and a solid understanding of how such dates are derived is therefore critical for statistical programmers. This paper discusses hands-on examples in solid tumor trials based on investigator assessment using RECIST 1.1 and presents SAS programming techniques that are usually implemented in ADRS. Response date derivations are categorized as below: 1. Overall response date derivation: a. When an overall response is disease progression (PD): when at least one target, non- target, or new lesion shows PD, the date of PD is derived from the earliest of all scan dates that showed PD b | create table, proc sort, proc step, sample, cdisc | source | 11 |
| 3 | 10 things you need to know about PMDA eSubmission | Yuichi Nakajima | From April 2020, Pharmaceuticals and Medical Devices Agency (PMDA), health authority in Japan, mandates electronic submission (eSubmission) in new drug application (NDA). PMDA has published Basic Principals, Notification on Practical Operations, Technical Conformance Guide and FAQs of electronic submission for applicants. Although those guidance documents cover general and practical topics, there are many operational and technical challenges found in the transitional period from October 2016 to March 2020. Submission requirements of Food and Drug Administration (FDA), health authority in United States, and PMDA are different. Needless to say, electronic Case Report Tabulation (eCRT) package accepted by FDA is not always accepted by PMDA. It is difficult to define "golden standard" of electronic submission due to its various submission requirements | data transformation, data cleaning, clinical trial | source | 6 |
| 4 | A Brief Introduction to Performing Statistical Analysis in SAS, R & Python | Erica Goodrich, Daniel Sturgeon |
Statisticians and data scientists may utilize a variety of programs available to them to solve specific analytical questions at hand. Popular programs include commercial products like SAS and open source products including R and Python. This paper aims to present a brief primer into the coding and output provided within these programs to preform data exploration and commonly used statistical models in a healthcare or clinical space and looks at the reasons why a user may want to use different programs | logistic regression, proc freq, logistic distribution, regression analysis, proc means, proc step, log-rank test, rank test, survival analysis, data science, time variable, proc contents, interval variable, class variable | source | 22 |
| 5 | A Brief Understanding of DOSUBL beyond CALL EXECUTE | Ajay Sinha, M Chanukya Samrat |
SAS is one of the most widely used programming language in clinica l space, the newer release of SAS 9.3 R2 and above offers a function "DOSUBL" that can come very handy for programmers to shorten and make code more efficient. This paper intends to explain the use of DOSUBL function with help of some examples that can make code more robust and flash the best use of this function within the code. Function DOSUBL enables immediate execution of SAS code after a text string is passed. It is somewhat similar to CALL EXECUTE routine however differs significantly, this paper intends to present the merits and pitfalls of DOSUBL function and where it can come handy to make most use of this function. The DOSUBL function has a single argument, which is a string va lue. In a DATA STEP, the function submits code to SAS and waits for that code to complete submission. DOS UBL should be used in a DATA STEP |
call execute, dosubl, data step, hash table | source | 4 |
| 6 | A Doctor's Dilemma: How Propensity Scores Can Help Control for Selection Bias in Medical Education | Deanna Naomi Schreiber-Gregory | An important strength of observational studies is the ability to estimate a key behavior's or treatment's effect on a specific health outcome. This is a crucial strength as most health outcomes research studi es are unable to use experimental designs due to ethical and other constraints. With this in mind, one drawback of observational studies (that experimental studies naturally control for) is that they lack the ability to randomize their participants into treatment groups. This can result in the unwanted inclusion of a selection bias. One way to adjust for a selection bias is by using a propensity score analysis. In this paper, we explore an example of how to use these types of analyses. In order to demonstrate this technique, we seek to explore whether clerkship order has an effect on National Board of Medical Examiners (NBME) and United States Medical Licensing Examination (USMLE) exam scores for 3rd year military medical students | propensity score, sample, regression analysis, logistic distribution, sample size, proc step, logistic regression, causal inference, treatment effect, potential outcomes, predictor variable, randomization, proc freq, likelihood function, response variable, histogram | source | 14 |
| 7 | A Guide for the Guides: Implementing SDTM and ADaM standards for parallel and crossover studies | Azia Tariq, Janaki Chintapalli |
cdisc | source | 1 | |
| 8 | A Novel Solution for Converting Case Report Form Data to SDTM using Configurable Transformations | Sara Shoemaker, Matthew Martin, Robert Kleemann, David Costanzo, Tobin Stelling |
Converting case report form (CRF) data to SDTM is a complicated process, even when data are collected in CDASH format. Conversion requires many data set manipulations and demands flexibility in the order of execution. In addition, different domain types require different actions on the source data, e.g. Findings domains require transposition of data records. Many conversion solutions address this complexity by performing data pre-processing, mapping, and post-processing as disparate pipeline sections. Most include programming in a language such as SAS where blocks of code can obscure the details of the transformation from data customers. This paper describes a solution for SDTM conversion that uses a method termed "Configurable Transformations". This model both achieves conversion using one consistent pipeline for all phases from CRF data to SDTM and provides visibility into the data transformations for non-programmers | data dictionary, clinical data, crf, controlled terminology, sdtm domain, raw data, cdisc | source | 12 |
| 9 | A SAS Macro for Calculating Confidence Limits and P-values Under Simon's Two-Stage Design | Alex Karanevich, Michael Ames |
Simon's two-stage designs are popular single-arm, binary-endpoint clinical trials that include a single interim analysis for futility. One is typically interested in the proportion of successes (response rate), but SAS procedures (e.g. FREQ) do not provide p-values or confidence limits for this parameter that include the multiplicity adjustment for when such a trial proceeds beyond the first stage. This quick tip provides derivations (along with a SAS macro) for calculating such a trial's p-value and associated confidence limits. The macro requires the user to input the design parameters for the trial, as well as the total observed successes | confidence interval, macro definition, binomial distribution | source | 5 |
| 10 | A SAS Macro for Dynamic Assignment of Page Numbers | Manohar Modem, Bhavana Bommisetty |
In clinical domain, we usually create many safety and efficacy tables with various statistics. While creating these tables, we introduce SAS dataset with statistics into PROC REPORT to create listing or rtf output. Using PROC REPORT-BREAK-PAGE, we can make sure that each parameter statistics starts in a new page in the output. If we want to make sure that a group of statistics does not break abruptly between pages, we may need to use conditional statements to assign page numbers. Whenever there is an update in mock shell or data, the number of rows in the output may increase or decrease, which in turn requires an update in conditional statements to prevent abrupt, breaks in the output. This led to an effort to create a macro, which prevents abrupt page breaks and provides meaningful page numbers. This paper describes how the page numbers were dynamically assigned using SAS macro |
mock shell, proc report, dataset, demographics table, vital signs, numeric variable, proc step, macro call, macro parameter, ods rtf, sample | source | 10 |
| 11 | A SAS macro for tracking the Status of Table, Figure and Listing (TFL) Programming | Yuping Wu, Sayeed Nadim |
A clinical study report (CSR) typically involves in generation of hundreds of TFLs. To efficiently manage such large batch of outputs, Lead programmers and managers often need to know the current status of each programs and outputs. Many organizations may use an EXCEL based tracking document where production and validation programmers can enter the TFL status. However, for a CSR delivery that usually takes several months to prepare, such document is often out of status due to data addition, ADaM updating, miscommunication between production and validation programmers, etc.. This paper introduces a tool that can efficiently monitor the current status of the TFL programs for their logs, PROC COMPARE reports, program and output creation |
proc compare, proc step, data cleaning, clinical trial | source | 4 |
| 12 | A Set of VBA Macros to Compare RTF Files in a Batch | Jeff Xia, Shunbing Zhao |
Post database lock changes in a clinical trial are impactful and can result in significant rework. Statistical programmers must access the updated data and regenerate the TLFs for CSR to maintain data and result traceability. This paper briefly discusses the challenges in comparing two sets of RTF files before and after the post database lock changes, each set might contain tens or hundreds of files, and provides an efficient solution based on VBA technology | database lock, clinical trial | source | 6 |
| 13 | A Single, Centralized, Biometrics Team Focused Collaboration System for Analysis Projects | Chris Hardwick, Justin Slattery, Hans Gutknecht |
Statisticians and programmers work in concert with each other to complete analysis projects and often resort to using EXCEL and email to store metadata, track project milestones, and collaborate with each other. See how using a single, centralized system to store TFL/Dataset metadata, communicate key analysis project milestones, report on QC efforts, conduct collaborative TFL reviews, and track TFL change requests can dramatically reduce data entry and reap big productivity benefits |
proc contents, excel, audit trail, data science | source | 11 |
| 14 | A Solution to Look-Ahead Observations | Yongjiang (Jerry) Xu, Yanhua (Katie) Yu |
In clinical trials, it is common to compare certain laboratory test results across a period or various time points. For example, the confirmation of drug responses in multiple myeloma studies and some hematology indicators emerging back to safe levels from nadir (the lowest point). These data are usually mapped into finding domains in SDTM and programmed into BDS structure type ADaM datasets. However, it is difficult to look ahead at the values of future observations or look back to previous observations in SAS because SAS reads one observation at a time into the Program Data Vector (PDV). It is a known issue in SAS data management that we cannot do comparisons across observations. This paper will present a practical solution to transpose and do data comparison and calculations across observations using DO loops and arrays | do loop, clinical trial, cdisc | source | 5 |
| 15 | A Tool to Organize SAS Programs, Output and More for a Clinical Study | Yang Gao | Depending on the complexity of a study, there can be many programs and outputs to manage and review or even rerun due to new data or requirements. Programmers must navigate to different program subfolders to select and run the affected programs and revalidate as required. This paper presents a utility macro which can substantially reduce the resource and time spent on this task. This utility macro captures all SAS programs or output from a specified folder and saves them in an EXCEL Spreadsheet with the information of file path and corresponding hyperlink, which provides a convenient way to navigate to individual folders and subfolders. The hyperlinks facilitate output review for programmers and statisticians when there is a need for updates. In addition, this macro utility includes functionality to list the output with the corresponding SAS programs in the same spreadsheet. Programmers can quickly find the corresponding programs and outputs |
excel, clinical trial, proc sql, proc step | source | 10 |
| 16 | An Effective Management Approach for a First-Time Study Lead | Himanshu Patel, Jeff Xia |
Becoming a first-time study lead from a programming study team member is a significant step that comes with various challenges. Perseverance plays a substantial role in their development process, which helps to overcome these challenges. Programming of tables, listings, graphs, and datasets is mostly a logical and technical task, whereas leading the study involves additional management tasks. The successful accomplishment of any project is determined by the technical and management approach used by the study lead. Lead can adopt different management approaches, which include techn ical and leadership skills, effective communication, team motivation, stakeholder relationship, proper planning, and risk management. These factors allow study leads to create a committed and robust team that is dedicated to achieving the best result. This paper illustrates certain key features of an effective management approach that can be implemented to ensure effective and robust study management | adam dataset, clinical trial, sdtm domain, crf, data science, adverse events, cdisc | source | 10 |
| 17 | An Introduction to the ODS Destination for Word | David W. Kelley | The SAS Output Delivery System (ODS) destination for Word enables customers to deliver SAS reports as native Microsoft Word documents. The ODS WORD statement generates reports in the Office Open XML Document (.docx) format, which has been standard in Microsoft Word since 2007. The .docx format uses ZIP compression, which makes for a smaller storage footprint and speedier downloading. ODS WORD is preproduction in the sixth maintenance release of SAS 9.4. This paper shows you how to make a SAS report with ODS WORD. You learn how to create your report's content (images, tables, and text). You also learn how to customize aspects of your report's presentation (theme and styles). And you learn how to enhance your report with reader-friendly features such as a table of contents and custom page numbering. If you're cutting and pasting your SAS output into Microsoft Word documents, then this paper is especially for you! |
proc step, proc template, excel, proc print | source | 17 |
| 18 | An Overview of Medical Device Data Standards | Shilpakala Vasudevan | Medical device trials are widely conducted in recent times, for different therapeutic and diagnostic reasons. The nature of device trials is different from traditional clinical trials, and involve different study design, ways of collecting and data standards. Over the last few years, SDTM domains have been identified to accommodate data for devices, which can be used alongside with other regular domains usually found in clinical trials. Additionally, there have been analysis data set proposals that are being currently used. In this paper, we will see what medical devices are, what data standards exist for devices, and how different they are with example | adverse events, clinical trial, cdisc | source | 5 |
| 19 | Analysis Package e-Submission - Planning and Execution | Abhilash Chimbirithy, Saigovind Chenna, Majdoub Haloui |
Analysis package is one of the e-Submission components submitted to regulatory agencies as part of INDs, NDAs, ANDAs, and BLAs. Analysis package contains the study analysis data and related files following a standardized electronic format. Proper planning and resources are required to create this package which has several individual components such as datasets in XPT format, define, analysis results metadata (ARM), data reviewers guide, and programs. These components are separate deliverables but interrelated. With frequently identified challenges, questions and issues from previous studies, we have provided guidance related to planning, setting deliverables timelines, identifying team members' responsibilities and ensuring regulatory required data standards compliance for the components submitted. In this paper, we will present details for best practices, proper planning and checklists that can help teams efficiently create an analysis package | adam dataset, data cleaning, data standard, regulatory submission, efficacy summary, clinical programming, clinical trial, cdisc | source | 8 |
