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BUGSPharmaSUG 2022 Papers - Index
145 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | A Beginner's Guide to Create Series Plots Using SGPLOT Procedure: From Basic to Amazing | Aakar Shah, Tracy Sherman |
As a beginner SAS programmer, it can be intimidating to work on graph assignments. SAS ODS Graphics provides an avenue for SAS users to create visually pleasing graphs very quickly. However, ODS Graphics documentation is extensive and cumbersome. This paper can help beginner SAS programmers on their journey from creating a basic series plot using a simple SGPLOT statement to amazing, formatted graphs which can be shared with upper management or in a conference presentation. Knowledge learned from this paper can be further applied to other types of graphs |
proc step, proc sgplot, macro variable, series plot, output delivery system | source | 18 |
| 2 | A Dynamic Data Visualization Report for Efficacy Endpoints of Early Oncology Study | Hong (Lily) Zhang, Yixin Ren, Huei-Ling Chen |
In early oncology studies with solid tumors, efficacy analysis usually focuses on tumor size change and the best overall response of the drug treatment effect. Clinical clinicians and statisticians analyze the drug effect based on the summarized results and individual patient -level analys es. When they have questions on certain data points, t hey prefer more detailed individual patient data to understand the data better . The clinical programmers often work with the clinicians and statisticians in the data research effort by providing requested data. This paper presents a macro tool that creates a dynamic visualization specifically for oncology studies with solid tumors to avoid repeated programming efforts to answer requests from clinicians and statisticians. This macro tool connects the patient's solid tumor size, response, and any critical events in the study to produce patient-level listing worksheets in EXCEL file format |
best response, sdtmig, disease response, efficacy summary, excel, proc transpose, adam dataset, cdisc | source | 10 |
| 3 | A Multilingual Shiny App for Drug Labelling in Worldwide Submission | Jinchun Zhang, Aiming Yang, Yiwen Luo, Nan Xiao, Yilong Zhang |
In preparing a successful worldwide submission for a drug or vaccine, one of the critical steps is to provide drug labelin g in different languages and different formatting requirements to worldwide regulatory agenc ies. If a drug label contains figures, there is a challenge to create and maintain drug labels in different languages programmatically. For example, in oncology studies, Kaplan-Meier Plot (K-M plot) is frequently required for drug labelling worldwide. An application that can generate the figure in the required language s with controlled labeling process is highly desired. It ensures accuracy, consistency, and security of the data. In this paper, we demonstrate a user-friendly Shiny app to simplify the labeling process in collaboration with local regulatory teams who need to update drug label s in local languages. The Shiny app simplifies the manual steps to re-create K-M plots in different languages with different formatting requirements and enables advanced plot rendering | treatment arm, survival analysis | source | 13 |
| 4 | A Practical Approach to Preparing Documentation for Clinical Registries | Jennifer McGrogan, Rex Tungala, Mario Widel |
Clinical trial sponsors must comply with a wide variety of legal requirements. The focus of this paper is the requirement to publicly register and post clinical trial information, including trial results. Depending on where the trial is conducted, there are different clinical registries that need to be supported. The processes are documented on the corresponding websites where the trials must be registered. While it is possible to manually calculate and enter trial results into the registry, that information must be accurate and manual entry increases the review burden and the risk of human error. Programming support can make the process safer and easier. More specifically, for some content it is possible to directly upload validated content, avoiding the risks associated with manual entry. In other cases, manual entry and review can be facilitated with validated tables that specifically target entry needs | adverse events, excel, clinical trial, demographics table, serious adverse event, standard deviation, macro definition | source | 16 |
| 5 | A Programmer's Perspective on Patient reported Outcomes in Oncology Trials | Divya Vemulapalli | Patient reported outcomes (PRO) are used in clinical trials to assess the quality of life from the patient's perspective. Patients are asked to respond to questionnaires related to physical functioning, adverse effects of treatment, and the overall burden of such adverse effects. In addition to overall quality of life, PROs also measure treatment benefit or risk and are helpful in supporting the labelling claims and reimbursement positioning for the drug. Since PROs are directly reported by the patient, they have gained increased utility for determining the quality-of-life endpoints of a clinical trial. This paper takes a closer look at the data collection process and the issues we may come across in PRO data in the context of clinical trials in an oncology setting. It also discusses some of the common PRO instruments in such trials and statistical analysis methods for those instruments | clinical trial, missing value, adverse events, data science, clinical research, proc freq | source | 7 |
| 6 | A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software | Stephen B. Sloan, Kirk Paul Lafler |
Data comes in all forms, shapes, sizes and complexities. Stored in files and datasets, SAS users across industries recognize that data can be, and often is, problematic and plagued with a variety of issues. Data files can be joined without problem when each file contains identifiers, or "keys", with unique values. However, many files do not have unique identifiers and need to be joined by character values, like names or E-mail addresses. These identifiers might be spelled differently, or use different abbreviation or capitalization protocols | proc step, proc sql, character variable, proc sort, data cleaning, sas function, missing value, numeric variable, order by, data transformation, proc format, proc contents, proc print, character functions, proc transpose | source | 29 |
| 7 | A SAS Macro to Aid Assembly Review of M5 Module in Submission | Jeff Xia | Assembly Review is the final check of the published electronic regulatory submission documents performed by the submission's content authors/owners prior to release to health authorities and/or subsidiaries. Normally the programming group is the content owner of all data components, which includes SAS datasets in XPT format, annotated case report form, define documents as well as the reviewer's guide, etc. in m5 module for all submitted studies. Therefore, the programming group is responsible to perform a thorough assembly review to ensure correctness and completeness of each data component in m5 module. Traditionally this was done by manually comparing the list of file names in each subfolder in m5 module in a published sequence against the corresponding folder in the final drop off location, and catching the difference, if any, to avoid any mistakes/overlooks in the regulatory publishing process | regulatory submission, macro definition | source | 5 |
| 8 | A SAS Macro to Support the Supplemental Qualifier Section in cSDRG | Jamuna Purma, Bindya Bindya Vaswani |
The programing group is responsible for completing the Clinical Study Data Reviewer's Guide (cSDRG). The cSDRG should include a high-level overview of the SDTM data submitted for the clinical study. The PHUSE template used to generate the cSDRG contains a supplemental qualifier section 3.4. Traditionally, this section was filled in by manually checking each dataset tab in the analysis dataset (ADaM) specification document, to identify the supplemental domain variables included in the statistical analysis. This paper introduces a SAS macro that identifies supplemental qualifier variables used in supporting analyses. It creates a table with required columns that can be easily transferred into the cSDRG section 3.4. Additionally, this macro flags any supplemental qualifier variables specified in the ADaM define document used in generating analysis datasets, but not in SDTM domains, to ensure the consistency between cSDRG section 3.4 and ADaM programming specification | excel, adam dataset, call execute, clinical trial, macro definition, sdtm domain, proc step, cdisc | source | 9 |
| 9 | A SAS macro wrapper for an efficient Deming regression algorithm via PROC IML: The Wicklin method | Natasha Oza, Ben Wang, Jesse Albert Canchola |
Deming regression is one tool in method comparison/correlation studies and is used to compare two or more quantitative measurements from two similar methods that produce like - measurements on the same subject/sample on the linear scale where both methods can be deemed to have error (Deming, 1943). Contrast this with linear regression (viz., Y=mX+b+error) where only the dependent variable Y is deemed to have error and the X variable is fixed (i.e., without error, for example, an expected value). At least two published SAS macros exist at this time that perform Deming regression (Deal, Pate, and El Rouby in 2009 and Njoya and Hemyari in 2017). However, these macros can lengthen the time to final result as they both use a mac ro loop to perform the jackknife or bootstrap. The method proposed by Wicklin (2019) is deemed as substantially more efficient as it uses SAS/IML with explicit formulas to compute the slope and intercept of the Deming regression line |
macro definition, regression analysis, sample, estimate, proc step, proc sgplot | source | 13 |
| 10 | A Shiny App to Spell Check an ADaM Specification | Simiao Ye, Jeff Xia |
The ADaM define.xml is a required file that describes the structure and content (datasets, variables, value level metadata, and controlled terminologies) of t he data in a submission package. To produce a high quality define.xml file, Pinnacle 21 Enterprise (P21E) is often a preferred tool for the compliance check and validation of CDISC standards. However, the features to automatically check spelling errors, SAS syntax, extra space, and traceability of SDTM variables are not implemented in P21E and may demand excessive attention and efforts from the ADaM define.xml authors. This paper describes a Shin y application that can read through various input files including the SDTM define.xm l, ADaM specification spreadshee t, and user-defined dictionaries and perform an automatic spell check to ensure document readability and eliminate manual efforts in identifying the spelling errors | sas syntax, adam dataset, controlled terminology, excel, cdisc | source | 9 |
| 11 | A Standardized Reviewer's Guide for Clinical Integrated Analysis Data | Srinivas Kovvuri, Kiran Kumar Kundarapu, Satheesh Avvaru, Randi McFarland |
The Integrated Summary of Safety (ISS) and Integrated Summary of Efficacy (ISE) are vital components of a successful submission for regulatory review in the pharmaceutical and biotechnology industry. The submission package includes integrated Data Reviewer's Guide (DRG) along with integrated database(s) (ISS and/or ISE) that allow reviewers to better understand traceability of the data, pooling strategy, analysis considerations, subject-specific significant information, and conformance findings. The PHUSE Analysis Data Reviewer's Guide (ADRG) applies to a single study. Sponsors have been adopting this template and making amendments when creating a reviewer's gu ide for an integrated analysis data submission. PHUSE recognized the need for a new, standardized template and completion guidelines for an integrated analysis data reviewer's guide | source | 11 | |
| 12 | A Tale of Two CDISC Support Groups - Supporting CDISC Standards is Anything but Standard | Christine McNichol, Mike Lozano |
It was the cleanest compliance report. It was the worst of compliance reports. It was the team of subject matter experts. It was the team mapping their first SDTM study. It was the project with well tested standard process used for years, it was the project without any developed standards. Any standards support team has likely been faced with supporting many of these. Their overall goal is the same: to support teams in the implementation of CDISC standards such as SDTM and ADaM in order to produce a high-quality compliant product following CDISC standards for submission. However, the challenges, structures and approaches to support can differ greatly between companies. As a result, CDISC standards support teams come in many different shapes and sizes with different processes and methods of support. This is the tale of two CDISC support groups, one Sponsor and one CRO, navigating these challenges with converging paths to one common goal | clinical trial, gcp, cdisc | source | 9 |
| 13 | Above and Beyond Lesion Analysis Dataset (ADTL) | Raghava Pamulapati | A subject's lesion response is often a primary efficacy measure in Oncology studies. A lesion analysis dataset is derived from Tumor Identification (TU) and Tumor Results (TR) SDTM domains. These two SDTM datasets contain the subject 's Target, Non-Target, and New Lesions information collected at numerous evaluation time points throughout the study. Each patient can have multiple target and / or non- target lesions. The image and measurement of those lesions are collected at each time point along with new lesion information if any. To evaluate the tumor responses to the investigational treatment throughout the study, we developed an analysis dataset (ADTL) with a single record per subject per time point. Those records contain information such as the sum of diameter of target lesions and percentage change from baseline | change from baseline, adam dataset, tumor identification, sdtm domain, cdisc | source | 11 |
| 14 | ADaM Child Data Set: An Alternative Approach for Analysis of Occurrence and Occurrence of Special Interest | Lindsey Xie, Richann J. Watson, Jinlin Wang, Lauren Xiao |
Due to the various data needed for safety occurrence analyses, the use of a child data set that contains all the data for a given data point aids in traceability and support of the analysis. Adverse Events of Special Interest (AESI) represents adverse events (AEs) that are of particular interest in the study. These AESI could potentially have symptoms associated with them. AESI could be captured as clinical events (CEs) in the CE domain while the associated symptoms for each CE are captured as AEs in the AE domain. The relationship between CEs and associated AE symptoms are an important part of the safety profile for a compound in clinical trials. These relationships are not always readily evident in the source data or in a typical AE analysis data set (ADAE). The use of a child data set can help demonstrate this relationship, which provides enhanced data traceability to help the review effort for both sponsor and regulatory agency reviewers | ctcae grade, adverse events, clinical trial, system organ class, data lineage, adverse event summary, raw data, imputation | source | 15 |
| 15 | ADCM Macro Design and Implementation | Chengxin Li, Tingting Tian, Toshio Kimura |
Previous work explained in detail a set of macros that automated the development of safety ADaM datasets. As an extension to that previous work, this paper delves into the design and implementation of a macro that will generate the ADCM dataset. This paper will broaden the focus to include the end-to-end streamlined data processing perspective from data collection to analysis and thus will discuss some of the upstream and downstream components including data collection and SDTM considerations as well as the analysis need that ultimately drives the ADCM requirements. Since the ADCM macro incorporates analysis need, the resulting ADCM will be analysis-ready in supporting common concomitant medication analyses while allowing for traceability back to SDTM. The GPS (Global, Project, Study) method was applied to the macro development. Global variables are derived without user input, whereas project and study specific variables are controlled by macro parameters | concomitant medications, data wrangling, imputation, adam dataset, macro call, macro parameter, sdtm domain, cdisc | source | 7 |
| 16 | Advanced Project Management beyond Microsoft Project, Using PROC CPM, PROC GANTT, and Advanced Graphics | Stephen B. Sloan, Lindsey Puryear |
The Challenge: Instead of managing a single project, we had to craft a solution that would manage hundreds of higher- and lower-priority projects, taking place in different locations and different parts of a large organization, all competing for common pools of resources. Our Solution: Develop a Project Optimizer tool using the CPM procedure to schedule the projects and using the GANTT procedure to display the resulting schedule. The Project Optimizer harnesses the power of the delay analysis feature of PROC CPM and its coordination with PROC GANTT to resolve resource conflicts, improve throughput, clearly illustrate results and improvements, and more efficiently take advantage of available people and equipment |
proc step, proc sql, excel | source | 10 |
| 17 | Agile innovation - an adaptive approach to transformation in Clinical Research Organizations | Aman Bahl, Hrideep Antony |
Healthcare organizations today need to be agile in the age of digital transformation. Creativity, innovation, and sustainability are some of the key skills that organizations today need to be continually adaptive. Healthcare organizations are under mounting pressure to improve quality and reduce expenses. Agile helps the organization to quickly respond and adapt to ever-changing customer needs and evolving research requirements. Agile relies on iterative improvement, encouraging adaptability and quick responses to validated feedback. This process can help in integrating design with development and adapt continually to reduce the innovation time | clinical trial, clinical research, adaptive design, clinical data | source | 10 |