| 20 | Are you Ready? Preparing and Planning to Make the Most of your Conference Experience | Richann J. Watson, Louise S. Hadden |
Whether you are a first-time conference attendee or an experienced conference attendee, this paper can help you in getting the most out of your conference experience. As long-standing conference attendees and volunteers, we have found that there are some things that people just don't think about when planning their conference attendance. In this paper we will discuss helpful tips such as making the appropriate travel arrangements, what to bring, networking and meeting up with friends and colleagues, and how to prepare for your role at the conference. We will also discuss maintaining a workplace presence with your paying job while at the conference | problem solving, likelihood function | source | 15 |
| 21 | Assessing Performance of Risk-based Testing | Amber Randall, Bill Coar |
Trends in the regulatory landscape point to risk-based approaches to ensure high quality data and reporting for clinical trials. Risk-based methods for validation of production programming code which assign testing methods of varying robustness based on an assessment of risk have been evaluated and accepted by some industry leaders, yet they have not been fully adopted. Some view risk-based testing as simply an attempt to save money or compensate for limited resources while claiming a minimal impact on overall quality. While that may sometimes be the case, the intent should rather be to focus finite resources on what matters most. Even with the robust gold standard of full independent reproduction, mistakes still happen. Humans make errors. Therefore, risk and consequence should be considered in choosing verification methods with a resource emphasis on those areas with greatest impact | data cleaning, clinical trial, raw data, clinical programming, excel | source | 11 |
| 22 | Assigning Agents to Districts Under Multiple Constraints Using PROC CLP | Stephen B. Sloan, Kevin K. Gillette |
The Challenge: assigning outbound calling agents in a telemarketing campaign to geographic districts. The districts have a variable number of leads and each agent needs to be assigned entire districts with the total number of leads being as close as possible to a specified number for each of the agents (usually, but not always, an equal number). In addition, there are constraints concerning the distribution of assigned districts across time zones, in order to maximize productivity and availability. Our Solution: uses the SAS/OR procedure PROC CLP to formulate the challenge as a constraint satisfaction problem (CSP), since the objective is not necessarily to minimize a cost function, but rather to find a feasible solution to the constraint set |
proc step, macro variable | source | 9 |
| 23 | Automate your Safety tables using Artificial Intelligence & Machine Learning | Roshan Stanly, Ajith Baby Sadasivan, Limna Salim |
As part of the FDA submissions, it has been a common practice to create tables that present the output of statistical analysis of trial data. Some of these tables display the statistics of subject's safety parameters such as adverse events, laboratory results, vital signs etc. This paper explores the possibility of automating safety table (eg. Demographics, disposition, change from baseline etc.) generation using Natural Language Processing and Machine Learning algorithms. The proposed software framework uses Angular JS, SAS, R and Python for automating safety table generation | adam dataset, machine learning, demographics table, proc step, proc template, macro definition, proc means, cdisc | source | 8 |
| 24 | Automating CRF Annotations using Python | Hema Muthukumar, Kobie O'Brian, Julie Stofel |
When data are submitted to the FDA, an Annotated Case Report Form (aCRF) is to be provided as a PDF document, to help reviewers find the origin of data included in submitted datasets. These annotations should be simple, clean, and should respect appearance and format (color, font) recommendations. aCRFs are traditionally produced manually. This involves using a text editor in PDF and working variable by variable across many pages. This is a time-consuming process that can take many hours. In addition, maintaining consistency across pages requires substantial effort. This paper talks about an effective way to automate the entire aCRF process using Python. This approach automatically annotates the variables on the CRF next to their related questions on the appropriate pages | excel, crf, sdtm programming, xls, sample, cdisc | source | 9 |
| 25 | Automating of Two Key Components in Analysis Data Reviewer's Guide | Shunbing Zhao, Jeff Xia |
The Analysis Data Reviewer's Guide (ADRG) provides reviewers with additional context for analysis datasets received as part of a regulatory submission. It is crucial to submit a clear, concise and precise ADRG. The ADRG consists of seven sections (1. Introduction, 2. Protocol Description, 3. Analysis Considerations Related to Multiple Analysis Datasets, 4. Analysis Data Creation and Processing Issues, 5. Analysis Dataset Descriptions, 6. Data Conformance Summary, 7. Submission of Programs) and optional appendices. In sections 4 and 5, two key components are highly recommended to be included by FDA reviewers: a graph showing data dependencies and a table that identifies efficacy and primary outcome datasets. This paper presents three useful SAS macros to automatically create two key important components for ADRG. The first macro generates the data dependency graph by using SAS Graph Template Language (GTL). The second macro generates the table of "Analysis Dataset Descriptions" in ADRG | adam dataset, primary endpoint, excel, macro variable, macro definition, cdisc | source | 8 |
| 26 | Automation of Conversion of SAS Programs to Text files | Sachin Aggarwal, Sapan Shah |
During Clinical Trials Statistical Programmers, works on creation of many SAS programs to produce SDTM, ADaM datasets, Tables, Listings and Graphs as per the study requirements and various ad-hoc and post-hoc request also, which ends with ".sas" extension. The ".sas" extension SAS programs are required for working on different SAS software but for NDA submission process, one of the requirement by FDA is that all the submitted SAS programs have to be in ".txt" format. In this paper, we will show how to utilize PC SAS software and MS command prompt in order to change extension from ".sas" to ".txt" for submission to the FDA. This ".sas" to ".txt" conversion Macro helps Statistical Programmer in saving their valuable time by eliminating manual conversion as well as eliminate the error possibility, which might happen during manual conversion | macro variable, proc sql, proc step, macro quoting function, cdisc | source | 8 |
| 27 | Automation of Flowchart using SAS | Xingshu Zhu, Bo Zheng |
Flowchart diagrams are commonly used in clinical trials because they provide a direct overview of the process, including participant screening, recruited patient enrollment status, demographic information, lab test results, etcetera. Flowcharts are particularly useful when working on Pharmacoepidemiology studi es, which require dynamic study designs due to the unpredictability and variation in data sources. In this paper, we introduce a simple SAS macro that allows programmers to create customized flowchart diagrams that fit a customer's individual needs by selecting two different methods and applying them to various types of source data | excel, dataset, macro definition, proc sql, clinical trial, macro variable, sas programming, proc step | source | 9 |
| 28 | Best Practices for Annotated CRFs | Amy Garrett | There is no doubt that the SDTM annotated CRF (aCRF) is one of the most cumbersome submission documents to create. Once a purely manual task, the extreme burden of creating the aCRF has led to several novel methods to automate or partially automate the process. As the industry moves away from manually annotating CRFs and towards automation, it's more important than ever to truly understand the properties of a high-quality aCRF. This paper reviews published guidance from regulatory agencies and provides best practices for CRF annotations. Following these best practices will ensure your aCRF fulfills current regulatory requirements and meets the needs of internal users and programs. Figure 1. Sample aCRF form for a Randomization page TECHNICAL REQUIREMENTS FDA ACRF REQUIREMENTS Any process for creating the aCRF must consider and conform to the FDA technical requirements | sample, data cleaning | source | 5 |
| 29 | 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 |
| 30 | Building a Strong Remote Working Culture - Statistical Programmers Viewpoint | Ravi Kankipati, Prasanna Sondur |
Remote Working or Telecommuting has become more of a norm than exception. To add a dimension of off shore Functional Service Provider who extend this to their employees whilst maintain the status quo is a challenge in itself. Companies situated offshore are embracing this feature of flexible hiring by casting a wider net or to retain the existing talent. Only a handful of companies in the Statistical Programming Sphere have been successful in having a Full Remote workforce. Statistical Programming is a diverse field involving frequent discussions with fellow Programmers and Client Point of Contacts (PoCs) within the Statement of Work (SOW). A strong liaising with Data management staff, Statisticians and Medical writers is expected. Remote working thus throws a challenge for effective communication within & between global teams. Some examples being: Communicating timelines of each deliverable, data issues, validation comments and work transitions | statistical programmer, data cleaning, data science | source | 4 |
| 31 | Butterfly Plot for Comparing Two Treatment Responses | Raghava Pamulapati | Butterfly plot is an effective graphical representation for comparing two treatment responses for the same subject across different time points. Butterfly plots are drawn across a centered axis to separate two treatment responses in one picture. Butterfly plots accommodate a subject's two treatment responses in one plot, such as, current treatment response with prior study/non-study treatment response or active treatment response with control treatment response or mono therapy response with combination therapy response. Butterfly plots can be created using SAS PROC SGPLOT. The inclusion of the HBAR statement creates horizontal bars on the Y-axis. Each bar represents one subject. The RESPONSE option displays duration of study medications on the X-axis. The FILLATTRS option categorizes the response in colors by using variables that are derived for each treatment and response type with corresponding description displayed at plot legend using KEYLEGENT statement |
proc step, proc template, proc sgplot, treatment period, best response, adam dataset, complete response, class variable, procedures, pd, response variable, standard deviation, cdisc | source | 13 |
| 32 | Calculation of Cochran-Mantel-Haenszel Statistics for Objective Response and Clinical Benefit Rates and the Effects of Stratification Factors | Girish Kankipati, Chia-Ling (Ally) Wu |
In oncology clinical trials, primary and secondary endpoints are analyzed using different statistical models based on the study design. Objective response rate (ORR) and clinical benefit rate (CBR) are commonly used as key endpoints in oncology studies, in addition to overall survival (OS) and progression-free survival (PFS). The use of ORR and CBR as an endpoint in these trials is widespread as objective response to therapy is usually an early indication of treatment activity and it can be assessed in smaller samples compared to OS; furthermore, FDA considers ORR and CBR as clinical and surrogate endpoints in traditional and accelerated approvals. Bringing new therapies to market based on ORR and CBR requires specialized statistical methodology that not only accurately analyzes these key endpoints but can also accommodate stratified study designs aimed at controlling for confounding factors. The Cochran-Mantel-Haenszel (CMH) test provides a solution to address these many needs | fisher exact test, objective response rate, odds ratio, clinical trial, exact test, procedures, confidence interval, proc freq, surrogate endpoint, best response, sample, proc step | source | 8 |
| 33 | CDISC Standards for Medical Devices: Historical Perspective and Current Status | Carey G. Smoak | Work on SDTM domains for medical devices was begun in 2006. Seven SDTM domains were published in 2012 to accommodate medical device data. Minor updates to these seven SDTM domains were published in 2018. These seven SDTM domains are intended for use by medical device companies in getting their products approved/cleared by regulatory authorities and for use by pharmaceutical companies to put ancillary device data. As evidenced by the Therapeutic Area User Guides, pharmaceutical companies are using these seven SDTM domains for ancillary devices. However, adoption of these seven SDTM domains by medical device companies seems to be happening rather slowly. In 2014, a statistician at the Centers for Radiologic Health and Devices (CDRH) presented the issues that CDRH has with medical device submission and in 2015 the CDISC medical device team presented how the CDRH issues could be solved with CDISC standards | sdtm domain, data standard, cdisc | source | 8 |
| 34 | CDISC-compliant Implementation of iRECIST and LYRIC for Immunomodulatory Therapy Trials | Kuldeep Sen, Sumida Urval, Yang Wang |
The current RECIST and LUGANO criteria are designed for traditional chemotherapeutic regimens in solid tumor and lymphoma trials respectively. For chemoimmunotherapeutic trials however, immunotherapeutic regimens may cause tumor flares during the treatment and these tumor flares can be associated w ith clinical and imaging findings suggestive of progressive disease (PD). Without a more flexible interpretation, it is possible that some patients can be prematurely removed from a potentially benefiting treatment, thus leading to underestimation of the magnitude of the clinical benefit of the testing agent. Therefore, for trials involving immunotherapies, iRECIST and LYRIC guidelines were introduced in solid tumor and lymphoma trials respectively. This paper focuses on the implementation of additional response categories introduced by these guidelines, namely "Unconfirmed PD (iUPD)" and "Confirmed PD (iCPD)" by iRECIST, and "Indeterminate Response (IR)" by LYRIC | pd, standard deviation, complete response, procedures, proc step | source | 11 |
| 35 | Challenges and solutions for e-data submission to PMDA even after submission to FDA | Akari Kamitani, HyeonJeong An, Yura Suzuki, Malla Reddy Boda, Yoshitake Kitanishi |
CDISC compliant electronic data (e-data) submission is mandatory in PMDA application from April 2020, while FDA has been requesting standard e-data packages earlier. Those who have experience in FDA e- data submissions may tend to think preparation of PMDA e-data packages would not be very different. In fact, we tend to consider that submission to PMDA is easy after submitting e-data for the compound to FDA. However, this is not always true. Shionogi is a pharmaceutical company headquartered in Osaka, Japan with global presence in the European nations, Asia pacific and the United States of America. In this paper, we would like to share our test case in global submissions preparing e-data submission package of one study to FDA and PMDA. In particular differences in validation rules, target dates, documents for consultations, and other aspects will be discussed | data cleaning, data validation rules, clinical trial, cdisc | source | 14 |
| 36 | Chasing Master Data Interoperability: Facilitating Master Data Management Objectives 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 direc t 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 s oftware maintainability 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 co mma- 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 temp orary arrays 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 |
| 37 | Clinical Database Metadata Quality Control: Two Approaches using SAS and Python | Craig Chin, Lawrence Madziwa |