| 18 | An Expanded Set of SAS Macros for Calculating Confidence Limits and P-values Under Simon's Two-Stage Design Accounting for Actual and Planned Sample Sizes | Mikhail Melikov, Alex Karanevich |
Simon's two-stage designs are popular single-arm, binary-endpoint clinical trials that include a single interim analysis for futility. In the literature, it is widely acknowledged that the inferential statistics calculated (p-values, point estimates, and confidence limits) in Simon's designs typically do not account for when the actual sample size differs from the planned sample size at stage two. Koyama and Chen (2008) provided methods to calculate proper inferential statistics for studies where the planned sample size at stage two is either the same or different from the actual sample size. Our previously published SAS macro implemented these methods for the case where the pla nned sample size matched the actual sample size | sample size, sample, stage 2, macro definition, inferential statistics, confidence interval, macro call, stage 1, type i error, binomial distribution | source | 8 |
| 19 | Analysis of sample size calculations in clinical trial - errors, pitfalls and conclusions | Igor Goldfarb, Ritu Karwal, Xiaohua Shu |
The goal of this work is to increase awareness of investigators, medical writers and statisticians working on the development, planning and conduction of the randomized clinical trial (RCT) about real importance of the statistical sections of the corresponding study protocols describing calculations of the sample size for this trial. Every RCT is carefully and thoroughly planned from its very early stage. This plan typically includes the main objectives of the trial, primary and secondary endpoints, method of collecting the data, sample size with scientific justification, methods of handling data, statistical methods and assumptions. This plan represents a natural part of the Protocol of RCT. One of the key aspects of this plan is an esti mation of the required sample size - a number of subjects to be enrolled in the RCT | sample size, sample, proc power and sample size, clinical trial, type ii error, effect size, type i error, null hypothesis, phase ii, confidence interval, standard deviation | source | 14 |
| 20 | Applying Agile Methodology to Statistical Programming | Gina Boccuzzi | When working in a CRO environment, Waterfall project management methodologies are often the default and seem like the only way to guide a project. While a full transition to Agile-based project management may not always be practical or the right fit for every workplace, many Agile methodologies feature practices that can be easily implemented to optimize daily workflow. These tools include: Daily Standup, a daily team meeting to discuss progress and upcoming work; Sprint Planning/Retrospective, bi -weekly meetings to discuss the work needed in the coming weeks; Post-Mortem, a meeting at the end of a project to discuss how it went and what could have been done differently; and Cyclical Timelines, working on different components of a project at the same time rather than waiting to start one until the last is finished. Regardless of the overarching project management style, these tools can be implemented individually or in tandem, depending on the user's needs | sdtm programming, raw data, cdisc | source | 5 |
| 21 | Auto Extraction of the Title Description in Table Report | Xingshu Zhu, Li Ma, Bo Zheng |
There are times during the completion phase of a study when programmers are asked to provide a full list of all table reports in Rich Text Format and their descriptive titles to help with preparing the Clinical Study Report or publications. When the number of total reports is small, this request is simple and can be quickly completed manually by opening each report and copying the title over. However, when there are hundreds of reports or more, it becomes a daunting task where a manual process is impractical and can dramatically increase the risk of mistakes. This paper shares an approach that utilizes RTF control words to programmatically identify the descriptive titles for a list of table reports and outputs the results to an EXCEL file that contains the report names, titles, and folder location links. A report in RTF format that contains multiple tables is out of scope in this paper. PC SAS version 9.4 (TS1M4) and Microsoft 365 MSO were used in this paper |
do loop, macro variable | source | 7 |
| 22 | Automatic CRF Annotations Using Python | Lijun Chen, Jin Shi, Tong Zhao |
The annotated CRF is a required component in the submission package sent to FDA. However, the CRF annotation is usually a monotonous and time -consuming manual process, and some existing automatic packages still have limitations. Taking the advantage of all the powerful functions Python can carry, we have developed a Python package to automatically annotate CRF pages. The package is designed to work on a blank CRF that is for a version update or that comes from a new study. The whole process consists of four steps, kicked off with optional information extraction from an old annotated CRF, followed by reading and mapping new CRF to provide input for the next step of annotating CRF, and lastly bookmarks can be optionally added. At a few check points, the EXCEL spreadsheets are used to allow manual controls to ensure accuracy. The current performance of the package is acceptable, and more explorations have been planned |
excel, annotated crf, sample, cdisc | source | 12 |
| 23 | Automation of Clinical Data extracts using Cloud Applications | Syam Prasad Chandrala, Madhusudhan Nagaram, Chaitanya Chowdagam, Jegan Pillaiyar, Kunal Chattopadhyay |
Data managers and external vendors post the data, in the form of zip files, into various storage locations such as BOX, SharePoint, sFTP, etc. Some of these data extracts are scheduled periodically, and some are scheduled bimonthly or on ad hoc basis. In addition to this, these data extracts could be in various formats and most of the time the files are password protected. It is the programmer's responsibility to make sure that current data and historic data are stored in the appropriate folders and in a consistent format such as SAS7BDAT as a source for creating SDTM, ADaM datasets. Programmers spend a lot of time and effort managing this process manually. By leveraging the Cloud-based applications such as AWS secrets manager, AWS lambda functions in combination with python scripting, SAS program, and scheduling shell scripts, we have made this process automated and serverless | data lake, adam dataset, cdisc | source | 9 |
| 24 | Automation of Variable Name and Label Mapping from Raw Data to Analysis Data Standards | Xingshu Zhu, Bo Zheng, Li Ma |
Raw data files with no standard variable naming and labeling conventions are a common occurrence in non-interventional primary data collection studies. As real-world data gains more relevance in the pharmaceutical industry, it becomes important to have a repeatable and consistent proces s to map raw variable names and labels to standardized versions for analysis and reporting. This paper shows two methods for importing and mapping variables in SAS to reduce the time spent on data management. One method leverages the built-in features of SAS PROC IMPORT as a simple direct solution for importing and mapping raw data to predefined standards. The second method uses SAS to extract the relevant metadata from a raw file to create an EXCEL mapping template and facilitates collaboration with non-SAS users by making it easier for them to access and comment on the mapping process |
excel, proc step, proc import, raw data, dataset, macro variable, out=, clinical programming, proc sql | source | 9 |
| 25 | Automation using SAS Makes it Easy to Monitor Dynamic Data in Clinical Trial | Wenjun He | A successful clinical trial is the teamwork composed of study startup, conduct, data collection, monitoring and data cleaning, among which it is critical that the study is implemented as specified in the protocol and the data is clean and of good quality. As most electronic data capture (EDC) system is setup to export data as SAS datasets, the clinical SAS programming team can leverage these datasets and provide metrics and tools to facilitate dynamic data monitoring, improving the quality of research study data and enhancing human subject protection especially in risk-based monitoring. This paper presents example SAS code using SAS macros and other SAS facilities to automate tracking enrollment progression, real time site metrics reporting, and safety-based medical monitoring, which can benefit EDC clinical reporting and trial management processes | clinical trial, edc, proc sql, output delivery system, odbc connection, ods html, macro definition, clinical data, excel, randomization, adverse events | source | 9 |
| 26 | Avoid Common Mistakes in Preparing the BIMO Deliverables | Chintan Pandya, Himanshu Patel |
The purpose of the BIMO package is to verify the integrity of the data submitted and confirm that all regulations for clinical trials are being met. The data package is required for pivotal studies and should include information to support your application's safety and efficacy endpoints. It is important to pay special attention when creating a BIMO package and avoid making mistakes. This paper will discuss common mistakes we encounter and avoid them to create a good quality summary-level clinical site (CLINSITE) dataset, subject-level line listings by Site, and data definition (define.xml). The paper also provides tips on cross-checking the efficacy and safety counts with CSR counts | adverse events, safety population, protocol deviations, serious adverse event, clinical trial, cdisc | source | 9 |
| 27 | Begin with the End in Mind: eCOA data from collection to analysis | Franklin E. Menius, Monali Khanna, Vincent Allibone |
Similar to Stephen Covey's 7 Habits of Highly Effective People, Highly effective clinical trials should "begin with the end in mind". Rigorous analysis of data followed by successful submission to regulatory agencies should be the end goal of data collection. It sounds simple and straightforward, especially when dealing with questionnaire data often associated with electronic Clinical Outcome Assessments (eCOA) but, in an industry with increasing knowledge silos and outsourcing, it often is quite the opposite. Simple ideas like collecting data that are usable for analysis can often get lost in the machinery of running a study. Often data management and statisticians are left trying to make sense of incomplete, dirty, or gasp free text data. This paper will focus on steps that all stakeholders can take to make sure t hat the end goal is kept in mind, from design, to data collection, to analysis, and finally submission | data governance, clinical trial, audit trail, clinical endpoint, data cleaning, missing value, proc freq, data validation rules | source | 7 |
| 28 | Better Business Through Better Records | Evelyn Dyson Lee | source | 17 | ||
| 29 | Bookmarking CRFs More Efficiently | Noory Y. Kim | Preparing an annotated CRF (aCRF) entails adding PDF bookmarks. If done manually, this can be very time-consuming. This paper will show how to automate aspects of this process, including the specification of bookmark properties required by the FDA or CDISC (namely, nested levels and inherit zoom magnification). This paper assumes the reader knows how to edit the Windows PATH environment variable and use a Windows command line | create table, order by, excel, cdisc | source | 6 |
| 30 | Building Dashboards for Data Review and Visualization using R Shiny | John Saida Shaik, Sreeram Kundoor |
In the pharmaceutical and biotech industry, SAS is a widely used software for clinical trial data analysis and to prepare CDISC-compliant data sets for FDA submission. Though SAS is a robust and feature- rich programming language, access to features that enable development of interactive data exploration tools may be beyond the scope of the license of some companies. In scenarios where ad- hoc or exploratory analyses are desired with quick access to data, R, as an open-source software, is suitable and freely available with large community support behind it. Such quick access by cross-functional teams can be especially beneficial for efficacy data in single-arm clinical trials, for which interactive dashboards can easily be built using the Shiny package in R. These dashboards, also known as Shiny web apps, can be tailored to the specific needs of the study team | box plot, class variable, scatter plot, waterfall chart, by group processing, clinical trial, cdisc | source | 13 |
| 31 | Calling for Backup When Your One-Alarm Becomes a Two-Alarm Fire: Developing SAS Data-Driven Concurrent Processing Models through Control Tables and Dynamic Fuzzy Logic | Troy Martin Hughes | In the fire and rescue service, a box alarm (or, simply, "alarm") describes the severity of a fire. As an alarm is elevated from a one-alarm fire to a multi-alarm fire, additional, predetermined resources (e.g., personnel and apparatuses) are summoned to combat the blaze more aggressively. Thus, a five-alarm fire-or its equivalent "five-alarm" Cincinnati chili-represents something extremely hot and dangerous. After extinguishment, and as smoke and embers recede and overhaul begins, fire and rescue resources are released "back into service" so they can be utilized elsewhere if necessary. Essential to managing complex fireground operations, this load balancing paradigm is also common in grid and cloud computing environments in which additional computational resources can be shifted temporarily to an application or process to maximize its performance and throughput | cpu time, proc sql, macro variable, create table, %if-%then-%else | source | 27 |
| 32 | Checking Compliance of CDISC SDTM Datasets Utilizing SAS Procedures. | Ballari Sen | CDISC (Clinical Data Interchange Standards Consortium) SDTM (Study Data Tabulation Model) is a required standard data structure to be used for submitting tabulation data to the US FDA and PMDA and NMPA submissions for clinical trials. An approach for implementing such data standards should be quick and effective with less resource utilization. Compliance Measures are needed to check compliance and streamline operations for the preparation of data for FDA, PMDA and NMPA submission, although there are many good validation compliance tools for CDISC SDTM ready compliance data sets such as Pinnacle 21 Enterprise, SAS - Clinical Standards Toolkit and OpenCDISC Validator [1][2]. However, the reusability, efficiency and automation of SAS provides an effective and customized tool for validating CDISC SDTM compliance data sets to be used in clinical FDA submissions. This paper will discuss how to efficiently produce the validation reports utilizing SAS software utilizing base SAS capabilities | data cleaning, proc compare, sas programmer, sdtm domain, proc step, sdtm programming, clinical sas, clinical trial, flag variable, union all, adverse events, macro definition, cdisc | source | 17 |
| 33 | Clean Messy Clinical Data Using Python | Abhinav Srivastva | Data in clinical trials can be transmitted in various formats such as EXCEL, CSV, tab- delimited, ASCII files or via any Electronic Data Capture (EDC) tool. A potential problem arises when data has embedded special characters or even non-printable (control) characters which affects all downstream analysis and reporting; in addition to being non-compliant with CDISC standards. The paper explores various ways to handle these unwanted characters by leveraging the capabilities of Python language. The discussion begins with correcting these characters using their ASCII code, followed by Unicode, and later exploring normalization techniques such as NFC, NFD, NFKC, NFKD to normalize unicode characters. Often times, data cleaning will require a combination of these techniques to get the desired result |
normalization, excel, cdisc | source | 7 |
| 34 | Clinical Classifications: Getting to know the new kid on the QRS block! | Soumya Rajesh, Michael Wise |
Questionnaires, Ratings, and Scales (QRS) encompass 3 main concepts: Questionnaires (QS), Functional Tests (FT), and Clinical Classifications (RS) - the new kid on the QRS block. As with all things new, there may be some confusion in mapping Clinical Classifications, versus the other members of the QRS family. To add to this confusion, Clinical Classifications have recently been moved from QS to RS - a domain that was originally developed just for Oncology (Disease Response). Knowing the distinction between a Questionnaire and Functional Test, as well as what distinguishes them from a Clinical Classification is critical here. This presentation begins by introducing the members of the QRS family, and provides differences among them, by using a few published instruments for illustration. The Six Minute Walk and AUDIT-SR show how FT differs from QS | disease response, adam dataset, sdtm domain, cdisc | source | 9 |
| 35 | Connecting SAS and Smartsheet to Track Clinical Deliverables | Gregory T. Weber, Steven Hege, Siddharth Kumar |
Efficient monitoring and management of the clinical programming development and validation lifecycle is vital and can be a challenge in clinical study reporting. There are various methods used by companies to track the progress of clinical deliverables. Our current solution is tracking progress using Microsoft EXCEL spreadsheets. In our Statistical Computing Environment (SCE), this is not ideal as it requires checking out, downloading, editing the spreadsheet, and then uploading and checking in the updated EXCEL file. This manual process is both time-consuming and prone to error. In addition, sharing and working collaboratively is problematic as only one user can update the file at a time, so a better solution is desired. In this paper, we discuss connecting Smartsheet with our SAS LSAF environment to provide a more collaborative Clinical Programming Deliverable Tracker requiring less intervention from managers and programmers |
proc http, proc step, clinical programming, macro definition, excel, data cleaning, life sciences | source | 8 |
| 36 | Copy comments: A Dynamic solution to solve the age-old need | Joseph Cooney | Over the years, I have come to know that just about every Data Management team needs Copy Comments functionality. Whether it be achieved through fancy applications, EXCEL macros, or SAS programs, one thing is always certain, DM needs their comments carried from one output to the next. With nearly 15 years of Clinical Data Programming experience spread across multiple employers and sponsors, I have seen what works, and simply, what does not work. I have come up with a solution that trumps each of the methods that I have seen used in the past and requires only Microsoft EXCEL and SAS access with limited programming experience. I have created a SAS Copy Comments macro that when executed prior to exporting data, will dynamically import a listings previous output, compare to current final data set, and copy DM comments |
excel, macro variable, data review, sas output, %if-%then-%else | source | 8 |
| 37 | Cyber Resiliency - Computing Platform Modernization | Susan Kramlik, Eunice Ndungu, Hemu Shere |
This paper describes selected cyber-resiliency steps taken, challenges encountered, and lessons learned when implementing a modernization program to upgr ade the statistical computing environment at our company. The components of the transformation includ ed moving from Unix to Linux, integration with a SAS Grid infrastructure system, and addition of web-base d SAS Studio. The project's scope became larger than initially anticipated. System Development Lifecyle (SDLC) practices were also required because regulatory content is created using the computing platform. Careful planning, collaboration and alignment between the business users, IT, and Quality Assura nce drove the success despite the challenges. Significant participation from the user community was also instrumental in the success of this project. Lessons learned were shared after the project deployment | parallel processing, sas grid, sas studio, sas enterprise guide | source | 10 |
| 38 | Damn*t, The Define! | Julie Ann Hood, Sarah Angelo |
Validation rules for define.xml have been evolving for over a decade, and sponsors are expected to use these rules to validate prior to submission. However, even after years of improvements to rule descriptions in order to provide further guidance, validation rules can still prove difficult to interpret. Since the define.xml is generally not completed until the end of the study, many users may not be familiar with the validation rules or sometimes even the define.xml file itself. In this paper, we will cover some of the more obscure define validation rules to help provide clarity on what the rules are actually checking for, how to investigate the issue, and the necessary steps to correct the define.xml. This guidance will hopefully empower others in the industry to feel more optimistic about tackling define validation issues and refrain from yelling, "Damn*t, the define!" | data cleaning, cdisc controlled terminology, excel, data validation rules, cdisc | source | 16 |
| 39 | Data Masking | Sumit Pratap Pradhan, Navneet Agnihotri, Rachel Brown |
When Clinical trials are conducted in double blinded manner and continuous analysis is needed by both blinded and unblinded team, data should be handled with special attention. Ideally unblinded data should not be seen by blinded team until Database Lock is completed, but sometimes analyzing data is necessary by blinded team to take important decisions for the study. Here Data Masking comes into the role to help sharing data with blinded team without violating any rule. In the process of data masking, original subject Id of a patient will be replaced by a dummy Id. Since the study is ongoing, analysis must be performed on individual data cuts. Another important requirement is that keep the dummy Id assigned consistent across all data cuts. This is particular ly helpful in doing comparison between different data cuts. Example - Consider 1st data cut has 2 subjects, subject 101-33 is assigned dummy id DUM-01 and subject 101-45 is assigned dummy id DUM-02 | proc step, in= | source | 5 |
| 40 | Data Mining of Tables: The Barrier for Automation | Ilan Carmeli | The outputs of statistical analyses are static and considered to be unstructured/semi-structured files. Verify by Beaconcure was developed with a unique data processing pipeline in which clinical data tables are mined not only for cell values, but also for clinical context. The objective of the following paper is to provide an overview of current challenges working with unstructured/semi-structured formats, and introduce readers to Beaconcure's solution for table mining | data science, dataset, clinical data, data pipeline, data cleaning | source | 6 |
| 41 | Derivation of Efficacy Endpoints by iRECIST Criteria: A Practical Approach | Ilya Krivelevich, Lei Gao, Richard Kennan |
Immune-checkpoint inhibitors represent one of the most important therapy advancements in modern oncology. They are currently used for treatment of multiple malignant diseases, especially at advanced metastatic stages. A challenging aspect of these immunotherapies is that they may show atypical therapy response patterns such as pseudoprogression. In 2017, the RECIST working group published a modified set of response criteria, iRECIST, for immunotherapy, based on RECIST 1.1, which was initially developed for cytotoxic therapies and adapted for targeted agents. This document provides rules and examples for how to derive most common endpoints (like Best Overall Response or Progression-Free Survival Time) for clinical studies according to iRECIST criteria. When a Clinical Study Report is produced, it is developed from Analysis (ADaM) Data Sets created under CDISC guidelines | duration of response, randomization, pd, adam dataset, standard deviation, progression-free survival, data science, clinical trial, complete response, procedures, cdisc | source | 9 |
| 42 | Developing and running an in-house SAS Users Group | Stephen B. Sloan | Starting an in-house SAS Users Group can pose a daunting challenge in a large worldwide organization. However, once formed, the SAS Users Group can also provide great value to the enterprise. SAS users (and those interested in becoming SAS users) are often scattered and unaware of the reservoirs of talent and innovation within their own organization. Sometimes they are Subject Matter Experts (SMEs); other times they are new to SAS but provide the only available expertise for a specific project in a specific location. In addition, there is a steady stream of new products and upgrades coming from SAS Institute and the users may be unaware of them or not have the time to explore and implement them, even when the products and upgrades have been thoroughly vetted and are already in use in other parts of the organization | source | 6 | |
| 43 | Digital Health Technology in Clinical Trials: Opportunities, Challenges and Biometrics Future Re-imagined | Kalyan Chakravarthy Buddavarapu, Sherry Ye, Vijay Vaidya |
clinical trial, missing value, big data, clinical research | source | 12 | |
| 44 | eCOA and SDTM: Bring Order to the Wild West | Vincent Allibone, Franklin E. Menius, Monali Khanna |
As the implementation of electronic Clinical Outcome Assessments (eCOA) continues to grow, the need for data standards is more apparent than ever. Data standards allow data to be processed and exported more efficiently, leaving us with high quality, organized data. The Clinical Data Interchange Standards Consortium (CDISC) have developed a model for organizing and formatting data in the form of the Study Data Tabulation Model (SDTM), which, when implemented, allows data to be submitted in an organized and clean way. In theory, the application of SDTM to eCOA data is straightforward, however, to the outsider or newcomer, it can seem a daunting and confusing process. With several different data classes, domains, variable roles and core variables that data could fall under, annotating screen reports for eCOA data seems to be straightforward for the experienced and an almost impossible task for the inexperienced | char., annotated crf, cdisc | source | 8 |
| 45 | Empower Your Programmers: Get the Most from Your Programming Team | Carol Matthews | Experts agree that empowering your workforce can provide a path to increased productivity, higher quality, and greater employee retention. While the concept of empowerment seems like an obviously good idea, too often leaders behave in ways that do just the opposite. It is important for leaders to understand what empowerment really means, how to foster an environment that promotes employee engagement, and how that can be translated into a happier, more competent, and more productive workforce. Particularly in the highly regulated technical world in which pharmaceutical programmers work, it can be challenging for leaders to trust that their staff will make effective decisions on their own. We will discuss what it means to empower your programming staff and practical ways to do so while reducing risk and realizing the benefits to be gained | pharmaceutical industry, data cleaning | source | 8 |
| 46 | Ensuring Distributed Data Custody on Cloud Platforms | Ben E. Bocchicchio, Sandeep Juneja |
audit trail, life sciences, data lineage, access control, role-based access control, data integrity, data science | source | 26 | |
| 47 | Enterprise-level Transition from SAS to Open-source Programming for the whole department | Kevin Lee | The paper is written for those who wants to learn the enterprise-level transition from SAS to open-source programming. The paper will introduce the transition project that the whole department of 150+ SAS programmers has completely moved from SAS to Open-source programming. The paper will start with the scopes of the project - Analytic platform switch from SAS Studio to R Pro Server, converting the existing SAS codes to R/Python codes, Window server to AWS Cloud computing environment, and the transition of SAS programmers to R/Python programmers. It will also discuss the challenges of the project such as inexperience in Open-source Programming, new analytic platform, and change management. The paper will introduce how the transition-support team, executive leadership and SAS programmers have overcome the challenges together during the project | sas programmer, proc sql, analytics platform | source | 4 |
| 48 | Estimating Differences in Probabilities (Marginal Effects) with Confidence Interval | Jun Ke, Kelly Chao, Corey Evans |
Fitting logistic models using SAS procedures such as PROC LOGISTIC can obtain log odds for populations in the data, you can also obtain estimates of odds and odds ratios by options prespecified in the procedure (or by exponentiating the log odds). But to get difference in population event probabilities (population means), we need to estimate a nonlinear function of the parameters of the logistic model. Alternatively, you could model the probabilities themselves rather than the log odds. This paper will present the statistical background as well as the modeling process using layman's explanations. SAS Inc has provided a series of macros that could assist with calculating the difference in probabilities, however, in this paper, we will introduce a more robust model that could accommodate uncommon scenarios that could happen during the trial |
regression analysis, logistic distribution, confidence interval, logistic regression, proc step, proc reg, proc genmod | source | 4 |
| 49 | Evaluating and Correcting Nominal and Actual Relative Time Measurements in Clinical Trials PK data | Timothy J. Harrington | Pharmacokinetic (PK) data has two types of observations: Dosing and Sampling. Amongst the data items recorded in both of these observation types are the nominal and actual times of dose administration or sample collection. The nominal times are the named times specified on the study case report forms (CRFs) such as VISIT, CYCLE and DAY. The actual times are the chronological times (local to the location of the site) as recorded by the investigating staff at dose administration or sample collection, such as a blood draw. To analyze the PK data there is a need to know the actual and relative differences between these various times. Examples are the nominal and actual time a sample was taken after the first dose or the most recent dose, or how soon a pre-dose sample was collected before a dose was administered. This paper is intended for persons with knowledge of base SAS and with a basic understanding of Clinical Trials Pharmacokinetic data | sample, imputation, time variable | source | 17 |
| 50 | Excel the Data | Naga Madeti | In the pharmaceutical research, when processing the data, it is evident that converting SAS dataset to EXCEL data and EXCEL data to SAS dataset is inevitable. This paper explains coding tips and tricks in using ODS EXCEL with PROC REPORT to generate a beautiful EXCEL sheet, which can be useful to successively import to SAS dataset again if needed without any text wrapping issue for the columns with long text. Also, demonstrates the process of generating EXCEL sheet with addition of multiple tabs. In addition, describes the way for distinguishing the case of not generating the EXCEL sheet due to programming errors from when there is no data |
proc report, excel, proc step, dataset, proc print, ods excel | source | 9 |
| 51 | Exciting Opportunities for Fresher | Rajinder Kumar, Mandar Panchi |
Should we hire a fresher or experienced associate is the corporate version of hen or egg phrase? Everyone wants the experience in his/her team. Ironically, no one wants to groom a fresher into an experienced resource. The feeling that experience will add to richness of best practices learnt and better execution strategies keeps us blinded to many positive facets of the fresh energy, which the entrants can add to any industry. The advantage of being the lowest in hierarchy and no earned badges to lose gives the freshers innovative edge and higher risk appetite. This keeps their fear of mind more open to new ideas and think out of box. We are living in VUCA(Volatile, Uncertain, Complex and Ambiguous) world. Most of our experienced associates are not much comfortable with VUCA world. They have evolv ed into a particular way of working which was part of their experience journey | source | 8 | |
| 52 | Explanations in the Study Data Reviewer's Guide: How's It Going? | Kristin Kelly | In 2018, the paper 'Best Practice for Explaining Validation Results in the Study Data Reviewer's Guide' was presented at the PharmaSUG conference. Its focus was to provide sponsors with some best practices for explaining validation results in the Study Data Reviewer's Guide (cSDRG) as well as guidance for interpreting some of the more confusing validation rules in Pinnacle 21. This paper will focus on metrics gathered across industry since that time of the quality of explanations as well as best prac tices that will aid in improving them even more | type i error, data cleaning, sample | source | 9 |
| 53 | Exploring R as a validation tool for statistical programming in clinical trials | Uday Preetham Palukuru, Runcheng Li, Nilesh Patel, Changhong Shi |
SAS has long been used as the go to and in some cases the only software for statistical programming to support clinical trials analysis and reporting (A&R). In recent years, R has emerged as an alternative software for use in clinical trial A&R. However, the widespread use of R is still hindered by some practical considerations such as, learning curve for SAS only users and limited knowledge on the capabilities of R for data analysis. In this paper, we will discuss the unique strengths of R in the validation of clinical trial A&R deliverables. Exploratory results from the efforts to validate SAS generated outputs using R will be discussed. We mainly focus on the validation of the widely used deliverables in clinical trial data analysis i.e. Tables, Listings and Figures (TLFs) | data cleaning, clinical trial, clinical data, data science, adverse events, sas base | source | 11 |
| 54 | External R Package Qualification Process in Regulated Environment | Jane X. Liao, Fansen Kong, Yilong Zhang |
Traceability, reproducibility, and quality are critical components in clinical trial Analysis and Reporting (A&R). It is critical to ensure reproducibility by providing a centralized library of qualified R packages. The centralized library also needs to provide a traceable and consistent environment for members of an organization to use R for both exploratory and regulatory deliverables. R packages are open-source software developed by a community of contributors around the world. R benefits from this de-centralized cohort of developers who have contributed many R packages to the Comprehensive R Archive Network (CRAN) and other code repositories. CRAN and other code repositories empower accelerated innovation but come at a cost of inconsistent quality of R packages. The lack of guaranteed accuracy or standardized testing may be a concern of using R in a regulated environment for clinical development | data cleaning, sample, clinical trial | source | 10 |