A well-designed clinical database requires well-defined specifications for Case Report Forms (CRFs), Field Attributes, and Data Dictionaries. The specifications are passed on to the Electronic Data Capture (EDC) Programmers, who program the clinical database. How can a study team ensure that the source specifications are complete, and the resulting clinical database metadata match the source specifications? This paper presents two approaches. Initially, we used SAS to read in the specifications and clinical database metadata, and to provide comparison checks. Then, we converted the project in Python in order to build a user-friendly tool that allows customers to run the checks themselves. These reports have improved the quality of our clinical databases, as well as saved each study team several hours of back and forth between the specification development and EDC programming | excel, clinical database, quality control, dataset, data cleaning, proc step, proc import, sample, xls, proc sql | source | 16 |
| 38 | Color Data Listings and Color Patient Profiles | Charley Z. Wu | During clinical trials, there are frequent datacuts for safety data review, interim data analysis, CSRs. Data reviewers usually need to review the data carefully to ensure data accuracy and integrity. They frequently complain that they have already reviewed the same data many times before, and they don't like to review the same data again. They would rather pay more attention to new and updated data. However, most of data listings/patient profiles/reports cannot tell what are new data and what are old data. To solve this issue, we developed color data listings and color patient profiles. The idea is that we can set the first datacut as a benchmark, all future data changes are then highlighted with different colors. For example, updated data are colored pink. New data are colored green. Deleted d ata are colored grey. Unchanged data are not colored. By doing so, reviewers can easily identify any data changes since previous datacut | data review, clinical trial, dataset | source | 5 |
| 39 | Creating SDTMs and ADaMs CodeList Lookup Tables | Sunil Kumar Gupta | Do you need to review and confirm codelist values for variables in SDTMs and ADaMs? Codelists are lists of unique values for key variables such as LBTEST, AVISIT and AVISITN. Codelists need to be cross-referenced with control terms. Codelist dictionary compliance checks are very important but are often neglected. Since all raw data is now standardized to control terms, there are many opportunities for cross checking SDTM, ADaMs with codelist dictionary tables. In addition, the define xml file must have a correct and updated codelist section | macro call, excel, macro definition, controlled terminology, dictionary tables, raw data, proc step, %if-%then-%else, cdisc | source | 8 |
| 40 | Data Library Comparison Macro %COMPARE_ALL | Jeffrey P. Meyers | Reproducible research and sharing of data with repositories are becoming more standard, and so the freezing of data for specific analyses is more crucial than ever before. Maintaining multiple data freezes requires knowing what changed within the data from one version to another. In SAS there is the COMPARE procedure that allows the user to compare two data sets to see potential new variables, lost variables, and changes in values. Relying on the COMPARE procedure can be tedious and cumbersome when maintaining a database containing several data sets. The COMPARE_ALL macro was written to ease this burden by generating a Microsoft EXCEL report of a comparison of two data libraries instead of just two data sets. The report indicates any new or lost data sets, variables or observations and checks for changed data values within all variables. Multiple ID variables can be specified and the macro will determine which variables are relevant with each data set for comparison |
proc compare, excel, proc freq | source | 6 |
| 41 | Data Review: What's Not Included in Pinnacle 21? | Jinit Mistry, Lyma Faroz, Hao Meng |
Many pharmaceutical and biotechnology companies outsource statistical programming activities and submission package preparation to CROs. Still, for all programming deliverables the sponsor remains responsible for quality, completeness, and compliance to p ublished standards and regulatory guidance. This makes it critical for the sponsor to implement efficient vendor oversight that touches on enough detail to ensure quality of the product provided by the CRO. Sponsors are widely using the Pinnacle 21 toolset to ensure SDTM and ADaM compliance with CDISC guidance. However, by itself this is not enough and additional review of how the CRO adopted CDISC implementation guides to assign or derive variables in alignment with study design, protocol, and SAP need to be conducted beyond Pinnacle 21 reports | adam dataset, data cleaning, data validation rules, sdtm domain, controlled terminology, trial summary, cdisc | source | 7 |
| 42 | Data Transformation: Best Practices for When to Transform Your Data | Janet E. Stuelpner, Olivier Bouchard, Mira Shapiro |
When is the best time to create the CDISC standard data? This has been debated for many, many years. Some say that it should be done at the very end of the study before the protocol is submitted. Some say to transform the data at the very beginning of the study as subjects start to enroll. And some do it as needed as the study is enrolling, the data is being cleaned and the shells of the tables, listings and figures are in the process of creation. This is a great forum for experts in the field to give thei r opinion as to how and when to perform the transformation into CDISC format | phase iii, sdtm domain, life sciences, cdisc | source | 4 |
| 43 | Dating for SAS Programmers | Joshua M. Horstman | Every SAS programmer needs to know how to get a date... no, not that kind of date. This paper will cover the fundamentals of working with SAS date values, time values, and date/time values. Topics will include constructing date and time values from their individual pieces, extracting their constituent elements, and converting between various types of dates. We'll also explore the extensive library of built-in SAS functions, formats, and informats for working with dates and times using in -depth examples. Finally, you'll learn how to answer that age-old question... when is Easter next year? | date variable, sysdate9, datetime, time, macro variable, date and time functions, datepart, automatic macro variable, user-defined format, dhms, sas programmer, options statement, macro function | source | 13 |
| 44 | Demographic Table and Subgroup Summary Macro %TABLEN | Jeffrey P. Meyers | Clinical trial publications frequently allocate at least one of the allotted tables to summarize the demographics, stratification factors, and other variables of interest of the patients involved with the study. These tables generally include basic distribution information such as frequencies, means, medians, and ranges while also comparing these distributions across a key factor, such as treatment arm, to determine whether there was any imbalance in patient populations when doing analysis. These distributions are not difficult to compute and combine into a table, but as treatments become more specific to patient characteristics such as genetic biomarkers and tumor stages there is a need to be able to display the distributions in subgroups | binomial distribution, logistic regression, logistic distribution, macro call, hazard ratio, time variable, regression analysis, clinical trial, date variable, odds ratio, variable list, excel, demographics table | source | 12 |
| 45 | Demystifying SDTM OE, MI, and PR Domains | Lyma Faroz, Sruthi Kola |
CDISC's SDTM IG is an extensive repository of domain metadata that helps organize clinical trial data into relevant and detailed classifications. With rapid advancements in new drug development, patients now have superior and expansive options for treatment. These innovations in medicine necessitate continual updates to the SDTM IG. However, study implementations may not always keep pace with these updates, thereby not fully utilizing valuable resources available through the IG. This paper highlights three such lesser-known SDTM domains which allow statistical programmers to more efficiently structure study data for downstream analysis and submission. We will also share sample CRFs as part of our case study on these domains: Ophthalmic Examinations (OE) Added in SDTM IG v3.3, this is part of the Findings class. It contains assessments that measure ocular health and visual status to detect abnormalities in the components of the visual system and determine how well the person can see | sample, adverse events, concomitant medications, cdisc | source | 8 |
| 46 | Detecting Side Effects and Evaluating the Effectiveness of Drugs from Customers' Online Reviews using Text Analytics, Sentiment Analysis and Machine Learning Models | Thu Dinh, Goutam Chakraborty |
Drug reviews play a very significant role in providing crucial medical care information for both healthcare professionals and consumers. Customers are utilizing online review sites to voice opinions an d express sentiments about experienced drugs. However, a potential buyer typically finds it very hard to go through all comments before making a purchase decision. Another big challenge is the unstructured and textual nature of the reviews, which makes it difficult for readers to classify comments into meaningful insights. For these reasons, this paper primarily aims to classify the side effect level and effectiveness level of prescribed drugs by using text analytics and predictive models in SAS Enterprise Miner. Additionally, the paper explores specific effectiveness and potential side effects of each prescription drug through sentiment analysis and text mining within SAS Visual Text Analytics | data cleaning, response variable, proc freq, data mining, probability distribution, feature selection, proc hpsplit, neural network, histogram, parsing, validation set, regression analysis, machine learning, character variable, training set | source | 22 |
| 47 | Diaries and Questionnaires: Challenges and Solutions | Marina Komaroff, Sandeep Byreddy |
In PharmaSUG2019 conference opening session, there was a question about the most unfavorable data set for programmers and statisticians to work with. Diaries and questionnaires (QS) was named among the first five! The rationale was the complexity of QS: too m any items to work with, and hard to compare responses across the time points, within and between subjects. Clustering and/or categorization of diaries' items using clinical judgement is known approach that helps with analyses. However, to compare the responses of the questions across multiple time points still requires deep understanding of the research question and strong programming skills. The goal of this paper is to convert diaries -haters to diaries-lovers and explain how appropriate algorithm should be developed and programmed | character variable, macro definition | source | 7 |
| 48 | Effective Exposure-Response Data Visualization and Report by Combining the Power of R , SAS programming and VBScript | Shuozhi Zuo, Hong Yan |
Understanding the relationship between exposure and response is critical to finding a dose that optimally strikes a balance between drug efficacy and adverse events, therefore comprehensive exposure- response (E-R) analyses are highly demanded throughout all phases of clinical trials. This type of analysis could be planned, ad-hoc or exploratory, and it requires high quality data visualization and fast turnaround for dose selection, phase 2 decision or regulatory interaction and submissions etc. This paper introduces an innovative efficient way for exposure-response analysis results visual presentation by combining the power of SAS programming in data manipulation and processing and R language in data visualization and dynamic statistical analysis. We will use the logistics regression E-R analysis as an example, explaining how the data quality and accuracy is maintained in SAS and connecting R function to generate multiple figures by different exposure parameters and endpoints | sample, regression analysis, logistic regression, adverse events, logistic distribution, macro definition, excel, data science, data visualization, scatter plot, dataset, confidence interval, proc iml, bar chart, data cleaning, proc step, binomial distribution | source | 11 |
| 49 | End-to-end Prostate-Specific Antigen (PSA) Analysis in Clinical Trials: From Mock-ups to ADPSA | Joy Zeng, Varaprasad Ilapogu, Xinping Cindy Wu |
Prostate-specific antigen (PSA) level is a key biomarker in prostate cancer that has been used in standard guidelines as a measurement of clinical outcomes for patients with prostate cancer. This paper aims to provide an end-to-end overview of the programming aspects of PSA-related trials. We describe the concepts of PSA response and time to PSA progression, two important end points in assessing efficacy of prostate cancer trials, along with the statistical methods involved in estimating the distri bution of time to PSA progression. The paper also addresses the design of metadata from PSA -related mock-up tables and presents the considerations involved in the creation of CDISC-compliant ADPSA dataset based on the metadata. Programming in the oncology therapeutic area is highly specialized and we hope this paper serves as a one-stop shop for providing the necessary tools to navigate through it | hazard ratio, treatment arm, survival analysis, sample, waterfall chart, confidence interval, randomization, cdisc | source | 12 |
| 50 | Enhanced Visualization of Clinical Pharmacokinetics Analysis by SAS GTL | Min Xia | Graphs are essential tools to comprehensively represent data and raise the readability of analysis. They are an integral part of Pharmacokinetic (PK) analysis and the high-quality PK graphs are very important for Clinical Study Report (CSR) and a key task for regulatory submissions. This paper provides instructions to create PK analysis graphs powered by SAS Graph Template Language (GTL) such as multiple panels series plot and multiple columns forest plot. In addition, DYNAMIC variables and other advanced techniques are introduced to increase the flexibility of GTL and the data visualization accuracy | pk parameter, proc step, forest plot, by group processing, proc template | source | 9 |
| 51 | Ensuring Consistency Across CDISC Dataset Programming Processes | Jennifer K. Fulton | Whether you work for a small start-up, a mid-level CRO, or Fortune 500 biopharma company, CDISC compliance is a daunting prospect at the start of any project and requires creative solutions and teamwork. Consistency is the hallmark of a CDISC project and was the impetus for the formation of the consortium. Each CDISC project should be approached consistently as well, leading to improved accuracy and productivity, regardless of staff skill and experience. This approach leads to quality final product and FDA approval, ultimate goal. Westat approached this goal by developing an overarching CDISC development and delivery checklist. It provides a visual of the scope of a CDISC project, organizes work instructions and templates for users, and helps assure critical steps are not missed. Like the 26 miles in a marathon, our paper will lay out 26 steps to CDISC compliance, along with tools and techniques we have developed, to help others reach the finish line | sdtm domain, macro definition, independent qc, sas programmer, trial summary, data cleaning, cdisc | source | 10 |
| 52 | 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 data sets, SAS users across industries know all too well that data can be, and often is, problematic and plagued with a variety of issues. Two data files can be joined without a problem when they have identifiers with unique values. However, many files do not have unique identifiers, or "keys", 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 protocols | proc step, proc sql, character variable, proc sort, sas function, missing value, data cleaning, numeric variable, data transformation, proc format, order by, proc contents, proc print, character functions, proc transpose | source | 28 |
| 53 | Generating ADaM compliant ADSL Dataset by Using R | Vipin Kumpawat, Lalitkumar Bansal |
In this paper we show how to generate an ADaM compliant ADSL dataset using R. R packages such as Sas7bdat, Dplyr, Tidyr, and Hmisc are used to generate the ADSL dataset. A procedure to set-up the R-environment for the process of generating the ADSL dataset is shown. The typical steps used to create the ADSL dataset along with the derivation of numeric variables, flags, treatment variables and trial dates are outlined. R procedures to attach labels to the variables are discussed. A side by side comparison of R and SAS code is presented. A known weakness in R such as attaching labels to the variables [1] has been resolved in this work. The challenges encountered in generating the ADSL dataset using R are discussed | clinical trial, missing value, sdtm domain, proc sort, adam dataset, proc step, cdisc | source | 9 |