| 55 | Fast hashes for complex joins | Bartosz Szymecki | Even though SAS hash objects have been around for a long time, their application within the industry is still rather limited. It is well known that they can successfully replace DATA STEP MERGE statements with improved efficiency, but what about more complex types of joins that statistical programmers can come across in their day-to-day work? PROC SQL would be a natural choice, especially for small dataset s. However, as data volumes increase, as do the number of runs on that data, the advantage of using SAS hash objects over PROC SQL becomes clearly visible. If, in addition, a SAS hash approach is implemented within an easy-to-use macro, one may start to wonder why it shouldn't be used as the default for the majority of joins we do. This paper presents such macro in comparison to PROC SQL, using real-world examples that can arise whilst working with ADaM/SDTM standards |
hash table, proc sql, proc step, data step, out=, hash iterator, adverse events, cdisc | source | 12 |
| 56 | FDA Advisory Committee Meetings - A Statistical Programmer's Survival Guide | Phil Hall | Supporting your company's presentation to an FDA Advisory Committee Meeting may be one of the most important and high-profile things you will do during your career. As a statistical programmer, you will have to do a lot of preparation and be able to provide analyses to answer any questions during the meeting. This paper explains what to do in the months leading up to the meeting in order to be fully prepared based on my experience as the Lead Statistical Programmer for two FDA Advisory Committee Meetings . Although this paper is aimed at statistical programmers, it is useful to all involved in the preparation for the committee meeting, including statisticians and project managers | statistical programmer, data cleaning, secondary endpoint, clinical programming, subgroup analysis, adverse events | source | 8 |
| 57 | Finding Accord: Developing an eCOA Data Transfer Specification (DTS) All Can Agree On | Franklin E. Menius, Monali Khanna, Vincent Allibone |
When building a study where electronic Clinical Outcome Assessments (eCOA) are collected, the data transfer is often one of the last items considered and due to this can often be a source of headache for data providers and consumers alike. Assumptions are often made by one or more party about aspects of the collected data or their transfer that are easy to confuse or overlook. This paper will not only focus on bringing the topic of the data transfer closer to the beginning of the study design discussion but will also focus on the ins and outs of drafting a complete and concise transfer agreement that will work for all. We will discuss best practices for the industry, items to include or exclude, common sources of issues and errors, and real-world examples of the good and bad of a data transfer. Reusability, machine and human readability, and technical aspects will also be included. It is possible to make an eCOA data transfer clear, quick, easy, and painless. Let us show you how | clinical endpoint, clinical research | source | 12 |
| 58 | Following Your Footsteps: Maintaining Traceability in ADaM Datasets | Nancy Brucken, Karl Miller |
adam dataset, if-then-else, randomization, adverse events, final analysis, sdtm domain, sample, cdisc | source | 58 | |
| 59 | From %Let to %Local; Methods, Use, and Scope of Macro Variables in SAS Programming | Jayanth Iyengar | Macro variables are one of the powerful capabilities of the SAS system. Utilizing them makes your SAS code more dynamic. There are multiple ways to define and reference macro variables in your SAS code; from %LET and CALL SYMPUT to PROC SQL INTO. There are also several kinds of macro variables, in terms of scope and other ways. Not every SAS programmer is knowledgeable about the nuances of macro variables. In this paper, I explore the methods for defining and using macro variables. I also discuss the nuances of macro variable scope, and the kinds of macro variables from user-defined to automatic |
macro variable, call symput, macro put, proc sql, into, automatic macro variable, sas log, proc step, macro definition, symbol table, cpu time, macro facility, macro parameter, call symputx, local symbol table, macro quoting function, compilation phase, put statement, macro trigger, global symbol table, variable, %macro parameters | source | 23 |
| 60 | From Codelists to Format Library | Nancy Brucken | Codelist files in the pharmaceutical industry are designed to enumerate all possible values for a given variable. One common method for capturing and storing codelist information is to use the Microsoft EXCEL template generated by Pinnacle 21 Enterprise or Community, since that file is often used as the basis for analysis dataset specifications. From there, it's a short task to read the codelists from the file and convert them to SAS formats and informats |
excel, numeric variable, character variable, proc format, proc step | source | 6 |
| 61 | From the Laboratory Toxicity Data Standardization to CTCAE Implementation | Xiaoyin Zhong, Jerry Xu |
Oncology is one of the most prevalent therapeutic areas for clinical trials today. One specific method of analyzing oncology data relates to quantify the magnitude of abnormalities of clinical laboratory results. However, laboratory data is often challenging to work with during the analysis data set and output creation, especially when lab limits can be assessed bi-directionally. The lab data process becomes more complicated as the latest CTCAE version 5.0 incorporates grading criteria dependent on baseline measurements. In this paper, we will present the approach to ADLB data set creation, laboratory data presentation (grade shift table) as well as deriving the toxicity grade based on CTCAE version 5.0 and our recommendations on how to handle some common issues | ctcae grade, shift table, laboratory data, worst value, safety population, analysis set, clinical trial | source | 7 |
| 62 | Functions (and More!) on CALL! | Richann J. Watson, Louise S. Hadden |
SAS Functions have deservedly been the focus of many excellent SAS papers. SAS CALL subroutines, which rely on and collaborate with SAS functions, are less well known, although many SAS programmers use these subroutines frequently. This paper will look at numerous SAS functions and CALL subroutines, as well as explaining how both SAS functions and CALL subroutines work in practice. There are many areas that SAS CALL subroutines cover including CAS (Cloud Analytic Services) specific functions, character string matching, combinatorial functions, date and time functions, external subroutines, macro functions, mathematical functions, sort functions, random number functions, special functions, variable control functions, and variable information functions | sas log, sas function, call execute, proc step, rand, call sortn, macro variable, call sortc, macro call, call symput, character variable, missing value, call symputx, numeric variable, call streaminit, proc fcmp, character functions, symbol table, call missing, proc means, random number generation, cpu time, sample, prxchange, macro facility, proc sql, set statement, length statement, macro definition | source | 33 |
| 63 | Generating Bar Chat and Mutipanel graphs using SAS vs R for Regulatory Submission | Girija Javvaji, Alistair D-Souza |
In the submission package to the regulatory agencies, graphs play a vital role in interpreting the clinical data in the pictorial form. Until recently most prominently used software to create graphs was SAS but after knowing the importance of other languages (like R, Python, Spotfire etc.) which can be used for the same purpose, we tend to compare them to know the advantages and limitations of one another. Among these alternative options, R has been adopted in the pharmaceutical companies along with the traditional SAS language. In this paper we would like to discuss some of the prominent clinical graphs such as Bar chart and Multipanel plot using both SAS and R. A side- by-side comparison of both the graphs in SAS and R using their respective syntax, key differences and features that are available in both languages are highlighted in this paper | bar chart, waterfall chart | source | 7 |
| 64 | Generating Forest and Kaplan Meier graphs for Regulatory Submission: Comparison of SAS and R | Girija Javvaji, Sivasankar Konda |
Statistical graphs are the essential component of the clinical trials which are included in the submission package to the regulatory agencies. As we all know R language is being adopted in the pharmaceutical companies along with the traditional SAS which is still in use. In this paper we would like to discuss some of the prominent clinical graphs such as Forest plot and Kaplan-Meier plot using both SAS and R. A side-by-side comparison of both the graphs in SAS and R using their respective syntax, key differences and features that are available in both languages are highlighted in this paper | forest plot, clinical trial | source | 6 |
| 65 | Generating Real World Evidence On The Likelihood Of Metastatic Cancer In Patients Through Machine Learning In Observational Research: Insights For Prevention | Sherrine Eid, Samiul Haque, S. Robert Collins |
OBJECTIVES Properly identifying comorbidities in cancer patients that increase the likelihood of metastatic disease is critical to preventing disease progression and morbidity. Living with comorbidities can lead to an increased likelihood of negative outcomes in patients who are diagnosed with cancer. This research aims to identify opportunities to prevent or at least affect the likelihood of metastasis. METHODS Over 2.3 million deidentified, cancer patient medical claims records (www.c ompile.com) were analyzed to assess the likelihood of metastatic cancer. Cohorts were defined as any patient who was diagnosed with an ICD-10 diagnosis of C7.x, C78.x, C79.x, or C80.x (n=2,396,043). The outcome variable was metastatic cancer defined by Elixhauser | likelihood function, logistic regression, proc hpsplit, logistic distribution, regression analysis, gradient boosting, proc hpforest, machine learning, neural network | source | 8 |
| 66 | Generating statistical Tables, Listings, and figures using machine learning, deep learning, and NLP techniques that provide more efficiency to the statistical programming process | Farha Feroze, Ilango Ramanujam |
Our in-house tool is a seamless solution to generate statistical TLG reporting in a structured and faster way with the help of AI that reduces manual effort significantly. The tool is built with an efficient framework f or accelerating the TLG generation process in just three simple steps. Generate TLG reports just by uploading TLG mock shell and ADaM datasets. AI engine will automatically annotate mock shell with ADaM variables about 70 to 80% for the first time and accuracy can be improved on subsequent study usages. Once the annotations are finalized by the user, the system will generate the reports (tables, listings, and figures) in minutes and provide the stand-alone program for each TLG. The tool is easily configurable for any sponsor related TLG shell. The tool is trained by historical data. This paper will discuss one such algorithm of Machine Learning which was implemented as a tool | mock shell, machine learning, deep learning, adam dataset, cdisc | source | 3 |
| 67 | Getting a Handle on All of Your SAS 9.4 Usage | Stephen B. Sloan | SAS is popular, versatile, and easy to use, so it proliferates rapidly through an organization. It can handle systems integration, data movement, and advanced statistics and AI, has links to a large amount of file types (Oracle, EXCEL, text, and others), and has functionality for almost every need. In addition to SAS Base, there are a large number of specialized SAS products, some of the most popular of which are SAS STAT, SAS OR, SAS Graph, SAS Enterprise Miner, and SAS Forecast Server. SAS EG allows for quick creation of useful artifacts and facilitates saving the generated code for later use as part or all of another program. As a result, sometimes it is difficult to track all the SAS programs and artifacts being used across an organization, economies of scale can be overlooked, and repetition and "reinventing the wheel" sometimes take place. Programs and macros developed in one area can be useful in other areas and they can be improved by this internal crowd-sourcing |
proc step, data mining, dataset, macro call, sas enterprise guide, macro definition | source | 7 |
| 68 | Getting odds ratio at Preferred Term (PT) level for safety analysis in R | Kamleshkumar Patel, Jigar Patel |
Safety Analysis is vital part of regulatory review. Various types of adverse event (AE) analysis are being performed in industry for reviewing safety by comparing each preferred term (PT) level statistical parameters in two treatment groups. Hence, calculating various statistical parameters at preferred term level becomes necessary. We will discuss about getting Odds Ratio (OR) for two treatments with respect to each preferred term. Calculating Odds ratio in R for multiple records together is very tricky. This can be also used in various other safety analysis like volcano plot creation. We will be presenting a simple way to calculate odds ratio for hundreds of preferred terms together in R for various safety analysis | odds ratio, volcano plot, adverse events, sample | source | 4 |
| 69 | GIS Challenges of Cataloging Catastrophes: Serving up GeoWaffles with a Side of Hash Tables to Conquer Big Data Point-in-Polygon Determination and Supplant SAS PROC GINSIDE | Troy Martin Hughes | The GINSIDE procedure represents the SAS solution for point-in-polygon determination-that is, given some point on earth, does it fall inside or outside of one or more bounded regions? Natural disasters typify geospatial data -the coordinates of a lightning strike, the epicenter of an earthquake, or the jagged boundary of an encroaching wildfire-yet observing nature seldom yields more than latitude and longitude coordinates. Thus, when the United States Forestry Service needs to determine in what zip code a fire is burning, or when the United States Geological Survey (USGS) must ascertain the state, county, and city in which an earthquake was centered, a point-in-polygon analysis is inherently required. It determines within what boundaries (e.g., nation, state, county, federal par k, tribal lands ) the event occurred, and confers boundary attributes (e.g., boundary name, area, population) to that event |
cpu time, hash table, lookup, dataset, sas library, macro loop, big data, %if-%then-%else, proc gmap, data visualization, macro definition, proc step, macro variable, proc format | source | 28 |
| 70 | How Clear Communication & Expectations Improve the CRO - Sponsor Relationship: the CRO Perspective | Corey Evans, Samantha Kennedy |
The Sponsor - CRO relationship is a critical one in the life cycle of clinical trials. Fostering a positive relationship through clear communication as well as clear expectations and timelines is critical in maintaining harmony. A relationship that lacks any one of these key characteristics, whether it is communication, expectations, or no clear timeline can quickly lead to tension and frustration on both sides. Minimizing frustration and tension as much as possible is paramount in preserving a good relationship, which may reduce study team turnover, and will hopefully lead to a long-term relationship between the Sponsor and CRO | excel, sas log | source | 5 |
| 71 | How RANK are your deciles? Using PROC RANK and PROC MEANS to create deciles based on observations and numeric values | Lisa A. Mendez | For many cases using PROC RANK to create deciles works sufficiently, but occasionally , you find that it does not work for your needs. PROC RANK uses number of observations to produce a rank; however, if you need weighted percentiles then PROC RANK will not work. Instead, you can use PROC MEANS to successfully create weighted percent groups. This paper will illustrate the basic usage of PROC RANK and how to use PROC MEANS for the alternative. The paper will utilize BASE SAS 9.4 code and will use a fictional dataset that provides the total number of prescriptions written by providers for two years. All levels of SAS users may benefit from the information provided in this paper |
proc step, proc rank, out=, frequency analysis, proc freq, numeric variable, proc sql | source | 10 |
| 72 | How Simple Table of Content can make CSR Table, Listings, and Figures Easily Accessible? | Yogesh Pande | Clinical Study Report (CSR) c ontains 100+ Tables, Listings, and Figures (TLFs). If a study SAS programmer programmatically creates Table Of Cont ent (TOC) with titles, page numbers, and includes all displays as part of the same document, the file becomes quite large. Navigating from one display to another becomes slow. Also, the TLF folder can get crowded with lots of TLF files and to know which FILENAME belongs to which title becomes difficult. To overcome these challenges, this paper is introducing a macro that can generate TOC having the TLF titles and it ma kes sure that the TLFs are easily accessible via hyperlink for each display. Keywords: SAS, Macro, analysis, CSR |
proc report, proc step | source | 4 |
| 73 | How to create BARDA Safety report for Periodic review | Mrityunjay Kumar, Dnyaneshwari Nighot, Nitish Kumar, Ganeshchandra Gupta |
For an ongoing clinical study, we come across a situation wherein we need to submit safety report for periodic review to BARDA (Biomedical Advanced Research and Development Authority). It becomes of utmost importance for the statistical programmers to understand the contents of the report and the way to organize data within the report. Safety data such as number of enrolled subjects, screen failure subjects, discontinued subjects, adverse events and serious adverse events per site provides a detailed and easy way to understand safety report for an ongoing study to successfully review by BARDA committee. An illustration about contents of report with customized EXCEL output using ODS HTML may be useful for the programmers to easily produce the report without any hassle and may serve as a possible solution |
adverse events, clinical trial, create table, ods html, group by, excel | source | 5 |
| 74 | How to create SDTM SAS transport datasets using Python | Kevin Lee | The paper is written for those who wants to use Python to create SDTM SAS transport files from Raw SAS datasets. The paper will show the similarity and differences between SAS and Python in terms of SDTM dataset development, and actual Python codes to create SDTM SAS transport file. The paper will start with Python packages that could read and write SAS datasets such as xport, sas7bdat, and pyreadstat. The paper will introduce how Python reads SAS datasets from the local drive such as demographic, exposure, randomization and disposition raw SAS datasets. The paper will also show how Python creates variables from raw data such as SEX, USUBJID, RACE, RFSTDTC, and RFENDTC. The paper will also show how Python merge datasets using outer and inner join. The paper will also show how programmers use Python Dataframe for data manipulation such as renaming, dropping, and replacing variables. Finally, the paper will show how Python could create SAS SDTM transport file in the local drive | sas programmer, randomization, cdisc | source | 8 |
| 75 | How to Prepare for a Tax Audit | Evelyn Dyson Lee, Margaret Hung |
credit card | source | 6 | |
| 76 | How to utilize the EC domain to handle complex exposure data | Varsha Korrapati, Johnny Maruthavanan |
Dose modifications including dose elimination, hold, delay, reduction, and mid-cycle adjustments due to treatment-related toxicities are common in oncology clinical trials. In certain studies, the design of CRFs that capture treatment administration involves a high degree of complexity to account for such unplanned dose modifications that occur within or between treatment cycles. Under those circumstances, the creation of an SDTM EC (Exposure as Collected) data set helps capture all dose modifications that may occur to a protocol-specified treatment administration. The EC data set in turn facilitates a seamless functional path to derive the subsequent EX (Exposure) data set that contains information on actual drug administered within each study cycle; this separation makes reviewing and consuming these data a lot easier | dose modification, raw data, clinical trial, cdisc | source | 8 |
| 77 | Implementing and Achieving Organizational R Training Objectives | Jeff Cheng, Abhilash Chimbirithy, Amy Gillespie, Yilong Zhang |
Clinical data analysis and reporting for a clinical study report, submission, and internal decision-making are complex activities . The work requires large amounts of data to be processed, analyzed, and reported according to regulatory and departmental processes . As such, tools which help maintain process control, increase productivity, and sustain resourcing are in demand by statistical programming organizations. With many open-source libraries for data manipulation, statisti cal calculation , and visualization functions, R Statistical Programming Language is a tool that has gained popularity within the pharmaceutical industry. In this paper , we will highlight how Merck implemented its training strategy for R to achieve essential R proficiency in 75% of its statistical programming staff within one calendar year. We will shar e how the R curriculum was selected, planned, and implemented, which resulted in a robust training strategy that successfully achiev ed the training objective | clinical programming, clinical data, data science, sas programmer | source | 8 |
| 78 | Including Population Information in ADaM define.xml for Better Understanding of Datasets | Hong Qi, Majdoub Haloui, Guowei Wu |
To create analysis datasets for clinical trials, an important element is population selection criteria in the ADaM (Analysis Dataset Model) dataset specification to define study populations included in each dataset based on the Statistical Analysis Plan (SAP). Per current industry guideline/practice, population selection criteria or population information are not required to be listed directly in the submission document for each ADaM dataset, such as the analysis data definition document, ADaM define.xml, and Analysis Reviewer's Guide (ADRG). Therefore, this information is not described in ADaM define.xml and often missed in ADRG at the dataset level. Meanwhile, no software is available to automatically include the population selection criteria in define.xml. To help regulatory agencies for an easier review, effort can be made to communicate the study population enclosed in each submitted analysis dataset | adam dataset, clinical trial, excel, concomitant medications, cdisc | source | 6 |
| 79 | Intro to Coding in SAS Viya | Charu Shankar | This presentation is for users wishing to take advantage of the boost in processing speed for Base SAS programs that will execute in SAS Viya. The paper leverages the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. This presentation will attempt to answer three primary questions : 1. What is New in SAS Viya 2. How is SAS9 Different From SAS Viya 3. Where do I Integrate Existing SAS Code in SAS Viya | sas viya, parallel processing, sas base, proc step | source | 10 |
| 80 | Introduction to Annual Reporting for Beginners! | Akshita Gurram, Mamatha Mada |
In clinical trials, the safety information of the investigational drug plays a critical role. Regular analysis of safety is crucial for the assessment of risk to trial participants and to understand the risk vs. benefit of the medicinal product. Summaries of safety information are submitted to the regulators and other committees at regular periodic intervals to monitor the safety profile of an investigational drug. The Development Safety Update Report (DSUR) is the pre-marketing equivalent of the post-marketing Periodic Benefit-Risk Evaluation Report (PBRER). The DSUR goal aims to assess risk and any changes in risk since the previous DSUR, while the PBRER goal is to present a comprehensive and critical analysis of new or emerging information on the risks of the drug. Both the DSUR and PBRER are produced once every six months, annually or less frequently, based on national or regulatory requirements | adverse events, mock shell, clinical trial, statistical programmer, proc step | source | 8 |
| 81 | Introduction to Synthetic Control Arm | Venkat Rajagopal, Abhinav Jain |
There has been a rapid expansion in the use of non-RCT based evidence in the regulatory approval of treatments. A methodology named Synthetic Control Arm (SCA) has emerged to analyze external control data. SCAs help augment or replace randomized controls in many cases. SCAs provide cross-sponsor regulatory grade historical trial data that can be used for comparative analysis in new trials. This helps accelerate development timelines, reduces costs and increases the probability of trial success. It also helps decrease recruitment and reduces patient burden. Appropriate statistical methodologies need to be used to address the difference between RCT and SCA data. SCA comparisons are based on the complexity associated with matching external data to the study. For a simple comparison, simple mean, median or fixed-effect pooling is enough. Imbalance adjustments are carried out with multivariate regression and propensity scoring | clinical trial, synthetic control, clinical data, proc hpsplit | source | 4 |
| 82 | ISO 8601 and SAS: A Practical Approach | Derek P. Morgan | The ISO 8601 standard for dates and times has long been adopted by regulatory agencies around the world for clinical data. While there are many homemade solutions for working in this standard, SAS has many built-in solutions, from formats and informats that even take care of time zone specification, to the IS8601_CONVERT routine, which painlessly handles durations and intervals. These built-in capabilities, available in SAS 9.2 and above, will streamline your code and improve efficiency and accuracy | datetime, sysdate9, imputation, character variable, sample, clinical data, date variable, time, variable, options statement | source | 13 |
| 83 | Just Say No to Data Listings! | Mercidita Navarro, Nancy Brucken, Aiming Yang, Greg Ball |
Sponsor companies often create voluminous static listings for Clinical Study Reports (CSRs) and regulatory submissions, and possibly for internal use to review participant-level data. This is likely due to the perception that they are required and/or lack of knowledge of various alternatives. However, there are other ways of viewing clinical study data that can provide an improved user experience, and are made possible by standard data structures such as the Study Data Tabulation Model (SDTM). The purp ose of this paper is to explore some alternatives to providing a complete set of static listings and make a case for sponsors to begin considering these alternatives. We will discuss the recommendations from the PHUSE white paper, "Data Listings in Clinical Study Reports." | data review, clinical trial, data cleaning, regulatory submission, csr, cdisc | source | 4 |
| 84 | Leading into the Unknown? Yes, we need Change Management Leadership | Kevin Lee | The paper is written for those who want to lead the new changes in biometric department. Currently, the biometric department is going through Big Changes from traditional SAS programming to open-source programming, cloud computing, data science or even Machine Learning, and how to manage and lead those changes becomes critical for the leaders so that changes could be achieved under budget and on schedule. Change Management is the activities/processes that support the success of changes in the organization and is considered as a leadership competency for enabling changes within the organization. More importantly, the success rate of the changes directly correlates with change management by the leaders. Leaders with excellent change management is six times more likely to succeed than ones with poor change management. The paper will discuss major obstacles that leaders will face such as programmer/middle management resistance, insufficient support | sas programmer, machine learning | source | 3 |
| 85 | Lessons Learned from Using P21 to Create ADaM define.xml with Examples | Yizhuo Zhong, Christine S Teng, Majdoub Haloui |
Define.xml is a required component for the FDA, PMDA and NMPA submission data packages. It provides metadata for collected data as well as derived analysis data. Pinnacle 21 Enterprise (P21E) is a web-based tool that is commonly used by many sponsors and CROs to generate the define.xml. P21E supports multiple versions of CDISC standards and regulatory agency's business rules. During validation of ADaM datasets and ADaM define.xml, error and warning messages will be reported by P21E based on the configuration of data package used. These issues must be addressed to comply with CDISC and agency business rules. This paper will focus on the lessons learned through some examples when creating ADaM define.xml using P21E. A flow chart of suggested process and checking steps will be illustrated. Some best practices on ADaM datasets specification development will be discussed as well | adam dataset, data cleaning, sdtm domain, data validation rules, cdisc | source | 8 |
| 86 | Lessons learned while switching over to SAS Studio from SAS Desktop. | Steven C. Black | I've been working within the SAS desktop/server application for nearly 20 years and during that time have gotten to know my way around the block with shortcuts, abbreviations, keyboard macros and x commands to do some pretty fancy stuff. So moving over to SAS Studio was going to be a transition that I was interested in it but was not really looking forward to. As making the change would mean that some of the functionality that I enjoy would be lost and it was difficult to know what changes needed to be made to get everything working as it does in the SAS desktop environment. In this paper, I will discuss the similarities between the desktop version and SAS Studio, provide a detailed run through of the options available within SAS Studio. These are categorized by each major window in the application (Top menu, Explorer Window, and Editor Window). I then point out some of the new functionality that SAS studio offers which I find super useful | sas studio, txt, proc step | source | 19 |
| 87 | Leveraging AI to process unstructured Clinical data in real-time | Madhusudhan Nagaram, Syam Prasad Chandrala |
Clinical trial data is typically posted as structured data in the form of SAS datasets or EXCEL or CSV formats. This structured data can be easily read using various analytics tools such as SAS, R, Tibco, Spotfire, etc., and can be presented as listings, summarized reports or visualizations or dashboards. Sometimes Translational data, including labs, Pharmacokinetic s, Pharmacodynamics, and biomarker data, can be obtained from external vendors in various file format s. Often these external vendors lack the capability to send real-time data in structured file format. Instead, they send this data as PDF documents, scanned images of printer or handwritten documents, which are challenging to read and ingest into the tools for data listings and visualizations without human intervention |
clinical trial, clinical data | source | 10 |
| 88 | Look Up Not Down: Advanced Table Lookups in Base SAS | Jayanth Iyengar, Joshua M. Horstman |
One of the most common data manipulation tasks SAS programmers perform is combining tables through table lookups. In the SAS programmer's toolkit many constructs are available for performing table lookups. Traditional methods for performing table lookups include conditional logic, match-merging and SQL joins. In this paper we concentrate on advanced table lookup methods such as formats, multiple SET statements, and HASH objects. We conceptually examine what advantages they provide the SAS programmer over basic methods. We also discuss and assess performance and efficiency considerations through practical examples |
lookup, hash table, set statement, cpu time, proc step, dataset, conditional do loop, proc format, proc sql, data merging, do loop, put statement, sas log, key, insurance claims, key=, merge statement, sas base, sas programmer, temporary array, output, sample, sysrc, sample size, index, stop, user-defined format, call missing, if-then-else | source | 28 |
| 89 | Macro for Working with Zip Transport Files from the Web: The Case of the Medical Expenditure Panel Survey | Pradip Muhuri | Downloading zip SAS transport files (one at a time) from the web, unzipping the files outside of SAS, and then restoring them to SAS data sets can be time-consuming and cumbersome, mainly when a handful of files are of interest. This paper presents a SAS macro that automates downloading any number of zip SAS files from the Medical Expenditure Panel Survey website, unzipping them, and converting unzipped transport files to SAS data sets | macro variable, proc step, dataset, %if-%then-%else, sas log, macro loop, do loop, macro definition, proc copy, cpu time, proc http, proc sql, libname statement, dictionary tables, proc cimport | source | 14 |
| 90 | Making an ADaM Dataset Analysis-Ready | Sandra Minjoe | One of the fundamental principles of ADaM is to be "analysis-ready". But what does that mean, and how do you determine if your analysis dataset is indeed "analysis-ready"? This paper delves into what the ADaM documents say about being "analysis -ready", including what type of dataset manipulation is allowed (and not allowed) to happen between the ADaM dataset and the statistical output. It describes how to choose the appropriate dataset structure and recommends variables that will help efficiently create different types of analysis output, such as tables and figures. It also describes situations where "analysis-ready" doesn't apply. This paper includes examples of what you can do to ensure your dataset meets the ADaM "analysis-ready" fundamental principle | adam dataset, data science, adverse events, sdtm domain, anova, cdisc | source | 10 |
| 91 | Measuring Reproducibility and Repeatability of an AI-based Quantitative Clinical Decision Support Tool Having a Medical Decision Point | Douglas Milikien | Artificial Intelligence and machine-learning based methods have led to a rapid expansion of software products used in medical diagnostics. These tools are most frequently intended not directly for diagnosis, but as supporting information for the clinician to arrive at a diagnosis and referred to as Clinical Dec ision Support Tools. Methods for quantifying the measurement agreement of these applications to gold standard measurement is well known. What is less well-known are methods for quantifying the reproducibility and repeatability of quantitative assessments when those assessments involve measurement variation due to 1) case difficulty, 2) operator skill level or judgment, and 3) stability over time. Although generic guidelines exist (CLSI EP05-A3) for designing and measuring precision experiments, these guidelines assume that there are multiple replicates for each combination of experimental conditions | standard deviation, residual, proc mixed, sample | source | 9 |
| 92 | Merging Pharmacokinetic (PK) Sample Collection and Dosing Information for PK Analysis based on Study Design and lessons learned | Durga Kalyani Paturi | The PK merge dataset is a vital source dataset for PK statistical analysis. PK concentrations, sample collection, and dosing variables are merged and subsequently used as input for creating PK parameters . Because of the increasing complexity of clinical trial protocols, data issues, and the possible need of adding exclusion flags, one must carefully understand the study design , Statistical Analysis Pla n (SAP), and specifications before building a merge dataset. This paper explains the merge data set structure and presents examples of the merge data set in different study designs and various scenarios of data exclusions. Finally, we discuss the interesting lessons learned from the programmer's point of view | key, pk parameter, sample, randomization, clinical trial, crossover study | source | 11 |