| 54 | Getting It Right: Refinement of SEND Validation Rules | Kristin Kelly | The CDISC SDTM metadata, outlined in the SDTM Model, are used for submission of data from both clinical trials and nonclinical studies. Until recently, many of the Pinnacle 21 validation rules were assigned for both SDTM and SEND domains when in some cases, a specific rule did not apply for SEND data as outlined in the SENDIG. Over the past year, the SEND rule set has been refined through the modification of existing rules, removal of others and creation of new rules. All rules are based on either an FDA Business rule, an FDA Validator rule, or CDISC rules. This paper will discuss some of the changes that have been made in an effort to 'get the rules right' for SEND | data cleaning, data validation rules, type i error, cdisc | source | 7 |
| 55 | Gradient Descent: Using SAS for Gradient Boosting | Karen Walker | Much of applied mathematics is centered on equations and evaluation of ERRORS in effort to reduce them during computations. Optimization of ERRORS is the focus of this topic. This paper will illustrate mathematics equations applied to thousands of calculations. Then show algorithms that are proven to optimize these calculations through scoring. One such algorithm is a gradient descent. It will show ways to reduce the errors by letting the gradient approach zero and adjusting for that . Next, this paper will use the gradient descent SAS program to find the smallest ERROR when matching two models and sub sequently introduce the SAS GRADBOOST procedure. Finally it will prove that more precision is achieved using gradient descent with gradient boosting algorithms and show why thi s is so important to machine learning . Scientific methods like: Machine Learning and Deep Learning utilize mathematical functions that are rendered to algebraic computations are then programmed |
machine learning, neural network, proc step, data cleaning, deep learning, maximum likelihood estimation, normalization, regression analysis | source | 11 |
| 56 | Great Time to Learn GTL: A Step-by-Step Approach to Creating the Impossible | Richann J. Watson | Output Delivery System (ODS) graphics, produced by SAS procedures, are the backbone of the Graph Template Language (GTL). Procedures such as the Statistical Graphics (SG) procedures dynamically generate GTL templates based on the plot requests made through the procedure syntax. For this paper, these templates will be referenced as procedure-driven templates. GTL generates graphs using a template definition that provides extensive control over output formats and appearance. Would you like to learn how to build your own template and make customized graphs and how to create that one highly desired, unique graph that at first glance seems impossible? Then it's a Great Time to Learn GTL! This paper guides you through the GTL fundamentals while walking you through creating a graph that at first glance appears too complex but is truly simple once you understand how to build your own template |
proc template, series plot, proc step, sample, scatter plot, missing value | source | 26 |
| 57 | Hidden Gems in SAS Display Manager: Old Wine in New Bottle | Ramki Muthu | Clinical SAS programmers tend to spend more than 90% of their work time using the SAS enhanced editor and other display manager windows, and it is imperative to maximize the available functionalities for their routine task. While most of the programmers are alread y familiar handling the SAS editor, and the topic was reviewed earlier, this paper attempts to hunt some hidden (or under-utilized) features in SAS display manager for Windows. Briefly, starting with few common etiquettes this paper discuses: Summary of keyboard shortcuts that programmers should be aware, Window management to make a split view of a program and utilizing tile vertical options, Using command bars to display required variables from a dataset, sub setting the records and precisely moving to the desired record (e.g. from 1st to 28474th record) using the command prompt | sas log, proc step, clinical sas, sample, macro definition, proc freq, proc sql | source | 11 |
| 58 | Highlight changes: An extension to PROC COMPARE | Abhinav Srivastva | Although version control on the files, datasets or any document can be challenging, COMPARE Procedure provides an easy way to compare two files and indicate the differences between them . The paper utilizes the comparison results from PROC COMPARE and builds it into a SAS macro to highlight changes between files in terms of addition, deletion or an update to a record in a convenient EXCEL format. Some common examples where this utility can be useful is comparing CDISC Controlled Terminology (CT) release versions, comparing Medical dictionary versions like MedDRA, WHODrug, or comparing certain Case Report Form (CRF) data like Adverse Events (AE) to review new events being reported at various timepoints for data monitoring purposes |
proc compare, excel, proc step, macro definition, cdisc | source | 4 |
| 59 | How to Achieve More with Less Code | Timothy J. Harrington | One of the goals of a SAS Programmer should be to achieve a required result with the minimal amount of code. The two reasons for "code complexity" are: one, too many lines of code, and two: code which is unduly difficult to decipher, for example a large number of nested operations, pairs of parentheses, operators, and symbols packed closely together. This paper describes methods for reducing the amount and complexity of SAS code, and for avoiding repetition of code. Included are examples of SAS code using v9.2 or later functions such as IFN, IFC, CHOOSE, WHICH, and LAG. This content and discussion is primarily intended for beginner and intermediate SAS users |
character functions, sum, missing value, retain, ctcae grade, ifn, sas programmer, adverse events | source | 13 |
| 60 | How to let Machine Learn Clinical Data Review as it can Support Reshaping the Future of Clinical Data Cleaning Process | Mirai Kikawa, Yuichi Nakajima |
Technology utilized in pharmaceutical industry has been evolving. There are a lot of innovative new technology such as Artificial Intelligence, Machine Learning, Digitization, Block chain, Big Data, Open Source Software, etc., which can build a new era of clinical drug development. Manual data review is one of the required processes to ensure clinical data cleanness and readiness for analysis that are essential for subject safety and reliability of the submission documents. Manual data review process involves several roles of people such as Data Manager, Clinical and/or Medical Reviewers, Safety Reviewer, etc. Since it requires complicated logical thinking and clinical and medical knowledge and expertise, it has to be "manual". That has been the common understanding, and thus the conventional approach | data review, machine learning, clinical trial, proc hpsplit, clinical data, feature selection, f1 score, proc hpforest, supervised learning, model validation, model training, regression analysis, data cleaning, adverse events | source | 9 |
| 61 | Impact of WHODrug B3/C3 Format on Coding of Concomitant Medications | Lyma Faroz, Jinit Mistry |
The WHODrug dictionary is the industry standard for coding concomitant medications. As CDISC becomes more prevalent and strongly recommended by regulatory authorities for submission, the dictionary has evolved over time to ensure full compliance. It is maintained by the Uppsala Monitoring Centre (UMC) with updates provided to industry users twice every year, that is, 1 st March and 1st September. The previous WHODrug B2/C formats are now up versioned to B3/C3, which make WHODrug coded data fully compliant with the expectations of regulatory authorities and bring heightened efficiency and other benefits to the industry. The older vs newer format length updates have impact on mapping of coding concomitant medication data according to SDTM CM guidelines. To add to that, per a notice in the Federal Register published by FDA in October 2017, the use of the B3 format is required in submissions of studies starting after 15th March 2019 | concomitant medications, statistical programmer, cdisc | source | 6 |
| 62 | Implementation of Immune Response Evaluation Criteria in Solid Tumors (iRECIST) in Efficacy Analysis of Oncology Studies | Weiwei Guo | In recent years the Immunotherapy have gained attention as being one of the most promising types cancer treatment on the horizon. Immunotherapy, also called biologic therapy, is a type of cancer treatment that boosts the body's natural defenses to fight cancer. While conventional RECIST criteria have served us well in evaluating chemotherapeutic agents, in immuno-oncology, a small percentage of patients manifest a new response pattern termed pseudoprogression, in which, after the initial increase in tumor burden or after the discovery of new lesions, a response or at least a prolonged stabilization of the disease can occur. Tumors respond differently to immunotherapies compared with chemotherapeutic drugs, raising questions about analysis of efficacy. Therefore, a novel set of anti-tumor assessment criteria iRECIST was published to standardize response assessment among immunotherapy clinical trials | clinical trial, best response | source | 6 |
| 63 | Implementing a LEAD Function for Observations in a SAS DATA Step | Timothy J. Harrington | A common situation in DATA STEP processing is the need to reference the value of a variable (column) in a prior or later observation. The SAS system provides the functions LAG and DIFF to return the value of the variable in the prior observation or the difference between the current value of the variable, in the Program Data Vector (PDV), and the prior value. LAGn and DIFFn refer to the nth prior value, where 1<=n<=100 and must not refer to before the first observation (_N_=1) in the dataset. However, there is no corresponding LEAD function which looks at values in observations still to be read into the PDV. This paper demonstrates three different methods of implementing a LEAD functionality. The modus operandi of each method is illustrated with examples of SAS code and the advantages and disadvantages of each method are discussed, as is the suitability of each method for specific types of programming situations |
class variable, sample, out=, temporary dataset, proc transpose, in=, proc step | source | 12 |
| 64 | Implementing Quality Tolerance Limits at a Large Pharmaceutical Company | Steven A. Gilbert | Predefined quality tolerance limits (QTLs) were introduced in the revised ICH E6 (R2) Section 5 update to help identify systematic issues that can impact subject safety or reliability of trial results. This paper will focus on Pfizer's implementation of this requirement. The key focus will concern the approach with respect to loss of evaluable subjects, patient discontinuation and inclusion/exclusion errors, that is easily measurable attribute data. We discuss a team approach in setting tolerance limit as well as lessons learned in monitoring the progress and the important role of simulations in de fining best practices for monitoring trials. Examples of simulation methods, signal detection through control charts suitable for short-run attribute data such as variable life adjusted displays and other graphical methods will be demonstrated along with example code | clinical trial, sample, poisson distribution, standard deviation, sample size, binomial distribution, numeric functions, proc step | source | 13 |
| 65 | Improving the Quality of Define.xml: A Comprehensive Checklist Before Submission | Ji Qi, Yan Li, Lixin Gao |
The define.xml is the cover letter in Module 5 of the electronic Common Technical Document (eCTD) submission to the U.S. Food and Drug Administration (FDA) which provides a high-level summary of the metadata for all the data submitted. A functioning, complete and informative define.xml is required by FDA regulation. A high-quality define.xml will not only aid in the ease of FDA review but also convey the attentiveness of the sponsor's work attitude to the reviewers and augment their trust in the results. In this paper, we will provide a list of details to check and fix before submission of the define.xml, focusing on those for clinical tabulation data (SDTM) and analysis data (ADaM), based on a review of common issues reported in papers as well as our own experience generating define.xml for clients | data cleaning, adam dataset, quality control, sdtm domain, cdisc | source | 8 |
| 66 | Is Your Dataset Analysis-Ready? | Kapila Patel, Nancy Brucken |
One of the fundamental principles of the Analysis Data Model (ADaM) is that analysis data sets should be analysis-ready, which means that each item displayed on an output table, listing or figure can be generated from an analysis data set with minimal programming, according to the ADaM Implementation Guide (ADaMIG). The analysis data set s produced by assuming a 1 -to-1 relationship between Study Data Tabulation Model (SDTM) and ADaM data sets may not be analysis-ready, especially if the actual analysis requirements have not been considered. This paper will provide several examples of how an SDTM domain can be split into more than one ADaM data set to meet analysis needs and show that the SDTM domain class does not have to drive the class of the resulting ADaM data sets | sdtm domain, proc freq, cdisc | source | 10 |
| 67 | It's All about the Base-Procedures, Part 2 | Jane Eslinger | "It's All about the Base-Procedures" (a PharmaSUG 2019 paper) explored the strengths and challenges of commonly used Base SAS procedures. It also compared each procedure to others that could accomplish similar tasks. This paper takes the comparison further, focusing on the FREQ, MEANS, TABULATE, REPORT, PRINT, and SQL procedures. As a programmer, whether novice or advanced, it is helpful to know when to choose which procedure. The first paper provided best-use cases, and this paper takes it a step further in its discussion of when to choose one procedure over another. It also provides example code to demonstrate how to get the most out of the procedure that you choose | proc step, proc tabulate, proc report, proc sql, proc freq, proc print, proc means, class variable, by group processing, proc format, sas base, group by, macro variable | source | 20 |
| 68 | Leadership Lessons from Start-ups | Siva Ramamoorthy | Over the last two decades, we have seen an extraordinary growth of startups and startup ecosystems. This phenomenon is much more pronounced in countries like the US, China, and India, with 'billion-dollar plus valuation' unicorns, successful entrepreneur role models, and enormous media buzz. Startups are characterized by immense zeal and enthusiasm about bringing a great new idea to market and making a difference in the world. Such startup's often begin with a single idea or a unique solution to an existing problem and a notional investment. Still, they attract hundreds and thousands of dreamy-eyed employees who, in many cases (if it is a very early stage startup), work for free/almost free with a promise of future wealth. The failure rate of such startups is as high as 90%, yet employees are happy to take the risk. Besides being strongly motivated, startup employees are highly productive and help implement innovative ideas at rapid speeds | life sciences, data cleaning | source | 7 |
| 69 | Leading without Authority: Leadership At All Levels | Janette Garner | The idea of what it means to be a leader takes on different meanings for each person and can vary across situations. One image is that of a manager directing a team under tight timelines. Another example would be a team member who proposes a solution to a novel problem. At its core, leadership is the process of driving influence from others to achieve a goal. This does not require seniority, organizational hierarchy, titles or personal attributes, though these are often conflated. Anyone can be a leader, including those without direct reports. This paper examines different leadership styles and contains numerous examples of how an individual contributor can make an impact in an organization, both large and small | clinical programming, likelihood function | source | 7 |