| 93 | Metadata Driven Approach for Creation of Clinical Trial Figures | Jeremy Gratt, Qiuhong Jia, Girish Kankipati |
In clinical studies, a variety of figures is required to visually analyze the responses to study drug. Creation of these figures presents many programming challenges. For example, SAS provides multiple figure creation methods and this makes it difficult for programmers to learn. Modern figures used for clinical trials often overlay multiple kinds of data within a single graph, and this complicates the input data structure used to generate the figures. Within SAS, different kinds of figure elements (lines, bars, markers) have different rules for use, and these differences can also be tricky to learn. Creation of legends that combine and describe the various figure elements can present challenges that are not always natively handled within SAS. Finally, clinical study teams may ask for study -specific customizations to describe specific trial designs | macro variable, waterfall chart, where statement, macro parameter, sample, swimmer plot, macro call, do loop, proc step, clinical trial, dataset, macro loop, proc template, excel, macro definition | source | 13 |
| 94 | Monitoring SDTM Compliance in Data Transfers from CROs | Sunil Kumar Gupta | Typically, CROs transfer SDTMs to sponsors on a regular basis and sponsors accept the new SDTMs. Instead of just blindly accepting SDTMs, it is better to monitor SDTM compliance after each SDTM transfer. The benefits include higher SDTM compliance, higher data quality and error-free programs downstream. With each data transfer, smarter sponsors are proactive to build and compare metadata between the specifications, original and new SDTMs. This method assures more consistency in SDTM data structure as well as study level demographic and safety statistics. Sponsors can get early alert of any major changes in study baselines. This presentation shows how to create standard and custom SDTM metadata as well as methods to identify and track SDTM metadata differences | proc sql, proc step, create table, cdisc | source | 7 |
| 95 | New Digital Trends and Technologies in Clinical Trials and Clinical Data Management | Srinivasa Rao Mandava | Internet is a huge repository of information which is used to cater diverse needs of its users. Almost 5 billion people around the world now use the internet. The explosion of internet, Covid- 19 pandemic and social media expansion have been offered new digital trends and technological evolution in clinical trial operations and clinical data management sectors. We are going to discuss here, some of the latest new trends of clinical trial industry and challenges in these changing times and changing roles from data programmer to data scientist. Further, we are looking forward to the help from the industry in proposing a detailed curriculum for the current and future needs of career paths in clinical data operations and management towards a quick analysis and submissions of clinical data to regulatory agencies with quality for faster approvals using these technologies | clinical trial, clinical data, data governance | source | 8 |
| 96 | ODS Document & Item Stores: A New Beginning | Bill Coar | proc step, proc univariate, order by, proc report, proc freq, by group processing, output delivery system | source | 72 | |
| 97 | Optimizing TLF Generation: Titles and Footnotes Applying a New Idea to a Basic Approach | Jake Adler, Assir Abushouk |
When receiving feedback from a sponsor on tables/listings one of the most frequent updates is title/footnote changes. These updates can be very time consuming, so what if there was a way to take the updated shells and automate that to the programs? In this paper EXCEL Visual Basics for Applications (VBA) will be used to take the contents of titles/footnotes from the shells of a Word document and import that into SAS so titles and footnotes can be read in automatically. To validate this process when the shells are updated Apache Subversion (SVN) will be used to compare changes in old shells versus new shells |
excel, proc report | source | 12 |
| 98 | Orchestrating Clinical Sequels with a Strategic Wand - Challenges of introducing Rolling CSRs in a Master Protocol | Neharika Sharma | The requirement of fast paced drug development is often leading to heightened intricacies in trial designs and an upsurge of multifaceted questions to be answered from the same study. This further induces pivotal questions like how to maximize the existing resources to intensify the overall compound strategy, how to be submission ready etc. We had a similar situation on a First Time in Human Trial, a master protocol with an adaptive design, for a gamechanger tried out in multiple cancer indica tions at different dosages. Introducing the concept of Rolling CSRs for the very first time, as the programming lead, I drove the locking down of the ready cohorts based on a frequent scrutiny of the ongoing subjects versus the targeted powered sample size, upgraded at regular intervals through futility analysis. In this paper, I would highlight the various challenges involved e.g | clinical programming, clinical research, sample, clinical trial, futility analysis, sample size, data cleaning, excel | source | 9 |
| 99 | Pharmacokinetics Concentration Data Deciphered - Presenting an Oncology Case Study | Nikita Sathish | Pharmacokinetics (PK) analyses in clinical trials aim to help researchers understand to what degree the given drug is absorbed, distributed, metabolized, and excreted over time. The ability to view concentration as a function of time yields an efficient way to assess the safety of the drug, which is a first key step in any clinical trials. This paper will walk through a sample case study in an oncology setting to give a high- level overview of how statistical programming teams can transform lab collected PK concentration data into an interim PK concentration (PKC) file, which is then used by clinical pharmacologists as a source of information for deriving PK parameters. We explain in detail how several key variables are derived in the process and the challenges surrounding that effort. We will also clarify how clinical pharmacologists use the PKC file to generate the PK profile of the drug being studied | sample, pk parameter, clinical programming, excel, raw data | source | 9 |
| 100 | Potential features analysis affect Covid-19 spread | Yida Bao, Philippe Gaillard |
The emergence of the Covid19 virus has deeply affected the world for two years. There are no government or effective means that can completely suppress its spread. On the other hand, we cannot deny that the spreading map of the Covid-19 virus is not completely average. There are always regions with fewer infections and deaths than others. In this paper, we will use different features to explore the reasons. We selected the epidemic data from 2,352 counties in the United States and updated the latest infection and death number in 2022. Meanwhile, we introduced 28 objective variables such as temperature, longitude and latitude, diabetes Ratio, smoker proportion, etc. for statistical analysis. This is a very meaningful research project, which can help health institutions to invest the r ight resources according to different situations in different regions. Basic and graceful Linear Regression will be used in our projects, telling us a lot of potential information | proc reg, regression analysis, machine learning, neural network, response variable, data mining, proc hpsplit, proc step, proc hpforest, proc glmselect, scatter plot, time series analysis, correlation, proc princomp | source | 11 |
| 101 | PROC FUTURE PROOF 1.2 - Linked Data | Danfeng Fu, Ellie Norris, Suhas R. Sanjee, Susan Kramlik |
Throughout the clinical study analysis and reporting (A&R) process, source data goes through several transformation steps in different phases. Although pharmaceutical companies have built streamlined processes to generate data and results in each phase, there is no automated, streamlined way to provide traceability as data moves from SDTM -> ADaM -> TLFs -> study reports. Data traceability is critical to ensure good quality and regulatory compliance. Previously we published a paper that evaluated recent advances in technology and the clinical trial programming skillset to identify opportunities for improved programming efficiencies. Last year, we published an overview of steps and challenges building a linked data proof of concept and a readout to provide data traceability from ADaM to SDTM | proof of concept, clinical trial, macro definition, adam dataset, dataset, data lineage, excel, cdisc | source | 13 |
| 102 | Programmed Value-Level Metadata for Define XML 2.0 | Abhinav Srivastva | Value-level metadata in Define-XML 2.0 has been greatly enhanced to list a unique set of values (typically for a result variable) in combination with one or more categorical parameters of interest using a WHERE subset clause. A given slice of data using a WHERE subset clause can also be based on multiple datasets to create complex subsets as per the intent of the sponsor. The paper introduces a SAS macro which can help create a Value-level metadata in XML format that can be used as part of the larger Define XML file along with preparing other components like Control Terminology (CT), Comments, etc that go with the Define document | excel, ods excel, where statement, define-xml, cdisc | source | 6 |
| 103 | Quality Control With External Code | Bill Coar | In the CRO industry, we can create statistical reports in multiple ways: by writing our own source code or receiving source code from sponsors or other external CROs. In projects where we receive source code, the sponsor will provide us SAS code written and verified on their systems. When there is a need for unblinding prior to the final analysis, such as in supporting a Data Monitor Committee (DMC), some sponsors seek an independent Statistical Data Analysis Center (SDAC) to perform this task. The SDAC is expected to run the sponsor provided code in their own environment to produce an unblinded report used for interim monitoring of safety and efficacy. The primary assumption made about the SAS code is that it is tested/verified/validated (pick your favorite word) by the client. In fact, the SDAC has little control, or sometimes no control, over the SAS code that is received | quality control, randomization, adam dataset, macro catalog, clinical trial, cdisc | source | 7 |
| 104 | Quality Gates: An Overview from Clinical SAS Programming Perspective | Nagadip Rao, Pavan Jupelli |
Clinical trials are conducted in pursuit to answer a clinical research question, by generating relevant data, and analyzing it in order to validate an initial hypothesis. Data quality, thorough data analysis, and reporting, plays a critical role in determining our confidence in clinical trial outcomes. An endower like clinical trial, which involves complex multi-disciplinary processes, with partner entities spanning across the globe, working together under stringent regulations to generate relevant data an d perform data analysis, assuring quality is not trivial task. In an attempt to improve clinical trial data and analysis quality there are many techniques utilized over the years, one such technique which is successfully utilized across multiple industries is quality gate (QG's) approach | clinical trial, adam dataset, clinical sas, data cleaning, csr, clinical research, data science, randomization, cdisc | source | 10 |
| 105 | R And SAS in Analysis Data Reviewer's Guide and Data Visualization | Fan Lin | Although SAS has been the preferred programming tool in clinical studies for decades, R is gaining more popularity recently due to its flexibility and advanced graphical capabilities in data visualization. The primary scope of this paper is to compare SAS and R in automation of Analysis Data Reviewer's Guide (ADRG) in preparation of Section 7.2, Analysis Output Programs. The secondary scope of this paper is using R for data visualization. In this paper the automation processes using SAS and R are described and the advantage or disadvantage of each language is summarized. Also the easy use of R in data visualization to create the complicated circular plot is demonstrated | ods pdf, dataset, data visualization, do loop | source | 11 |
| 106 | R syntax for SAS programmers | Max Cherny | Making the transition from SAS programming to R programming can be quite challenging, especially for SAS programmers who have never coded in an object-oriented language. However, learning to code in R does not have to be difficult for those already fluent in SAS. Both languages provide similar functionality, with the main difference being the syntax required to accomplish the same objective. Th is paper provides a side-by-side comparison of R and SAS syntax, using examples of a clinical programmer's typical SAS code adjacent to the corresponding R syntax. These easy and reusable examples can function either as stand-alone programs or as starting points for further acquisition of R fluency for the SAS programmer | character variable, numeric variable, sas programmer, sas syntax, proc step, proc transpose, sas programming, data science | source | 12 |
| 107 | Real Time Oncology Review Readiness from Programming Perspective | Binal Mehta, Patel Mukesh |
The US Food & Drug Administration (FDA) Oncology Center for Excellence (OCE) has introduced a pilot program Real Time Oncology Review (RTOR) to enhance their review process for Oncology treatments, aiming to provide treatments safely and effectively as early a s possible to patients. Another objective for this pilot project is to maintain and improve review quality a s well as maintain the reviewer's workload through data and analysis standardization while allowing early meaningful engagement with applicant. The RTOR program was initially begun to support Supplemental Drug Applications that added new dosing regimens, new indications, or any new clinical information to the label for prescribing medications, but later this program was expanded to include original new drug applications and biological license applications for new molecular entities (NME) | statistical programmer, database lock, adam dataset, clinical trial, sdtm domain, txt, data cleaning, controlled terminology, cdisc | source | 10 |
| 108 | Reconstruction of survival curves: An approach of comparing study drug curve with the standard of care digital survival curve | Girish Kankipati, Jai Deep Mittapalli |
Survival analysis corresponds to a set of statistical approaches used to investigate the time it takes for an event of interest to occur. Most of the survival procedures involve KM plots, log-rank tests, etc. Survival analysis focuses on two important aspects: occurrence or non-occurrence of an event, and follow-up time. We are often interested in assessing whether there are differences in survival (or cumulative incidence of event) among different treatments. For instance, in first-in-human (FIH) clinical trials we want to compare survival curves between the investigational and comparative drugs on market (or the standard of care). For exploratory purposes, there is often an interest in seeing study vs. comparison drug survival curves on the same graph. To plot the survival curve for standard of care, we may need to construct the data from a digital image file in .png or .jpg format | excel, clinical trial | source | 8 |
| 109 | Redefining Industry Standards: Making CDASH and SDTM Work Together from the Database Level | Chad Fewell, Jesse Beck |
In today's industry, there appears to be a disconnect between the purpose of designing a database and how the database can be utilized to represent a client's needs better and more efficiently. The database is not just a tool for capturing data. It is also a tool that can be standardized to provide SDTMs almost instantaneously. The industry has moved towards CDASH being the standard for database design however there appears to be a fundamental misunderstanding of "Why" CDASH is needing to be used. This has led to a lot of the efficiencies that CDASH can provide to never come to fruition for SDTM creation. Subsequently, this leads into the need for a redefinition of the industry norm when creating a database. This inefficiency in CDASH primarily comes from a lack of understanding of how both CDASH and SDTMs work together in tandem | data validation rules, sdtm programming, cdisc | source | 5 |
| 110 | Rethinking Programming Assignments | Scott Burroughs | Programmers working on a project team in the pharmaceutical industry can reside in all regions of the world, often with little or no work hours overlapping. What happens when deadlines are approaching with much work to be done and the production programmer for a data set or display and the QC programmer work in different regions of the world? Often it means working odd and/or long/extra hours so questions can be answered immediately and more back-and-forth production and QC runs can be made at the same time to get your tasks done on time. But does it have to be this way? Can we re-think this process so people aren't working so many odd/extra hours to get things done? This paper will examine potential fixes, including findings from an experiment trying to see what works and what doesn't | data science, laboratory data | source | 4 |