| 70 | Library Datasets Summary Macro %DATA_SPECS | Jeffrey P. Meyers | The field of clinical research often involves sharing data with other research groups and receiving data from other research groups. This creates the need to have a quick and concise way to summarize incoming or outgoing data that allows the user to get a grasp of the number of datasets, number of variables, and number of observations included in the library as well as the specifics of each variable within each dataset. The CONTENTS procedure can fulfill this role to an extent, but the DATA_SPECS macro uses the REPORT procedure along with the EXCEL Output Delivery System (ODS) destination to create a report that is fine tuned to summarize a library. The macro produces a one page overview of the datasets included in the specified library, and then creates a new worksheet for each dataset that lists all of the variables within that dataset along with their labels, formats, and a short distribution summary that varies depending on variable type |
excel, proc contents, numeric variable, ods excel, proc freq | source | 7 |
| 71 | Like, Learn to Love SAS Like | Louise S. Hadden | How do I like SAS? Let me count the ways.... There are numerous instances where LIKE or LIKE statements can be used in SAS - and all of them are useful. This paper will walk through such uses of LIKE as: searches and joins with that smooth LIKE operator (and the NOT LIKE operator); the SOUNDS LIKE operator; using the LIKE condition to perform pattern-matching and create variables in PROC SQL; and PROC SQL CREATE TABLE LIKE |
proc sql, proc step, create table, where statement | source | 7 |
| 72 | Macro for controlling Page Break options for Summarizing Data using Proc Report | Sachin Aggarwal, Sapan Shah |
In Statistical Programming while working on development of different types of Summary Tables and Listings using PROC REPORT, Statistical Programmers come across a number of challenges as far as presentation of the report output is concerned. Some of the challenges faced by the Statistical Programmers are: 1.) Printing certain number of non-missing lines on one page 2.) Not ending the page with a new parameter starting 3.) Combining the groups and sub-groups on one page 4.) Continuation of main group heading in case the splitting of main group and subgroup is unavoidable example- adverse events 5.) Addition of suffix in case of group splitting into different page |
dataset, proc report, adverse events, proc step, system organ class | source | 7 |
| 73 | Macro To Produce Sas-Readable Table Of Content From Tlf Shells | Igor Goldfarb, Ella Zelichonok |
The goal of this work is to develop a macro that automates a process of reading the shells for tables, listings and figures (TLF) and transforming them into SAS-readable ordered table of content (TOC). The proposed tool can significantly save time for biostatisticians and lead programmers who have numerously create, revise, update shells documents for TLF. Development of the shells for TLF for any clinical trial is a time-consuming task. Copying titles and footnotes from the shell document (typically MS W ord file) into SAS program or external source of titles and footnotes (e.g., EXCEL file) is a tedious process requiring scrupulous work subject to human errors. The proposed macro (developed in EXCEL VBA) automates this process. It reads the shell document (Word) and creates/updates SAS -readable ordered TOC (EXCEL) in a matter of seconds. The macro identifies common part of the titles and footnotes for all TLF as well as detects differences for specific outputs |
excel, clinical trial, txt, pharmaceutical industry, safety population, macro definition, infile statement, sas programmer, proc step, proc report | source | 18 |
| 74 | Making Customized ICH Listings with ODS RTF | Huei-Ling Chen, William Wei |
ICH (International Consortium on Harmonization) data listings are common reports prepared by pharmaceutical companies in regulatory submission. Such listings are often submitted in RTF format. This paper presents a technique that can be used to efficiently produce a customized ICH Abnormal Lab Listing based on a company's uniform RTF output standards. This technique includes five components: data preparation; and four SAS syntaxes (SYSTEM OPTIONS, ODS TAGSETS.RTF, PROC TEMPLATE style, and PROC REPORT) can be utilized to define the layout and to render a data listing table. This paper will first describe the problem that we are trying to solve and then give details on the ODS TAGSETS.RTF options that we have chosen to solve this problem with mock data |
proc report, proc step, proc template, treatment arm, binary variable, ods rtf, options statement, macro variable, proc print | source | 13 |
| 75 | Metadata-driven Modular Macro Design for SDTM and ADaM | Ellen (Zihui) Lin, Aditya Tella, Yeshashwini Chenna, Michael Hagendoorn |
In clinical trial data analyses, macros are often used in a modular or building-block fashion to standardize common data transformations in SDTM or derivations in ADaM to heighten efficiency and quality across studies. A traditional design of such macros is the parameter-driven approach, which provides users with many parameters to control differences on input, output, and data processing from study to study. There are limitations with this kind of design, for example the macro may stop working when unexpected differences arise beyond the scope controlled by parameters, or a lengthy revision and documentation cycle is needed to rework parameter-driven code once more variations are introduced by new studies. Metadata-driven programming is a much more dynamic approach for macro design, especially when targeting areas where differences between studies are less predictable | excel, clinical trial, macro parameter, macro definition, proc sql, macro variable, where statement, proc step, data cutoff, raw data, clinical data, data science, data wrangling, date variable, interim analysis, macro call, dataset, macro loop, cdisc | source | 9 |
| 76 | Microsoft OneNote: A Treasure Box for Managers and Programmers | Jeff Xia | Microsoft OneNote has many functionalities that are helpful for managers and lead programmers who have increasing responsibility of managing project deliverables as well as understanding staff availability to ensure quality deliverables with compliance to department SOPs. Additionally, managers and leads have the responsibility to keep upper management informed of the overall status in the group operation, including project progress, success stories, challenges as well as potential problems within the group. To effectively perform their daily operations with so many responsibilities in different directions, it is essential for managers and programming leads to find an efficient and effective way to organize the necessary information for the entire process so that they can locate files and information quickly to resolve any unexpected issues. Microsoft OneNote is a tool that can serve this purpose | access control, pharmaceutical industry | source | 8 |
| 77 | MKADRGM: A Macro to Transform Drug-Level SDTM Data into Traceable, Regimen-Level ADaM Data Sets | Sara McCallum | In our support of the NIH funded AIDS Clinical Trials Group (ACTG) and International Maternal Pediatric Adolescent AIDS Clinical Trials (IMPAACT) networks, participants concurrently take multiple medications for the treatment of HIV and other diseases in many of our studies. For analysis and presentation to investigators, our statisticians find an ADaM data set that aggregates all drugs in a regimen at a given time into a single record, where that record represents the period of time those drugs were taken, is needed to summarize regimens, identify regimen changes or treatment gaps, and to aid in explaining other events on study (e.g., occurrence of adverse events or emergence of drug resistance). This paper will present our organization's SAS macro, MKADRGM. The macro accepts WHO Drug coded CDISC domains recorded at the drug level, and outputs an ADaM data set at the regimen level with start and stop dates | whodrug, sdtm domain, proc step, create table, proc sort, adam dataset, order by, reference data, sample, cdisc | source | 34 |
| 78 | Moving A Hybrid Organization Towards CDISC Standardization | Kobie O'Brian, Sara Shoemaker, Robert Kleemann, Kate Ostbye |
This paper discusses the experience of implementing standardization of data collection and data set development of submission-ready data sets at a unique organization at the intersection of Academia and Industry Partners. SCHARP (Statistical Center for HIV/AIDS Research and Prevention) at Fred Hutchinson is an academic center with a nonprofit business model. It is in a unique position requiring a balance of standard regulatory reporting requirements as well as specific sponsor needs with stakeholders including the National Institutes of Health (NIH), academic centers, nonprofit foundations, and pharmaceutical manufacturers. This requires a tailored approach using Clinical Data Interchange Standards Consortium (CDISC) standards for collecting, submitting, and analyzing data across the organization. Governance of the different CDISC implementation strategies for organization-wide data collection, storage, and analysis is discussed as well | crf, clinical trial, data governance, data standard, data standardization, regulatory reporting, data cleaning, cdisc | source | 8 |
| 79 | Natural History Study - A Gateway to Treat Rare Disease | Tabassum Ambia | A Natural History Study is a study that follows a group of people over time who have, or are at risk of developing, a specific medical condition or disease. It bears significant importance in the discovery, marketing and post-marketing phases of a drug. There are different types of natural history studies which may help to determine the requirement of the adequate treatment in a target population or to assess the outcome of a treatment in real life. Real world evidence (RWE) data are the primary source of health information to be used in a natural history study. Statistical analysis is focused on both incidence and prevalence of the disease involving different procedures to determine the distribution of characteristics or events over a certain time and association with covariates. A rare disease is one which affects a very small percentage of the population. Recently FDA has emphasized the importance of natural history studies for the development of orphan drugs for rare diseases | sample, medical history, proc means, adam dataset, proc freq, clinical research, cdisc | source | 5 |
| 80 | Next Level Programming-Reusability and Importance of Custom Checks | Akhil Vijayan, Limna Salim |
SDTM-domain structures and relationships are similar across studies under a therapeutic area which leads to code standardization and reusability especially within ISS/ISE submissions. Inter im data transfers also come with changes in data leading to rerun of existing programs with minor updates. The possibility of errors in such scenarios are large with truncation in data, new data issues being unidentified, attribute changes etc. This paper details the importance of using standard macros and alternative programming approaches like enabling custom checks/warnings that makes reusability of programs a much smoother process. Not all Data Issues are identified at the initial stage of Source Data Validation, but they tend to surface during the development of CDISC datasets. In addition, certain data issues identified at the initial run might not always be necessarily resolved in the next data transfer. This is where custom errors / warnings play a significant role | proc step, data cleaning, proc sql, cdisc | source | 9 |
| 81 | 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 D ietary Supplements section of NHANES, a parallel programming project, conducted by the National Center for Health Statistics, a | dataset, proc step, sample, create table, proc compare, proc sql, quality assurance, if-then-else | source | 18 |
| 82 | Normal is Boring, Let's be Shiny: Managing Projects in Statistical Programming Using the RStudio Shiny App | Girish Kankipati, Hao Meng |
The ability to deliver projects on schedule, within budget, and aligned with business goals is key to gaining an edge in today's fast-paced pharmaceutical and biotechnology industry. Project management plays an important role in achieving key milestones with high quality and optimal efficiency. While successful project management is an application of processes, methods, skills, and expertise, tools like SAS, RStudio, and PythonTM can help track project status and better position the lead programmer to allocate appropriate resources. One suitable app that fits the specific needs for status tracking in Statistical Programming is R Shiny, a flexible programmer-friendly tool that can present an instant study status overview in graphical format with minimal coding and maintenance effort. This paper introduces an innovative design to track study status through an RStudio Shiny app that is interactive and reusable and can present status on demand | data step, clinical programming | source | 9 |
| 83 | Oncology Graphs-Creation (Using SAS and R), Interpretation and QA | Taniya Muliyil | Data visualization plays a key role in analyzing and interpreting data. In oncology studies, graphs help visualize, interpret and analyze trends in data from a statistical perspective. Graphical outputs help in exploring data, identifying issues with data and in turn help improve data quality. Most commonly used statistical software for creating oncology graphs in pharmaceutical industry is SAS and R. Programmers create complex graphical outputs, but many are unable to interpret the results that these graphs display. This paper focuses on creating some common oncology graphs like spider plot, swimmer plot and waterfall plot using R and SAS, along with interpreting the results displayed by these graphs. It also discusses common QA findings that will reduce issues while generating these outputs and in turn help with statistical interpretation and analysis | waterfall chart, swimmer plot, best response, baseline, change from baseline, sample size, sample, data science | source | 10 |
| 84 | One Boy's Dream: Hitting a Homerun in the Bottom of the Ninth Inning | Carey G. Smoak | One boys' dream of hitting a homerun in the bottom of the ninth inning has been realized in my career. My career started out as an epidemiologist in academia. My SAS skills were pretty basic back then. My SAS skills advanced tremendously as I transitioned to working as a statistical SAS programmer in the pharmaceutical and medical device industries. My career has been varied from strictly working as a statistical SAS programmer to managing statistical SAS programmer. My interest in statistics began with my interest in baseball. Little did I realize that my interest in statistics as a teenager would lead to a fulfilling career and, thus, fulfill my childhood dream | sas programmer, statistical programmer, clinical trial, clinical programming, macro facility, regression analysis, adverse events | source | 5 |
| 85 | One Macro to create more flexible Macro Arrays and simplify coding | Siqi Huang | The purpose of using macro array is to make it easier to repetitively execute SAS code. Macro array is defined as a list of macro variables sharing the same given prefix and a numeric suffix, such as A1, A2, A3, etc., plus an additional macro variable with a suffix of "N" containing the length of the array. In this paper, I will introduce a %MAC_ARRAY macro, which provides a more flexible way to create a macro array from any given list of values, or from any selected variable in a dataset, either character or numeric. The application of this macro array is broad as well, including but not limited to: 1) creating similar datasets; 2) stacking multiple datasets; 3) repeating same calculation among multi ple variables in the datasets; 4) automatically updating parameters used in other macros. To sum up, %MAC_ARRAY macro can easily keep your code neat and improve program efficiency | macro variable, macro loop, do loop, sample, out=, macro variable array | source | 6 |