| 111 | Rita: Automated Transformations, Normality Testing, and Reporting | Daniel Mattei | R is an open-source programming language that allows for highly customizable analyses and visualization capabilities within a more traditional computing environment. Statistical programmers within the clinical trials industry have taken advantage of its flexibility to create specialized packages for the creation of SDTM and ADaM data structures, tables, figures, and listings (TFLs), as well as import/export capabilities to interface with SAS datasets and associated metadata. More general packages aimed at users transitioning from SAS, providing critical functions such as descriptive statistical reports, hypothesis-testing to assess parametric assumptions of normality, and nonlinear transformations are not yet widely available for use on tabular data, as is typically the case when working with clinical data structures | normality test, proc means, proc step, cdisc | source | 15 |
| 112 | Running Parts of a SAS Program while Preserving the Entire Program | Stephen B. Sloan | The Challenge: We have long SAS programs that accomplish a number of different objectives. We often only want to run parts of the programs while preserving the entire programs for documentation or future use. Some of the reasons for selectively running parts of a program are: Part of it has run already and the program timed out or encountered an unexpected error. It takes a long time to run so we don't want to re-run the parts that ran successfully. We don't want to recreate data sets that were already created. This can take a considerable amount of time and resources and can also occupy additional space while the data sets are being created. We only need some of the results from the program currently, but we want to preserve the entire program. We want to test new scenarios that only require subsets of the program | proc step, run, macro variable, sas enterprise guide | source | 5 |
| 113 | RWD (OMOP) to SDTM (CDISC): A primer for your ETL journey | Ashwini Yermal Shanbhogue | Real-World Data (RWD) is defined as data relating to patient health status and/or the delivery of health care routinely collected from a variety of sources like electronic health records (EHRs), medical claims and billing data, data from product and disease registries, patient-generated data etc. The increase in availability of this observational data and the evolution of tools to analyze it has piqued the interest of global regulatory agencies to use this data to support regulatory decision making. Since t his data and its collection was not designed for this purpose however, it does not have uniform structure and vocabulary, making it difficult and time consuming to compare or exchange between computer systems. To circumvent this problem, RWD can be put int o a common format or common data model (CDM) with common representation (terminologies, vocabularies) | clinical trial, data standard, excel, cdisc | source | 10 |
| 114 | SAS Macro to Colorize and Track Changes Between Data Transfers in Subject-Level Listings | Inka Leprince, Elizabeth Li, Carl Chesbrough |
Most clinical trials have an independent data monitoring committee (DMC). In order to provide quality data to the DMC, sponsors conduct internal reviews of available data prior to each DMC meeting. Subject-level listings are used to aid the clinical data review of the ongoing studies. To ease the burden on the data reviewers, providing color coding for old/deleted records from the previous data transfer and highlighting new records from the current data transfer is increasingly requested by sponsors. We have developed a SAS macro, TrackCHG, to meet this request. This paper describes the SAS macro code of TrackCHG in detail. It also provides sample datasets and code for the reader to create an adverse event listing using the TrackCHG macro. After reading this paper, SAS users should be able understand an d implement the TrackCHG macro into their existing listing programs to color code the ir output listings | adverse events, sample, proc report, proc step, macro definition, adae, adam dataset, macro variable, character variable, clinical data, data review, clinical trial, raw data, excel, cdisc | source | 19 |
| 115 | SAS ODS Application of Interactive and Informative Clinical Trial Reports | Luwei Pang, Aiming Yang |
Graphs and charts are used extensively in the clinical-trial efficacy and safety analyses. They transform large amounts of information and statistical results into easy-to-understand formats. Visualization methodologies have been developed in different languages and software. For clinical trials, directly using the analysis datasets in real time without transferring data from one language/system such as SAS to another language/system such as R is desired. It is also desired to enhance data traceability by producing visualization programmatically rather than transfer data to an EXCEL spreadsheet to manually explore the features. Meanwhile, compared with the traditional graphs and charts, more interactive and informative visualization outputs are in demand to enhance the reviewing efficiency |
clinical trial, kaplan-meier plot, adam dataset, survival analysis, bar chart, efficacy summary | source | 9 |
| 116 | SAS Spontaneous Combustion: Securing Software Portability through Self-Extracting Code | Troy Martin Hughes | Spontaneous combustion describes combustion that occurs without an external ignition source. The right combination of fire tetrahedron components-including fuel, oxidizer, heat, and chemical reaction-can be a deadly yet awe-inspiring phenomenon, and differs from traditional combustion that requires a fire source, such as a match, flint, or spark plugs (in the case of combustion engines). SAS code as well often requires a "spark" the first time it is run or run within a new environment. For example, SAS programs may operate correctly in an organization's original development environment, but may fail in its production environment until necessary folders are created, SAS libraries are assigned, control tables are constructed, or configuration files are built or modified | sas library, macro variable, libname statement, data validation rules, macro put, exception handling, data cleaning, automatic macro variable, sas enterprise guide, if-then-else | source | 15 |
| 117 | SAS Standard Programming Practices - Handy Tips for the Savvy Programmer | Navneet Agnihotri, Sumit Pratap Pradhan, Rachel Brown |
As a programmer, did you ever paused for a moment and pondered that your program code could stand up to the review and re-use by your colleagues (beginners, intermediate or experts)? Ever wondered if the algorithm used in your program and that your programming style would pass the "white glove" assessment? We've all been there! Every programmer at some point in their career certainly encounters a scenario wherein some critical delivery to a client is planned. It is late in the day and to meet the client deadline, you're unfortunately required to pick up another programmer's code. As you open the program and scroll through it, your heart sinks. You quickly realize that resolving the issue is not a fast fix but will take hours of deciphering. But what shall we do now; we grumble, sigh and either re-write or struggle to clean it up to make it better for the next programmer and future deliveries. Inconsistent and unstructured code affect s us all, including the client | proc step, sas log, macro parameter, cpu time, dataset, data step, proc sql, macro variable | source | 11 |
| 118 | SAS Studio: Creating, Analyzing, and Reporting Data with Built-in & Custom Tasks | Himanshu Patel, Chintan Pandya |
SAS Enterprise Guide or Display Manager is the default programming tool for SAS users in the clinical trial industry. SAS Studio is a relatively new tool within the SAS environment, with powerful enhancements compared to SAS Enterprise Guide and Display Manager. SAS Studio provides built-in tasks for generating and executing complex statistical methods, models, graphs, and SAS procedures using a simple point-and- click interface. SAS Studio also allows programmers to develop custom tasks for the unique requirements they receive from time to time. This paper helps understand some of the predefined point-and-click task features and how to create custom task s for individual needs with the SAS Studio interface. It can be a beneficial tool for urgent requests, data analysis, and exploration. This paper will be helpful to those planning to consider an alternative programming option to SAS EG or Display Manager | sas studio, waterfall chart, bar chart, sas enterprise guide | source | 8 |
| 119 | Schoveing Series 5: Your Guide to a Quality NO | Priscilla Gathoni | Have you ever said NO and felt guilty, ashamed, anxious, frustrated, or angry afterward? Or better still, have you said NO and walked away feeling calm, poised, balanced, and centered? The two-letter word NO can pose both danger and authority when proclaimed, and that is why understanding the various ways of saying NO is crucial. Leadership, management, coaching, mentoring, facilitating, parenting, marriage, teaching, recruiting, hiring, firing, and many other roles require the mastery of the word NO. The most valuable assets as you progress in your career and life are your time, the ability to think, doing things in the order of their importance, and relationship building. Therefore, protecting these valuable commodities requires a strategic and tactical delivery method of saying NO. Your time on earth is too precious to be wasted with the inability to say NO or have failed relationships | source | 4 | |
| 120 | Self Modifying Macro to Automatically Repeat a SAS Macro Function | Chary Akmyradov | SAS Macro language makes the workflow easier. Writing macro functions to shorten a code and to obtain the analysis is a time saver. However, if there are hundreds of variables to plug in a macro function or if the same macro function is repeated by changing one of its arguments, then it is a time consuming process. In this paper, I demonstrate how to develop a SAS macro function using %do and %while loops with %scan and %eval functions to run any macro function automatically over a list of values | macro function, proc step, proc sql, nominal variable, create table, macro variable, macro definition, proc freq, macro loop | source | 6 |
| 121 | Simple and Efficient Bootstrap Validation of Predictive Models Using SAS/STAT Software | Matthew T. Slaughter, Isaiah Lankham |
Validation is essential for assessing a predictive model's performance with respect to optimism or overfitting. While traditional sample-splitting techniques like cross validation require us to divide our data between model building and model assessment, bootstrap validation enables us to use the full sample for both. This paper demonstrates a simple method for efficiently calculating bootstrap -corrected measures of predictive model performance using SAS/STAT procedures. While several SAS procedures have options for automatic cross validation, bootstrap validation requires a more manual process. Examples focus on logistic regression using the LOGISTIC procedure, but these techniques can be readily extended to other procedures and statistical models. The running example can be downloaded in .sas file format from https://github.com/saspy- bffs/pharmasug-2022-bootstrap-validation |
bootstrap, sample, data cleaning, proc step, proc logistic, logistic distribution, ods output, output, feature selection, key performance indicator, proc hplogistic, proc surveyselect, proc phreg, regression analysis, proc plm, sample size, correlation, macro variable, out=, training set, by group processing, logistic regression, proc sql | source | 22 |
| 122 | Smart Batch Run your SAS Programs | Wayne (Weizhen) Zhong | While batch running your SAS programs, you may need more features than simply running all programs. This paper shows how you can: redirect the log for each program, clean temporary files between runs, summarize errors, and pass environmental macro variables into each executing program | macro variable, call execute | source | 8 |
| 123 | Sponsor-defined Controlled Terminologies: A Tiny Key to a Big Door | Shefalica Chand | It takes a village to bring a novel treatment to the patients. One of the keys to getting faster drug approval is submitting a data package to the agencies that is easy to maneuver and unlock an efficient and quality review. There are several aspects contributing to a robust submission data package, and Controlled Terminology (CT) is one of them. CT is the collection of code lists and values used within a data set. CDISC regularly shares industry standard CT and code list values. Apart from these CDISC-compliant CT, sponsors are allowed to add sponsor-defined CT and additional code list values in extensible CDISC CT. Adding sponsor-defined CT for as many variables as possible gives FDA reviewers better insight into the submission data. These CT and code lists are summarized into submission data dictionary files such as define.xml. It can be a tedious task for sponsors to present sponsor-defined code list values, especially for larger studies and data integration projects | cdisc controlled terminology, excel, controlled terminology, cdisc | source | 15 |
| 124 | Standardised MedDRA Queries (SMQs): Beyond the Basics; Weighing Your Options | Richann J. Watson, Karl Miller |
Ordinarily, Standardised MedDRA Queries (SMQs) aim is to group specific MedDRA terms for a defined medical condition or area of interest at the Preferred Term (PT) level, which most would consider to be the basic use of SMQs. However, what if your study looks to implement the use of SMQs that goes beyond the basic use? Whether grouping through the use of algorithmic searching, using weighted terms or not, or through the use of hierarchical relationships, this paper looks to cover advanced searches that will take you beyond the basics of working with SMQs. Gaining insight to this process will help you become more familiar in working with all types of SMQs and will put you in a position to become the "go - to" person for helping others within your company | adverse events, exposure | source | 8 |
| 125 | Standardizing Procedures for Generating Dose Proportionality Figures to Improve Programming Efficiency | Jianli Ping, Krishna Sivakumar |
Dose proportionality figures with selected PK parameters provide an intuitive linearity assessment of PK parameters with dose levels for clinical trials in dose escalation. The figure relating the log of the pharmacokinetic parameter linearly to the log of the dose is commonly used. However, the programming procedures for such figures can be time-consuming and tedious. This paper presents generalized procedures to create a natural logarithmic scale of dose proportionality figures that include observed scatter plots, reference line, regression line, and legend annotation through macro calls using SAS. The described procedures can be applied to different studies by adjusting the parameter calls to increase the programming efficiency and accuracy. Some challenges and proposed solutions will be discussed | pk parameter, macro call, dose, regression analysis, proc step, dose escalation, scatter plot, macro variable | source | 11 |
| 126 | Subjects Enrolled in Multiple Cohorts within a Study - Challenges and Ideas | Peng Du, Hongli Wang |
Recently, an increasing number of study protocols have allowed the same subject to participate in more than one cohort within a study or multiple studies within a submission. One of the reason could be recruiting subjects especially health subjects and subjects with non-life threaten disease, has gained a lot of challenge during the COVID pandemic season. At the meantime, the concepts of SUBJID and USUBJID are clearly defined by FDA in the Study Data Technical Conformance Guideline (TCG, MAR 2022). USUBJID should be same for each subject throughout the compound but SUBJID can be different. In this case, collecting and mapping all these demographic information presents a great challenge for our stats programmers and data management. The CDISC presentation from MSI team has proposed the structure of DC domain at SDTM level. Even so, there are still some remaining questions such as how to handle it at ADaM level and what is the impaction to other SDTM datasets | sdtm domain, adam dataset, cdisc | source | 7 |
| 127 | The Emerging Use of Automation to Address the Challenge of Cross-Table Consistency Checking of Output Used in the Reporting of Clinical Trial Data | Ilan Carmeli, Keren Mayorov, Hugh Donovan |
The current gold standard for validation of programs summarizing data from clinical trials is double programming of all analysis datasets, tables, listings, and figures. This approach, however, does not identify cross-table discrepancies, and, we would argue, is not the best method of obtaining the highest quality results. As it is a manual exercise, it is labor intensive and subject to human error. Performing cross-table checks of outputs manually is a common practice performed by biostatisticians in the pharmaceutical industry. Manually validating multiple tables takes a significant amount of time and resources. This repetitive work can be done by a dedicated software, allowing the biostatisticians to focus on the statistical aspects of the study. It also provides a consistent, specified approach, whereas the current checking process is often unspecified and therefore not replicable | lookup, independent qc, data cleaning | source | 6 |