| 86 | Opening Doors for Automation with Python and REST: A SharePoint Example | Michael Stackhouse | A large part of increasing the efficiency of a process is finding new ways to automate. If manual components of a process can be replaced with automation, those components can then move to the background where they can be validated and trusted to be reliable. Large swaths of data sit within company intranets or other services that may seem 'disconnected' - but they may be more connected than one might think. This paper will explore extending automation capabilities with Python and REST APIs, using SharePoint as a primary example. Some topics covered will include using Python to make web requests, the basics of authentication, and how to interact with REST APIs. The paper will demonstrate how data repositories that may seem disconnected can be integrated into automated processes, opening doors for new data pipelines and data sources to pave the way for process improvements, efficiency increases, and better information | rest api, clinical trial | source | 9 |
| 87 | Overcoming Pitfalls of DS: Shackling the 'Elephant in the room' | Soumya Rajesh, Michael Wise |
Disposition (DS) is a standard SDTM domain that has been around since the inception of SDTM. Although familiar, it has often been m isinterpreted or misu sed. Unlike other SDTM domains, direct mapping from CRF pages presents challenges within DS. For example, CRF values may not be a perfect fit for the terms defined in controlled terminology (CT) Codelists, especially as seen in 'End of Study' or 'E nd of Treatment' pages. When a Codelist does not have an exact match with the CRF text, you may need to request NCI to extend the Codelist or add new terms. This, however, may create problems because not every "new term" should extend a Codel ist. Also, it's important to understand the differences between criteria so that D SCAT / DSD ECOD values are ass igned app ropriately. From an annotation perspective, this means that if DS CAT / DSDECOD values are 'assi gned', they should not be annotated on the CRF | randomization, sdtm domain, cdisc | source | 9 |
| 88 | Pattern Detection for Monitoring Adverse Events in Clinical Trials - Using Real Time, Real World Data | Surabhi Dutta | Patient care involves data capture from disparate sources of care delivery. This includes clinical trials data, sensor data from wearable sensors, handheld devices, and Electronic Health Records. We are accustomed to devices that generate health indicator data in large volumes and rapid rate. This paper discusses the benefits, challenges, methods of utilizing this real-time data for pattern detection using machine learning algorithms. This will be done using real world data that has been standardized and integrated during clinical trials. In our previous years paper, we had discussed about "Merging Sensor Data in Clinical Trials". The paper dealt with standardizing clinical trials data and making it ready to use for analysis. This year we will delve deeper in to using the sensors data for Pattern Recognition and Adverse Event Monitoring by classifying and segregating specific group of high-risk patients, participating in clinical trials, right from first subject first data point | clinical trial, time series analysis | source | 20 |
| 89 | Pinnacle 21 Community v3.0 - A Users Perspective | Ajay Gupta | Pinnacle 21, also previously known as OpenCDISC Validator, provides great compliance checks against CDISC outputs like SDTM, ADaM, SEND and Define.xml. T his validation tool provides a report in E xcel format which contains information categorized as errors, warnings, and notices. In May2019, Pinnacle 21 team had released Community v3. 0. This paper will provide an overview of all major updates in Pinnacle 21 community v3.0 e. g. new validations checks, ADaM IG v1.1 support, latest Controlled Terminology support. Later, this paper will cover the SN OMED, MedDRA dictionaries installation process which is no longer supported by Pinnacle 21 | data cleaning, clinical data, web service, data validation rules, cdisc | source | 10 |
| 90 | Preparing a Successful BIMO Data Package | Elizabeth Li, Carl Chesbrough, Inka Leprince |
In order to shorten the time for regulatory review of a new drug application (NDA) or biologic license application (BLA), more and more biotech and pharmaceutical companies prepare their Bioresearch Monitoring (BIMO) program packages as part of their initial submissions. In this paper, we walk the reader through a process of producing BIMO information, particularly the subject-level data line listings by clinical site (by-site listings) and the summary-level clinical site (CLINSITE) dataset. This paper concludes with methods of preparing electronic Common Technical Document (eCTD) documentation, such as data definition (define.xml) and reviewer's guide, to support the CLINSITE dataset. In addition, we discuss challenges as we share our experience in planning, producing, and quality control (QC) for a successful BIMO package | proc calis, sample, adam dataset, label statement, clinical trial, clinical database, protocol deviations, raw data, excel, confidence interval, treatment arm, treatment effect, cdisc | source | 20 |
| 91 | Principal Stratum strategy for handle intercurrent events: a causal estimand to avoid biased estimates | Andrea Nizzardo, Giovanni Marino Merlo, Simona Scartoni |
The ICH E9 (R1) addendum "Estimands and Sensitivity Analysis in Clinical Trial" emphasizes the necessity to quantify better treatment effects addressing the occurrence of intercurrent events that could lead to ambiguity in the estimates. One approach proposed in the addendum is the "Principal Stratum strategy," where the target population for the analysis is a sub-population composed of patients free from intercurrent events. The main concern is that it is not possible to identify the stratum in advance, and the analysis is then not causal and liable to confounding. Moreover, the occurrence of intercurrent events is not predictable, and each subject is observed on one treatment only and could experience different intercurrent events on different treatments. Also the FDA Missing Data Working Group recommends the use of a causal estimand for the evaluation of primary interest endpoints | intercurrent event, principal stratum, sensitivity analysis, treatment arm, treatment effect, sample, sample size, randomization, proc sort, clinical trial, potential outcomes, proc step, anova | source | 13 |
| 92 | PROC Future Proof; | Amy Gillespie, Susan Kramlik, Suhas R. Sanjee |
Clinical trial programmer s are critical contributors to regulatory submissions, manuscripts, and statistical analyses. They operationalize analysis plans to create high -quality, innovative, compliant deliverables according to stakeholders' needs. Clinical trial programmer s author programming code and leverage programming standards to produce deliverables in a v alidated, efficient, and reproducible manner . However, the function itself has stayed relatively constant for more than 20 years , and it is natural to question whether there are opportunities and a need to transform the clinical trial programming role for continued success. This paper evaluates 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 | clinical trial, artificial intelligence, adam dataset, data science, link analysis, sdtm domain, cdisc | source | 8 |
| 93 | PROC REPORT - Land of the Missing OBS Column | Ray Pass | proc report, proc step, proc print | source | 3 | |
| 94 | Programming Patient Narratives Using Microsoft Word XML files | Yuping Wu, Jan Skowronski |
Patient narratives are an important component of a clinical study report (CSR). The ICH-E3 guidelines require a brief narrative to describe each death, each serious adverse event and other significant adverse events that are judged to be of special interest because of clinical importance. Different from individual tables and listings in a CSR, patient profiles present most, if not all, of the information collected for a subject over the course of a clinical trial. Such documents typically integrate the various sources of data into one cohesive output with each part present in different formats which pose challenging problems for both programmers and medical writers. This paper introduces a new method that uses SAS together with Office Open XML. The narrative Word templates layout is created separately with properly tagged cells. The Word file is then broken up into its XML parts | clinical data, dataset, adverse events, concomitant medications, proc step, serious adverse event, proc print, proc report | source | 9 |
| 95 | Programming Technique for Line Plots with Superimposed Data Points | Chandana Sudini, Bindya Vaswani |
Line graphs are mainly plotted by connecting associated data points over a specified time interval to portray an overall trend. Despite the existence of many visualization methods and techniques, line plots continue to be a simple way of displaying quantitative data patterns in exploratory analyses. Line plots can be used to present either summary stat istics such as mean, standard deviation of a population or individual subject level data over a time period. In a scenario where we need to understand the impact of concomitant me dication (CM) on the laboratory measurements (LB) for each subject , presenting line plots with subject level data from those two sources can be extremely challenging, since the y-axis value at the time of CM administration may be unknown. In order to accomplish this, we propose using the properties of a straight line to predict the potential y-value at the time of the CM administration | study day, line chart, proc sgplot, sample, data step, proc step, concomitant medications | source | 7 |
| 96 | Project Metrics - a powerful tool that supports workload management and resource planning for Biostats & Programming department. | Jian Hua (Daniel) Huang, Rajan Vohra, Andy Chopra |
For pharmaceutical compan y with a large number of pipelines (i.e. several hundreds of studies ongoing) and a big group of Biostats & Programming team (i.e. > 200 people), it could be challenging for its leadership team to conduct workload management and resources planning. My team creates a "project metrics" which is aimed to collect Biostats & P rogramming work load related information and resource prediction . The metrics contains the key information of 'Biostats & Programming Deliverables' and they are further organized by following categories: Function, (TA) Group, Lead, Compound, and Study. It provides pre - defined pull-down lists (i.e. compound list, type of deliverables list) to ensure the consistency of data entry. In addition, it creates customized dashboard reports that give leadership team a comprehensive overview and great details | data validation rules, adam dataset, cdisc | source | 11 |
| 97 | Python-izing the SAS Programmer 2: Objects, Data Processing, and XML | Mike Molter | As a long-time SAS programmer curious about what other languages have to offer, I cannot deny that the leap from SAS to the object-oriented world is not a small one to be taken lightly. Anyone looking for superficial differences in syntax and keywords will soon see that something more fundamental is at play. Have no fear though, for languages such as Python have plenty of similarities to give the SAS programmer a strong base of knowledge from which to start their education. In this sequel to an earlier paper I wrote, we will explore Python approaches to programming tasks common in our industry, taking every opportunity to expose their similarities to SAS approaches. After an introduction to objects, we'll see the many ways that Python can manipulate data, all of which will look familiar to SAS programmers. With a solid working knowledge of objects, we'll then see how easy object-oriented programming makes the generation of common industry XML | data step, macro variable, sas programmer, proc step, dataset, excel, by group processing, character variable, do loop, proc sort, put statement, class variable, sas function, sas syntax, macro facility, macro parameter, %macro parameters | source | 18 |
| 98 | Quick, Call the "FUZZ": Using Fuzzy Logic | Richann J. Watson, Louise S. Hadden |
SAS practitioners are frequently called upon to do a comparison of data between two different data sets and find that the values in synonymous fields do not line up exactly. A second quandary occurs when there is one data source to search for particular values, but those values are contained in character fields in which the values can be represented in myriad different ways. This paper discusses robust, if not warm and fuzzy, techniques for comparing data between, and selecting data in, SAS data sets in not so ideal conditions | proc step, index, where statement, decimal, fuzz, if-then-else, prxmatch, proc fcmp, upcase function, sample, character variable | source | 30 |
| 99 | R for Clinical Reporting, Yes - Let's Explore it! | Hao Meng, Yating Gu, Yeshashwini Chenna |
R is an interpreted programming language- based software application which can be an ideal platform for statistical analysis and data visualization. For biostatisticians and programmers in the pharmaceutical and biotech industry, it offers a wide and rapidly growing range of user-developed packages containing functions which can efficiently manipulate complex data sets and create tables, figures, and listings. While SAS remains a critical tool in these industries, the popularity of RStudio in both academia and the clinical industry increased exponentially over the last decade since it is free and open- source, has powerful statistical support and advanced visualization through its huge user base and extension packages | data visualization, sample, adverse events, clinical trial, excel, clinical data | source | 16 |
| 100 | r2rtf - an R Package to Produce Rich Text Format (RTF) Tables and Figures | Siruo Wang, Keaven Anderson, Yilong Zhang, Simiao Ye |
The use of open-source R is evolving in drug discovery, research and developmen t for study design, data analysis, visualization, and report generation in the pharmaceutical industry. The ability to produce tables, listings and figures (TLFs) in customized rich text format (RTF) using R is crucial to enhance the workflow of using Microsoft Word to assemble analysis results. We developed an R package, r2rtf, that standardizes the approach to generate highly customized TLFs in RTF format. The r2rtf package provides flexibility to customize table appearance for table title, subtitle, colu mn header, footnote, and data source. The table size, border type, color, and line width can be adjusted in each cell as well as column width, row height, text format, font size, text color, alignment, etc. The control of the format can be row or column ve ctorized by leveraging the vectorization in R | change from baseline, sample, adverse events, efficacy summary, r package | source | 15 |
| 101 | Remove Strikethrough Texts from Excel Documents by VBA Macro | Li Liu | This paper focuses on removing strikethrough text embedded in EXCEL Spreadsheet documents programmatically using VBA Macro. Data Definition document (i.e., define.xml) is a critical part of FDA Submissions. ADaM specifications in EXCEL Spreadsheet format is the source document to create the Analysis Define.xml document. Before doing so, programmers must clean up the ADaM specifications by removing irrelevant text (e.g., strikethrough text embedded in EXCEL Spreadsheet documents for tracking). Manually removing strikethrough text from an EXCEL Spreadsheet is a cumbersome task to do & prone to errors. We must apply extra caution to make sure the entire strikethrough text removal when done manually. Also, the user must make sure that normal text remains and is not deleted by mistake |
excel, sas programmer, cdisc | source | 5 |
| 102 | Risk-based and Exposure-based Adjusted Safety Incidence Rates | Qiuhong Jia, Fang-Ting Kuo, Chia-Ling (Ally) Wu, Ping Xu |
In clinical trials, safety event incidences are summarized to help establish the safety profiles of investigational drugs. The most common and straightforward method is the crude rate, which is the total number of subjects with at least one event of interest within a given population. However, if the average duration of exposure differs significantly between treatment groups within a trial or between trials included in an analysis due to differential drop-out rates or study design, such incidence rates may need statistical adjustment to make the comparison meaningful. The analysis of exposure-adjusted incidence rates is often found useful in such cases. This paper introduces a simplified exposure-adjusted rate where sum of treatment duration of a population is used as the denominator, as well as a time-at-risk adjusted rate where sum of person-time- at-risk for each event of interest is used as the denominator | exposure-adjusted incidence rate, adverse events, risk exposure, forest plot, treatment effect, clinical trial, exposure | source | 8 |
| 103 | RStats: A R-Shiny application for statistical analysis | Sean Yang, Hrideep Antony, Aman Bahl |
This paper will introduce RStats application which is an interactive and dynamic R-Shiny based application that can perform popular statistical analysis models that are frequently used in clinical trials. The application can perform even the advanced analytics such as Logistic regression, Survival analysis, Anova test, Correlation etc., along with the summary plots for users with no prior R programming or even with limited statistical knowledge. This application also eliminates the need for numerous lines of programming effort to create a similar statistical analysis | data science, logistic regression, survival analysis, logistic distribution, regression analysis, dataset, response variable, predictor variable, anova | source | 8 |
| 104 | RTOR: Our Side of the Story | Shefalica Chand, Eric Song |
FDA's Real-Time Oncology Review (RTOR) pilot program was initially introduced in June 2018 for supplemental New Drug Applications (NDA) and supplemental Biologics License Applications (BLA) and more recently extended to original NDAs and BLAs. This presents a new ray of hope for cancer patients , as the program aims to expedite review of oncological submissions with improved efficiency and quality by allowing FDA earlier access to clinical safety and efficacy data and results, especially those related to Biometrics. This in turn may help expedite availability of novel treatments to cancer patients. Seattle Genetics participated in the RTOR pilot program for the supplemental BLA for ADCETRIS based on the ECHELON-2 trial in the frontline treatment of patients with CD30-expressing PTCL, which received approval in an unprecedented 11 days from sBLA submission | clinical programming, data cleaning | source | 10 |
| 105 | Sample size and HR confidence interval estimation through simulation of censored survival data controlling for censoring rate | Giulia Tonini, Letizia Nidiaci, Simona Scartoni |
Time to event variables are commonly used in clinical trials where survival data are collected. When planning a trial and calculating the sample size, it is important t o estimate not only the expected sample size but also the minimum value of the Hazard Ratio for which the trial can be considered successful ( i.e. accepting the alternative hypothesis). Calculating the confidence interval for the HR when the difference of treatments is statistically significant, can be performed simulating censored survival data. Simulating censoring can be challenging, especially when the trial does not appropriate to a fixed duration or when high drop-out rate is expected. This is often t he case in Oncology trials as patients exit the study prematurely (e.g. in case of AE or disease progression). In this work we present a case study where a dataset is simulated controlling for the resulting censoring rate | sample size, hazard function, weibull distribution, sample, confidence interval, hazard ratio, null hypothesis, median survival, proc phreg | source | 10 |
| 106 | SAS Formats: Same Name, Different Definitions FORMAT-ters of Inconvenience | Jackie Fitzpatrick | While working with multiple data sets, especially if they were created by different programmers, do you check the formats that are applied to the variables. This paper shows why it is best to proactively examine format catalogs while working with multiple data libraries, how to check the uniformity in their definitions, and what is necessary to fix discrepancies. Hopefully, you will benefit by adding this tool to your current collection | excel, char., proc step, error message, dataset, create table, macro loop, proc format, proc import | source | 9 |
| 107 | SAS Migration from Unix to Windows and Back | Valerie Williams | SAS software has been a mainstay for performing an alysis and reporting (A&R) in the pharmaceutical industry, for the past four decades. SAS occasionally releases new versions of their software with code enhancements and bug fixes to SAS functions, proce dures, and/or elements. In order to exploit those changes, SAS users may need to upgrade their progra mming environment and migrate their SAS programs and data to a new platform. Migrating A&R directories across operating systems has proven to be the most challenging, of any migration process, but much can be learned from migrating to a differe nt operating system twice. Resources from SAS, IT, and all business units that use or plan to use SAS mus t work together to perform the appropriate level of testing and documentation, to ensure that proper directory permissions are in place and that the new environment is fit for purpose, without affecting business unit delivery timelines | sas grid, sas studio, sas enterprise guide, catalog, clinical trial, pharmaceutical industry | source | 9 |
| 108 | SDSP: Sponsor and FDA Liaison | Bhanu Bayatapalli, Shefalica Chand |
The discussion between a sponsor and FDA on data standards for statistical programming deliverables in an electronic submission should start at the early stages of product development and continue along the way to filing. This discussion will involve data standards, structures, and versions to be used for each study submitted wit h an NDA or BLA. The Study Data Standardization Plan (SDSP) is used as a tool to communicate with FDA on these aspects. Sponsors and applicants are encouraged to utilize established FDA-sponsor meetings (e.g., pre-IND, end of phase 2, Type B/C) to share and discuss the SDSP | clinical trial, adam dataset, cdisc | source | 8 |
| 109 | SDTM to ADaM Programming: Take the Leap! | Jennifer McGrogan, Alyssa M. Wittle |
Does ADaM programming ever feel intimidating or outside of your expertise? Then this presentation is tailored just for you. Why is it time to take the leap into learning more about ADaM or even to dive into ADaM programming? The more you understand about the path clinical data goes through to arrive at the analysis, the better a programmer you will be! This presentation will highlight why it is important and exciting to learn about ADaM theory and ADaM programming. Where do you even start when learning about this topic? We will give some recommendations for effective ways to learn about ADaM and ADaM programming without getting overwhelmed in all the very technical details up front. We will give examples of common pitfalls and solutions often encountered when just starting out in ADaM. Finally, to assist in your success in this learning experience, we will give some tips and tricks to help you along the way | adam dataset, imputation, cdisc | source | 5 |
| 110 | Shell Script automation for SDTM/ADaM and TLGs | Pradeep Acharya, Aniket Patil |
In clinical trials, every study has multiple datasets (SDTM/ADaM) and several number of tables, listings and figures (TLFs). Throughout the study , there are many instances where you are required to re -run the datasets due to new incoming data or updates in key datasets such as subject level dataset (ADSL), which results in refresh of all other datasets and TLFs to maintain TIMESTAMP consistency. One of the approaches is to run the datasets and TLFs one after the other consuming a significant amount of time for program execution. Another commonly used method is to manually create a n executable batch/script file that includes all the program names . The drawback in these methods is you can inadvertently miss a dataset/TLG program execution due to manual error, and then it becomes cumbersome to go through the logs and output s to identify which program has been missed |
data cleaning, clinical trial, macro variable, cdisc | source | 6 |
| 111 | Should I Wear Pants? And Where Should I Travel in the Portuguese Expanse? Automating Business Rules and Decision Rules Through Reusable Decision Table Data Structures | 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 flex ible, 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 arrays that can accommodate limitless contingency groups and contingencies of any content |
if-then-else, macro variable, array, predictor variable | source | 13 |
| 112 | Simplifying PGx SDTM Domains for Molecular biology of Disease data (MBIO). | Sowmya Srinivasa Mukundan, Charumathy Sreeraman |
Pharmacogenomics/genetics peruses how the genetic makeup of an individual affects his/her response to drugs. It deals with the influence of acquired and inherited genetic variation on drug response in patients by correlating genetic expression with pharmacokinetics (drug absorption, distribution, metabolism and elimination) and pharmacodynamics (effects mediated through a drug's biological targets). The purpose of the SDTMIG-PGx is to provide guidance on the implementation of the SDTM for biospecimen and genetics-related data. The domains presented in the SDTMIG-PGx are intended to hold data that fall into one of three general categories: data about biospecimens, data about genetic observations, and data that define a genetic biomarker or assign it to a subject. The paper will throw some light on the mapping challenges encountered in MBIO data with sample CRF pages illustration | sample, sdtm domain, cdisc | source | 6 |
| 113 | Simplifying the Derivation of Best Overall Response per RECIST 1.1 and iRECIST in Solid Tumor Clinical Studies | Xiangchen (Bob) Cui, Sri Pavan Vemuri |
The objective tumor response rate (ORR) is one of endpoints in solid tumor clinical studies per FDA guideline [1]. It plays the critical role in earlier phase oncology studies. It is based on the best overall response (BOR), which is defined as the best response across all time-point responses. Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST 1.1) [2] has been quickly adopted since 2009, and iRECIST [3] has been recommended for use in trials testing immune therapeutics by the RECIST working group since March 2017. These two guidelines are applied to derive the overall response at each time point (both on-treatment and follow-up). For non-randomized trials, both RECIST 1.1 and iRECIST require the confirmation of complete response (CR) and partial response (PR). Moreover, progressive disease (iUPD) needs the confirmation per iRECIST. These confirmations provide some challenges in statistical programming during the derivation of BOR | complete response, best response, procedures, pd, clinical programming, macro definition, standard deviation | source | 24 |
| 114 | Sometimes SQL Really Is Better: A Beginner's Guide to SQL Coding for DATA Step Users | Brett Jepson | Structured Query Language (SQL) in SAS provides not only a powerful way to manipulate your data, it enables users to perform programming tasks in a clean and concise way that would otherwise require multiple DATA steps, SORT procedures, and other summary statistical procedures. Often, SAS users use SQL for only specific tasks with which they are comfortable. They do not explore its full capabilities due to their unfamiliarity with SQL. This presentation introduces SQL to the SQL novice in a way that attempts to overcome this barrier by comparing SQL with more familiar DATA STEP and PROC SORT methods, including a discussion of tasks that are done more efficiently and accurately using SQL and tasks that are best left to DATA steps |
proc sql, out=, in=, order by, where statement, create table, proc sort, group by, proc means, full join, proc step, right join, inner join, if-then-else, data merging | source | 16 |
| 115 | Special Plots methods with diabetes disease data | Yida Bao, Zheran Wang, Jingping Guo, Philippe Gaillard |
A graph has always been more intuitive than icy statistics. SAS gives us quite powerful graph capabilities. In this project, we use diabetes datasets as an example to do data visualization research. The detection of diabetes can generally be judged by several indicators - Glucose, Insulin, BMI , and so on. In general, our research contains three parts. First, we apply SAS procedure PROC GPLOT to create a line diagram, which helps us to explore the inner structure between different factors. We will introduce two different methods to generate an overlay plot based on the binary detection results. Also, we will apply SAS Enterprise Miner to proceed with the principal component analysis, which helps us to bright the project. Later, we'll use the typical discrimination method, convert the dataset into several canonical variables, and generate the proper plot to express the result |
data mining, data visualization, sample, interval variable, proc princomp, proc step, line chart, proc gplot, scatter plot | source | 10 |
| 116 | Standard Analyses and Code Sharing Working Group Update | Nancy Brucken, Dante Di Tommaso, Jane E. Marrer, Mary Nilsson, Jared Slain, Hanming Tu |
This poster updates the community on the efforts of the six project teams in the PHUSE Standard Analyses and Code Sharing Working Group. The Working Group publishes recommended analyses of clinical data suitable across therapeutic areas. These publications include presentations (tables, listings and figures) of the results from those analyses. The Working Group's GitHub repository contains a wealth of scripts that have been written by PHUSE members, or developed and contributed by the FDA and other organizations. The collaborative efforts of this group improve our collective efforts to design and implement transparent and robust analyses of our clinical data for regulatory decision making. Leveraging crowd-sourcing code development of these recommended analyses can promote access to and adoption of these analyses, and lead to better data interpretations and increased efficiency in our clinical drug development and review processes | quality control, clinical trial, data cleaning, clinical data, adam dataset, cdisc | source | 5 |
| 117 | Standardised MedDRA Queries (SMQs): Programmers Approach from Statistical Analysis Plan (SAP) to Analysis Dataset and Reporting | Sumit Pratap Pradhan | Standardised MedDRA Queries (SMQs) are groupings of MedDRA terms, ordinarily at the Preferred Term level that relate to a defined medical condition. SMQ information is provided by MedDRA in the form of SMQ files (SMQ_LIST, SMQ_CONTENT) and Production SMQ Spreadsheet. One of them should be used to create look-up table/SAS dataset which will be merged with adverse event dataset to derive SMQ information in analysis dataset which will be further used to do reporting. Now the question is how this SMQ information has to be implemented at study level - how many SMQs will be involved? Is Customized Query also involved? Which reports should be generated based on SMQ? Answer to these questions can be found in SAP. In this paper all the topics have been covered step by step with examples that will help even novice programmer to understand and implement SMQ at study level. First basics of SMQ (Narrow and Broad Scope, SMQ Category, Algorithm Search, Hierarchical Structure) has been explained | dataset, adverse events, excel, sample, adam dataset, data science, cdisc | source | 18 |
| 118 | Standardizing Patient Reported Outcomes (PROs) | Charumathy Sreeraman | Any health outcome directly reported by the subject in the trial is referred to as Patient reported Outcome (PROs). It is an addendum to the data reported by the investigator and/or study staff who are conducting the trial. Patient -reported data helps in better understanding of the subject's perspective. In addition to providing physiological ef fects, it is critical in evaluating the safety and efficacy of a drug administered. The patient-reported data is typically collected through subject diary. Subject diary, often called patient diary, is a tool used during a clinical trial or a disease treat ment to assess the patient's condition or to measure treatment compliance. The use of digitized patient -reported data, or patient -reported data, is on the rise in today's health research setting | clinical trial, concomitant medications | source | 7 |
| 119 | Successful US Submission of Medical Device Clinical Trial using CDISC | Phil Hall | 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 FDA submission of the Edwards Lifesciences' PARTNER 3 trial 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 approach | clinical trial, clinical data, adverse events, sdtmig, sdtm domain, cdisc | source | 6 |
| 120 | Supplementary Steps to Create a More Precise ADaM define.xml in Pinnacle 21 Enterprise | Majdoub Haloui, Hong Qi |