| 128 | Twenty Ways to Run Your SAS Program Faster and Use Less Space | Stephen B. Sloan | When we run SAS programs that use large amounts of data or have complicated algorithms, we often are frustrated by the amount of time it takes for the programs to run and by the large amount of space required for the program to run to completion. Even experienced SAS programmers sometimes run into this situation, perhaps through the need to produce results quickly, through a change in the data source, through inheriting someone else's programs, or for some other reason. This paper outlines twenty techniques that can reduce the time and space required for a program without requiring an extended period of time for the modifications. The twenty techniques are a mixture of space-saving and time-saving techniques, and many are a combination of the two approaches. They do not require advanced knowledge of SAS, only a reasonable familiarity with Base SAS and a willingness to delve into the details of the programs | proc step, dataset, proc sort, numeric variable, by group processing, proc means, character variable | source | 5 |
| 129 | Two Methods to Collapse Two Treatment Groups into One Group | Kenneth S. Liu, Ziqiang Chen |
When studies include different vaccine regimens (eg 1 Dose, 2 Dose) or multiple doses of the same treatment (eg 10 mg, 20 mg), statistics for each regimen or dose are summarized in separate columns in a table. Sometimes, it is of interest to combine the multiple regimens or doses into 1 group. This presentation will discuss 2 methods to combine multiple columns of a table into 1 column. One way is to collapse the regimens or doses into 1 group and analyze the data as if they were 1 group. Another way is to keep the regimens or doses in separate groups and use the ESTIMATE statement in SAS PROC MIXED to combine them. These 2 methods will be compared and discussed with an example and a simulation |
sample size, treatment effect, sample, weighted mean, confidence interval, proc step | source | 6 |
| 130 | Understanding Administrative Healthcare Datasets using SAS programming tools | Jayanth Iyengar | Changes in the healthcare industry have highlighted the importance of healthcare data. The volume of healthcare data collected by healthcare institutions, such as providers and insurance companies is massive and growing exponentially. SAS programmers need to understand the nuances and complexities of healthcare data structures to perform their responsibilities. There are various types and sources of administrative healthcare data, which include Healthcare Claims (Medicare, Commercial Insurance, & Pharmacy), Hospital Inpatient, and Hospital Outpatient. This training seminar will give attendees an overview and detailed explanation of the different types of healthcare data, and the SAS programming constructs to work with them. The workshop will engage attendees with a series of SAS exercises involving healthcare datasets | proc step, proc freq, sas programmer, proc sort, proc tabulate, insurer, health insurance, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc contents, proc report, sas programming, by group processing, dataset, proc means, lookup | source | 24 |
| 131 | Unravel the mystery around NONMEM data sets for statistical programmers | Sabarinath Sundaram, Lyma Faroz |
Having a deeper knowledge of the relationships between pharmacokinetics (PK)/ pharmacodynamics (PD) and exposure-response effects is crucial to product development and drug registration. Nonlinear mixed-effects analysis is typically adopted for any repeated measurements or longitudinal data analysis such as PK and PKPD using a software called Nonlinear Mixed Effects Modeling (NONMEM). A high- quality NONMEM data set is essential for this important decision-making modeling. Statistical programmers must have a solid understanding of PK and PD data associated with the study drug components and the study design. For a NONMEM data set, statistical programmers need to input dosing information, scheduled timepoints, PK data, PD data (labs, response, biomarker, and adverse events) as assigned by the PK scientist, as well as covariant information like demographics, baseline laboratory results, and vital signs and disease characteristics among others | clinical trial, response variable, population pk, missing value, imputation, adverse events, dataset, key, pk parameter, statistical programmer, time variable, data science | source | 9 |
| 132 | Upgrading from Define-XML 2.0 to 2.1 | Trevor Mankus | The Define-XML standard has developed significantly since its original inception in 2005 when version 1.0 was released. Fast forward to this year and versions 2.0 and 2.1 are the industry standard now. However, about 6 years separate the publication of the two versions and much has changed. This presentation will focus on highlighting the new elements and attributes introduced in version 2.1 as well as discuss some best practices that creators should follow in order to upgrade their existing define 2.0 to the latest published version of the standard. In addition, the presentation will show how Pinnacle 21 Enterprise can be used to aid in the up-versioning process using our EXCEL-like editor and also cover the new validation rules that were implemented so that you can feel confident your upgraded define.xml file complies with the latest guidance |
data cleaning, adverse events, adam dataset, cdisc | source | 6 |
| 133 | Using Boolean Functions to Excel at Validation Tracking | Noory Y. Kim | Our statistical programming teams use EXCEL to keep track of the completion and validation of project outputs. Large spreadsheets can provide detailed information on individual outputs but not a quick summary of overall progress. How can we enable all team members to be more mindful of the overall progress and thus take better ownership of meeting project deadlines? This paper will show how to combine Boolean formulas in EXCEL to count metrics such as (1) the number of topline outputs that passed validation and (2) the number of outputs that have passed validation on or after the primary completion date. The reader can use these formulas to create a dashboard in a separate tab of the validation tracker. This method does not require VBA or SAS, making it relatively simple to implement. By including the dashboard in the validation tracker itself, the reader can provide an easy way for all programmers working on a project to check its overall progress |
data cleaning, excel | source | 9 |
| 134 | Using R-Shiny Capabilities in Clinical Programming to create Applications | Prakasam R, Sujatha Kandukuri, Tyagrajan Swaminathan |
As of 2021, 399,549 clinical studies have been reported in clinicaltrials.gov. For the last 4 years, more than 30,000 studies were registered each year. Enormous amounts of data is generated from these studies and a number of pharmaceutical companies have started initiatives to make use of open-source tools like R to analyze this data. The potential of this data can be further unleashed using interactive visualization tools that can bring out intricacies of the data and patterns within it that cannot be identified with routine tabulation methods. In addition, there are possibilities with the available tools, such as R- Shiny, to integrate automation capabilities, web deployment capabilities, and Graphic user interface based interactive visualization capabilities to create a single front-end application that can enable end- user efficiency with zero license cost | clinical programming, data science, adverse events | source | 12 |
| 135 | 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 |
| 136 | Using the SAS System to interface with the Entrez Programming Utilities (E-utilities) of the National Center for Biotechnology Information (NCBI). | Kevin R. Viel | The National Center for Biotechnology Information (NCBI) of the United States National Institute of Health has multiple databases that are generally accessed via a web interface. Popular databases include PubMed, which houses citations for biomedical literature, and PubChem, which houses chemical information, such as chemicals by name, molecular formula, or structure, among other data. NCBI provides a set of eight server-side programs, the Entrez Programming Utilities (E-utilities), that provide a stable interface into the Entrez query and database system. NCBI also made available the NCBI Datasets command line tools datasets and dataformat. These tools are helpful for work with sequences of SARS-CoV-2, for instance. The goal of this paper is to introduce the E-utilities and describe two SAS System macros that use the HTTP procedure using the E-utilities URL and the command line tools |
macro quoting function, macro parameter, call symputx, macro variable, proc http | source | 17 |
| 137 | Using the SAS System with the National Center for Biotechnology Information resources and Immune Epitope Database to explore the realm of potential SARS-CoV-2 variants. | Kevin R. Viel | SARS-Cov-2 emerged in late 2019 and quickly became a pandemic. Infection may result in Covid-19, which may cause acute respiratory distress syndrome (ARDS), a risk factors for death, and post-infection sequelae (Covid-19 long haulers). The public databases of the National Center for Biotechnology Information (NCBI) of the United States National Institute of Health (NIH) include RNA sequences of SARS-CoV-2 and their classification by the Pango Nomenclature System. The number of viruses produced by an infected person and the poor fidelity of replication of viruses make variants of SARS-CoV-2 a practical certainty. The "epitope escape" model may predict which variants emerge and, specifically, does not require inter-human transmission. Even asymptomatic infections will generate variants. Generally, an MHC-II groove must bind a peptide with sufficient affinity to effectively present it to the adaptive immune system to generate antibodies | proc step, sample, proc sql | source | 25 |
| 138 | Validation of Statistical Outputs Using Automation | Ilan Carmeli, Yoran Bar |
Validation of statistical outputs of clinical trials is cumbersome and time consuming. Relieving these tasks requires a solution involving automation supported by Machine Learning (ML), an approach that has been proven across many industries, and is considered a game-changer in the world of drug development. Applied to statistical outputs validation, automation can be fundamental to rapidly ensuring sound interpretation of clinical data and, therefore, more efficient regulatory submissions. 'Verify' is Beaconcure's automated output validation solution, based on ML algorithms and created specifically for the pharmaceutical industry. It can read different clinical data formats in any layout into a semantic and dynamic database, to which any required segmentation rule, crosscheck and analysis can be applied | data cleaning, clinical data, lookup | source | 6 |
| 139 | Vendor Audit: What it is and What to Expect from it | Parag Shiralkar, Nagadip Rao |
Audits are primary instruments to assess the processes, procedures, and practices of an organization. It is common in pharmaceutical industry to utilize multiple contracted service providers or 'Vendors' to support various clinical trials activities for the sponsor. Likewise, the sponsors get audited by regulat ory agencies. All audits in pharmaceutical industry are geared towards ensuring that the organization follows good clinical practices or GCPs and other regulatory requirements for data management and reporting. The primary objective of 'vendor audits' is to ensure that there is sufficient risk management and mitigation procedures executed on sponsor side to ensure integrity of clinical data management and reporting processes. These audits can be conducted pro-actively to support the vendor selection process or can be done re-actively to investigate the practices of the sponsors and providers | clinical trial, service provider, clinical data, quality assurance, data cleaning, validation documentation | source | 6 |
| 140 | Visualization of Programming Activities and Deliveries for Multiple Clinical Studies | Zhouming(Victor) Sun | Managing programming activities is very challenging, especially when multiple studies are involved with competing deliverables for each study. As a project programming lead, to ensure that all programming deliverables are on track with high quality and an optimized resource usage is critical. Therefore, an efficient planning and monitoring is key to a successful programming management. This paper presents one comprehensive visualization of the programming activities and deliverables for multiple studies, including key deliverable timelines and major study information for tracking progresses in real-time. The graph is generated using SAS Graph Template Language (GTL), which enable to create your own custom graphs or to modify graphs created by SAS analytical procedures. The example presented in this paper illustrates how assignments and deliverables from multiple studies can be dynamically visualized and managed in a powerful manner | bubble plot, graph template language | source | 4 |
| 141 | What's black and white and sheds all over? The Python Pandas DataFrame, the Open-Source Data Structure Supplanting the SAS Data Set | Troy Martin Hughes | Python is a general-purpose, object-oriented programming (OOP) language, consistently rated among the most popular and widely utilized languages, owing to powerful processing, user -friendly syntax, and an unparalleled, abundant open-source community of developers. The Pandas library, a freely downloadable resource, extends Python functionality, and has become the predominant Python analytic toolkit. The Pandas DataFrame is the primary Pandas data structure, akin to the SAS data set in the SAS ecosystem. Just as SAS built-in procedures, functions, subroutines, and statements manipulate and interact with SAS data sets to transform data and to deliver business value, so too do Python and Pandas methods, functions, and statements deliver similar functionality | user-defined format, dataset, master data management, proc sql, macro variable | source | 17 |
| 142 | What-s your model really doing? Human bias in Machine Learning | Jim Box | Machine Learning is showing up everywhere in our industry, from performing diagnostic tasks to determining how patients are treated. People sometimes think that since a model came up with the process, it must be a fair one. However, there are multiple places where human biases affect the outcomes of these models. We'll explore some of the sources of bias, see the impact this can have on real people, and look at ways to mitigate the possible harmful side effects of using machine learning | machine learning, neural network, training set | source | 4 |
| 143 | Win Ratio Simulation For Power Calculation Made Easy | Lili Li, Roger Chang, Benjamin Fine, Yu-Chen Su |
More often than not, treatment effects cannot be evaluated by a single event. Therefore, composite endpoints have been frequently used for decision-making in clinical trials. However, conventional analysis of composite endpoints still treats each component as separate and equal. While with clinical relevance in mind, for instance, death may be seen as more important than other events such as stroke; thus, hierarchy ranking of component endpoint is desired. In 2012, Pocock et al. proposed the win ratio approach for composite endpoint analysis. In this approach, each component from the composite endpoint is ranked by its clinical importance in the study, and then analyzed by one component at a time in a hierarchical fashion between any two subjects. Since then, applications of win ratio approach have been steadily gaining momentum in clinical trial analysis. While more of those applications are observed at the hypothesis testing stage, fewer are focused on the early study design phase | clinical significance, clinical trial, p-value, macro definition, data science, hypothesis testing, macro variable, sample size, treatment arm, sample, correlation, proc freq, proc step | source | 13 |
| 144 | Working with Dataset-JSON using SAS | Lex Jansen | The Operational Data Model (ODM) is a vendor neutral, platform independent data exchange format, intended primarily for interchange and archival of clinical study data pertaining to individual subjects, aggregated collections of subjects, and integrated research studies. ODM provides the foundation for most CDISC Data Exchange Standards, such as Define-XML. CDISC is in the late-stage development of the much-anticipated ODM v2.0 update. ODM v2.0 will include the specification of Dataset-JSON, an efficient and modern exchange format for data which addresses many of the limitations of SAS v5 XPT files. JSON representations for exchange standards are widely used in today's architectures. In RESTful web services, JSON is often the preferred format for the service response, due to its compactness and ease of use in mobile applications. Other standards used in healthcare, such as HL7-FHIR support JSON as well as XML, together with other formats such as RDF | dataset, proc step, ods json, proc json, proc lua, libname statement, format statement, character variable, web service, missing value, macro variable, cdisc | source | 23 |
| 145 | You Don-t Know Where That-s Been! Pitfalls of Cutting-and-Pasting Directly into the Programming Work Flow | Dave Hall | It is sometimes tempting to save time by cutting -and-pasting values directly into a SAS program, a specification, or any other element involved in the life -cycle of a project. The alternative, when it involves substantial amounts of text that would otherw ise need to be typed, can be tedious and time -consuming. However, inserting values from potentially unreliable sources without adequate safeguards can be a risky proposition. In the end, it may be wiser and more efficient to be more deliberate about addi ng such values. Saving time by cutting -and-pasting is like finding a piece of candy on the street. Although it might look tasty, there's no way to know where it came from, where it's been, or what's in it. Putting it in one's mouth is unwise and risky. C aution is advised , because you don't know where that's been! | label statement, data cleaning, excel | source | 5 |
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