The Analysis data definition document, ADaM define.xml, is a required document in a regulatory submission package. It provides necessary information to describe the submitted ADaM datasets and their variables. A high quality define.xml is important for a smooth review process. Pinnacle 21 Enterprise enables the automation and standardization to generate a high quality define.xml. However, due to certain limitations of the current version of Pinnacle 21 Enterprise software, extra steps are needed to create a more precise define.xml after the import of ADaM datasets specification. These steps will generate an ADaM define.xml with better descriptions of attributes, controlled terms, and the source for certain variables. In this paper, the authors will introduce detailed steps leading to a more accurate ADaM define.xml file | adam dataset, length statement, controlled terminology, data cleaning, regulatory submission, cdisc | source | 10 |
| 121 | SUPPQUAL Datasets: Good Bad and Ugly | Sergiy Sirichenko | SUPPQUAL datasets were designed to represent non-standard variables in SDTM tabulation data. There are many recent discussions about whether the SDTM Model should allow the addition of non-standard variables directly into General Observation Class domains instead of using SUPPQUAL datasets? However, there is still a lack of implementation metrics across the industry to understand actual utilization of SUPPQUAL datasets. In this presentation we will summarize metrics from many studies and different sponsors to produce an overall picture of utilization of SUPPQUAL datasets by the industry. We will analyze commonly used SUPPQUAL variables for being potentially promoted to standard SDTM variables. Also, we will provide and discuss examples of correct and incorrect utilization of SUPPQUAL datasets in submission data to understand if the industry is ready to switch from SUPPQUAL datasets to non-standard variables? | trial elements, adverse events, regulatory submission, data cleaning, cdisc | source | 14 |
| 122 | Tackle Oncology Dose Intensity Analysis from EDC to ADaM | Song Liu, Mijun Hu, Jieli Fang, Cindy Q. Song, Quting Zhang |
Dose intensity involves a couple of critical parameters: planned dose intensity (e.g. mg/m2/cycle in the sample study) which is planned dose per cycle as defined in the protocol; the actual dose intensity (ADI), defined as the total cumulative dose taken divided by the treatment duration. Without the Body Surface Area (BSA) being available in the electronic database (EDC), dose intensity derivation becomes complicated when we need to standardize the actual dose taken, EXDOSE (mg/m2/day) in the SDTM domain of EX. The treatment usually goes on continuously for several days and there could be dose interruptions in the middle. It becomes more challenging to calculate EXDOSE when we need to reset weight baseline after significant weight change happened. As a backup, we requested BSA at each visit from the blinded IRT data for reference when weight is missing | dose intensity, sample, cdisc | source | 7 |
| 123 | TDF - Overview and Status of the Test Data Factory Project, Standard Analyses & Code Sharing Working Group | Nancy Brucken, Peter Schaefer, Jessica Dai, Cynthia Stroupe, Dante Di Tommaso |
Test Data Factory (TDF) is one of seven projects within PhUSE's Standard Analyses and Code Sharing Working Group (SACS). Suitable test data are an essential part of software development and testing. The objective of the TDF project is to provide up-to-date and CDISC-compliant test data sets to empower statistical programmers and software developers. Users, primarily software developers and quality control staff, should be able to customize fundamental aspects of test databases | clinical trial, xls, excel, cdisc | source | 4 |
| 124 | Text Wrangling with Regular Expressions: A Short Practical Introduction | Noory Y. Kim | Regular expressions provide a powerful way to find and replace patterns in text, but their syntax can seem intimidating at first. This paper presents a few simple practical examples of using a text editor to adapt text from SDTM or ADaM data set specifications for insertion into SAS programs. This paper is intended for SAS users of any skill level. No prior knowledge of regular expressions is needed | format statement, label statement, cdisc | source | 7 |
| 125 | The Art of Work Life Balance | Darpreet Kaur | source | 5 | ||
| 126 | Transpose Procedure: Turning it Around Again | Janet E. Stuelpner | In the life science industry, CDISC standards dictate that we keep the data for several domains in a vertical format. It is very efficient to have this format to store the data in this way as there is less waste of space. In order to create our tables, listings and figures, we need to transform or transpose the data into a horizontal format. This is a more efficient way to analyze the data. There are many ways in which this can be done. The purpose of the TRANSPOSE Procedure is to reshape the data so that it can be stored as needed and then analyzed in the easiest way possible. PROC TRANSPOSE is the easiest and most complex procedure in SAS. It has only has five options. This paper will revisit how to change the format of the data. You will be taken from the easiest way of doing the transformation without any options to a more complex manner using many options |
proc transpose, proc step, out=, by group processing, character variable, numeric variable, cpu time, vital signs, proc sort, life sciences, cdisc | source | 8 |
| 127 | Trial Sets in Human Clinical Trials | Fred Wood | The Trial Sets domain specification has been included in the Study Data Tabulation Model (SDTM) since Version 1.3, published in 2012; however, the only implementation guide in which it appears is the SEND ((Standard for the Exchange of Nonclinical Data) Implementation Guide (SENDIG). The Trial Sets dataset (TX) was created to allow the subsetting of subjects within an Arm (treatment path) as well as facilitating the "grouping" multiple Arms together. A Trial Set represents the most granular subdivision of all the experimental factors, treatment factors, inherent characteristics, and distinct sponsor designations as specified in the design of the study. Within a nonclinical trial, each animal is assigned to a Set in addition to an Arm. The Set Code (SETCD) variable is Required in the SEND DM dataset | clinical trial, dose, randomization, cdisc | source | 13 |
| 128 | Untangling the Subject Elements Domain | Christine McNichol | The Subject Elements (SE) domain is unique and challenging in its sources and mapping. Without much encouragement, a map of the sources, derivations and interaction between records in SE can start to look more like a tangled mass of spaghetti than common linear SDTM mapping. Why is this? SE can have multiple sources for the data points needed to derive each element's start and end. Compared to other domains, SE also has a good deal more direct correlation to values in other domains. For successful implementation, it is critical to understand SE's purpose and requirements, the unique mapping path from source to SE and how this differs from other common domains, the steps needed to successfully derive SE, and programming methods that can be used. This paper will explore the inner workings of SE and explain how to successfully create SE one manageable bite at a time | raw data, date variable, sdtm domain, sdtm programming, randomization, medical history, cdisc | source | 8 |
| 129 | Updates in SDTM IG V3.3: What Belongs Where - Practical Examples | Lucas Du, William Paget, Lingyun Chen, Todd Case |
CDISC SDTM Implementation Guide (IG) Version 3.3 was released on November 20th 2018. New domains and implementation rules have been added to standardize SDTM implementation within the industry. A lot of information has been updated since the release of Version 3.2 five years previously and understanding all these updates presents a great challenge for people working in Pharma/Biotech. For example, what are the new domains and how should we use them? Furthermore, the same information could be mapped to different domains which have slightly different purposes, how should we decide which domain the information should go to? Version 3.3 also provides additional guidance to resolve some known issues. For example, under Version 3.2 text variables with length greater than 200 characters have the additional text mapped to the corresponding supplemental (SUPP) domains but the labels for these SUPP variables vary within the industry | sdtm domain, medical history, sample, cdisc | source | 8 |
| 130 | Using Data-Driven Python to Automate and Monitor SAS Jobs | Julie Stofel | This paper describes how to integrate Python and SAS to run, evaluate, and report on multiple SAS programs with a single Python script. It discusses running SAS programs in multiple environments (Latin- 1 or UTF-8 encoding; command-line or cron submission) and ways to avoid potential Python version issues. A handy SAS-to-Python function guide is provided to help SAS programmers new to Python find the appropriate Python method for a variety of common tasks. Methods to find and search files, run SAS code, read SAS data sets and formats, return program status, and send formatted emails are demonstrated in Step-by-Step instructions. The full Python script is provided in the Appendix | sysrc, format statement, dataset, informat statement, sample | source | 15 |
| 131 | Using R Markdown to Generate Clinical Trials Summary Reports | Radhika Etikala, Xuehan (Emily) Zhang |
The scope of the paper is to show how to produce a statistical summary report along with explanatory text using R Markdown in RStudio. Programmers write a lot of reports that describe the results of data analyses. There should be a clear and automatic path from data and code to the final report. R Mar kdown is ideal for this as it is a system for combining code and text into a single document. It is also an efficient, user-friendly tool for producing reports that do not need constant updating. RStudio is often used in the Pharmaceutical and Healthcare industries for analysis and data visualization, and the R Markdown tool can also be leveraged for creating reports and datasets for submission to regulatory agencies. This paper presents an RStudio program that demonstrates how to use R Markdown to generate a statistical table showing adverse events (AE) by system organ class (or preferred term) and severity grade along with text that explains the table | adverse event summary, adverse events, integer | source | 10 |
| 132 | Using SAS Simulations to determine appropriate Block Size for Subject Randomization Lists | Kevin Venner, Jennifer Ross, Kyle Huber, Noelle Sassany, Graham Nicholls |
ICH E9 (Regulatory) guideline specifies that the minimum block size should be avoided for the generation of Randomization lists to avoid predictability / selection bias and to avoid full or partial unblinding to treatment assignment. Additionally, knowledge of the block size used within the Randomization list should be restricted (typically known by Sponsor Biostatistician only). However, in reality Sponsor Biostatisticians repeatedly utilize the same (default) block size for list generation (e.g. always use a block size of 4 for x2 treatments with equal allocation ratio) and/or utilize the minimum block size. While Treatment balance for a clinical trial can be critical for establishing effectiveness, perfect Treatment balance is not required. The goal of randomization is to maintain the integrity of the study (keeping study blind, avoiding bias) and to achieve acceptable balance | randomization, macro definition, macro variable, open label, sample, clinical trial, macro parameter | source | 10 |
| 133 | Using SAS to Create a Build Combinations Tool to Support Modularity | Stephen B. Sloan | With SAS PROC SQL we can use a combination of a manufacturing Bill of Materials and a sales specification document to calculate the total number of configurations of a product that are potentially available for sale. This will allow the organization to increase modularity and reduce cost while focusing on the most profitable configurations with maximum efficiency. Since some options might require or preclude other options, the result is more complex than a straight multiplication of the numbers of available options. Through judicious use of PROC SQL, we can maintain accuracy while reducing the time, space, and complexity involved in the calculations |
proc sql, excel | source | 7 |
| 134 | What's Your Favorite Color? Controlling the Appearance of a Graph | Richann J. Watson | The appearance of a graph produced by the Graph Template Language (GTL) is controlled by Output Delivery System (ODS) style elements. These elements include fonts and line and marker properties as well as colors. A number of procedures, including the Statistical Graphics (SG) procedures, produce graphics using a specific ODS style template. This paper provides a very basic background of the different style templates and the elements associated with the style templates. However, sometimes the default style associated with a particular destination does not produce the desired appearance. Instead of using the default style, you can control which style is used by indicating the desired style on the ODS destination statement. However, sometimes not a single one of the 50-plus styles provided by SAS achieves the desired look. Luckily, you can modify an ODS style template to meet your own needs. One such style modification is to control which colors are used in the graph |
bar chart, proc template, proc step, sample, series plot, output delivery system | source | 33 |
| 135 | Why Are There So Many ADaM Documents, and How Do I Know Which to Use? | Sandra Minjoe | As of this writing, the CDISC website has the following ADaM documents for download: a model document, three versions of the implementation guide, an adverse event data structure, an occurrence data structure, a time-to-event document, a document with examples in commonly used statistical analysis methods, an analysis result metadata document, conformance rules, and an important considerations document. Additionally, you can download three release packages, each containing a subset of these documents. This paper describes why there are so many documents, walks through basic information contained in each, and makes recommendations of which set of documents to use in which circumstances | adverse events, bds, data science, survival analysis, excel, concomitant medications, medical history, cdisc | source | 10 |
| 136 | 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 the 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 failures. 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 cu rrent condition) automatic macro variable is demonstrated, facilitating the programmatic identification of warnings and runtime errors | exception handling, sysrc, sas log, macro put, missing value, abort, sas base, cpu time, macro function, macro variable, macro definition, proc sort | source | 11 |
| 137 | You Are Using PROC GLM Too Much (and What You Should Be Using Instead) | Peter L. Flom, Deanna Naomi Schreiber-Gregory |
Ordinary least square regression is one of the most widely used statistical methods. However, it is a parametric model and relies on assumptions that are often not met. Alternative methods of regression for continuous dependent variables relax these assumptions in various ways. This paper will explore PROCS such as QUANTREG, ADAPTIVEREG and TRANSREG for these data | regression analysis, proc step, proc quantreg, response variable, proc reg, predictor variable, proc adaptivereg, interval variable | source | 15 |
| 138 | You down to QC? Yeah, You know me! | Vaughn Eason, Jake Gallagher |
The successful delivery of clinical trial-related analysis datasets and outputs are heavily dependent on an efficient and fluid relationship between the Production Programmer, QC Programmer, and Lead Statistician. Given the increasing complexity and rapidity of project delivery, coupled with multiple regulatory standards that must be adhered to; a sound strategy and clear model of communication are paramount to a study's overall quality. Understanding the pressure and stress associated with project deadlines and nuanced sponsor requirements will help navigate around communication shortfalls. Further understanding of each role's subjective nature will help outline a succinct archetype to achieving high-quality results with minimal headaches. The three main barriers of communication between Production Programmer, QC Programmer, and Lead Statistician are vague understanding of client expectations, overall study comprehension and the rarely considered, Ego factor | clinical research, data cleaning | source | 5 |
This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.
The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.
SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.