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BUGSPharmaSUG 2023 Papers - Index
209 papers. Source: PharmaSUG (Pharmaceutical Software Users Group) conference proceedings.
| # | Title× | Authors× | Abstract× | Keywords× | Extras | Pages |
|---|---|---|---|---|---|---|
| 1 | 'TUFF' LOVE: SDTM Pain Points Explained! | Kristin Kelly, Michael Beers |
Though the industry is very familiar with preparing SDTM data, there are still many nuances that one can't always be sure! Trial Design, Relative Timing variables, PK, Exposure, validation rules…and the list goes on. In this paper, hot takes and topics that are crucial to 'getting it right' for submission will be discussed | data cleaning, data validation rules, sample, clinical trial, sdtmig, pk parameter, trial summary, sdtm domain, cdisc | source | 12 |
| 2 | 10 Quick Tips for Getting Tipsy with SAS | Lisa A. Mendez, Richann J. Watson |
There are many useful tips that do not warrant a full paper but put some cool SAS code together and you get a cocktail of SAS code goodness. This paper will provide ten great coding techniques that will help enhance your SAS programs. We will show you how to 1) add quotes around each tokens in a text string, 2) create column headers based values using TRANSPOSE Procedure (PROC TRANSPOSE), 3) set missing values to zero without using a bunch of if-then-else statements, 4) using short-hand techniques for assigning lengths of consecutive variables and for initializing to missing, 5) calculating the difference between the visit's actual study day and the target study and accounting for no day zero (0); 6) use SQL Procedure (PROC SQL) to read variables from a data set into a macro variable, 7) use the XLSX engine to read data from multiple Microsoft EXCEL Worksheets, 8) test to see if a file exists before trying to open it, such as an Microsoft EXCEL file, 9) using the DIV function to divide |
macro variable, proc step, sas log, proc sql, proc transpose, excel, missing value, proc report, numeric variable, sample, cpu time, sas enterprise guide, do loop, if-then-else, libname statement, macro call, work library, macro definition | source | 16 |
| 3 | A case study of a successful RTOR submission and approval for Rylaze | Sudhir Kedare, Dilip Nalla, Kumud Kanneganti, Jagan Mohan Achi |
The RTOR (Real-Time Oncology Review) program, launched by the FDA's Oncology Center of Excellence and Office of Oncologic Diseases in February 2018, aims to expedite the review process for oncology treatments by allowing for early submission of top-line efficacy and safety data. This enables FDA reviewers to begin the review process sooner and brings treatments to patients more quickly. The program also improves the review quality and early engagement between the sponsor and FDA. This paper examines the experience of the Programming team at Jazz Pharmaceuticals, which successfully submitted and gained approval for Rylaze/JZP458 (BLA, June 2021 and sBLA in Nov 2022), a part of chemotherapeutic regimen for acute lymphoblastic leukemia and lymphoblastic lymphoma under the RTOR program | adam dataset, raw data, cdisc | source | 6 |
| 4 | A Configuration File Companion: testing and using environment variables and options; templates for startup-only options initstmt and termstmt | Ronald J. Fehd | macro put, macro variable, proc step, proc sql | source | 18 | |
| 5 | A Light-Weight Framework to Manage Programs and Run All the TLFs in R | Chi-Hua Huang | In most cases, a lot of Table/Listing/Figure (TLF) programs are developed in a study and many standard and study-specific considerations take place in its analyses. For each study deliverable, a set of programs are selected and run to produce outputs accordingly. Some programs have dependencies in general. Both program selections and dependencies determine the order of running the programs, i.e., processing time. Furthermore, R pre-loads data into memory, time to load physical data impacts more on processing, especially when dealing with large data. While R has been widely used to generate analysis results, there is not yet a comprehensive solution available to batch run multiple R scripts concurrently. To address this, this paper presents a light -weight framework that utilizes R base, tidyverse, sassy, and rstudioapi packages in conj unction with MS EXCEL to efficiently produce/manage the TLFs for a deliverable |
macro put, adam dataset, output delivery system, cdisc | source | 27 |
| 6 | A Macro to Identify Repeating SAS BY Variables in a MERGE | Timothy J. Harrington | This paper describes how, when performing a MERGE between two SAS datasets, observations causing a message: 'NOTE: MERGE statement has more than one data set with repeats of BY values' are identified. This paper is intended for SAS programmers with a basic or higher level of expertise. |
in=, merge statement, out=, inner join | source | 9 |
| 7 | A Macro to Mark Invalid PK Data Observations for Exclusion | Timothy J. Harrington | This paper describes a macro for identifying and marking observations in a SAS PK dataset, containing dosing and related PK sampling observations, as invalid and marking them with a an exclusion code and exclusion reason test. This paper is intended for SAS programmers with a basic or higher level of expertise and familiarity with PK data | sample, adverse events | source | 10 |
| 8 | A Macro Utility for CDISC Datasets Cross Checking | Chao Su, Jaime Yan, Changhong Shi |
High-quality data in clinical trials is essential for compliance with Good Clinical Practice (GCP) and regulatory requirements. However, data issues exist in ADaM and SDTM datasets within and between them in most practical studies. In order to ide ntify and clean data issues before database lock (DBL) or other main milestones, a macro is developed for discrepancy cross-checking between ADaM and SDTM datasets during analysis and reporting processes. In this paper, some common data checks among ADaM and SDTM datasets are presented and discussed. The findings are reported in an EXCEL spreadsheet with a friendly interface consisting of a neat summary tab and individual formatted tab for each data issue category. Moreover, the modularized structure provides excellent scalability and flexibility for the user to add a user-defined rule with simple and easy steps. This feature allows the macro to be used far beyond CDISC datasets |
sdtm domain, excel, clinical trial, clinical data, sample, data cleaning, cdisc | source | 9 |
| 9 | A model for sponsors to support independent operation of IDMCs in clinical trials. | Bhanu Bayatapalli, Yiyi Chen |
The Independent Data Monitoring Committee (IDMC) is entrusted with monitoring patients' safety and benefits along with the power to stop a trial for safety, futility or efficacy based on pre-specified stopping rules. Since IDMC is a wholly independent entity appointed by sponsor, sponsors often engage a third party, like a contract research organization (CRO), to produce reports using the accumulated data for IDMC review and assessment. Therefore, ensuring IDMCs have the highest quality of reports for their review is critical to ensure trials are only stopped for the right reasons or, just as importantly, kept going based on valid conclusions. The question then becomes how a sponsor can best ensure quality of such reports without infringing upon the independence principle. In this paper we will share a working model we found to be successful, with special focus as follows: 1. Development of Scope of Work (SOW), charter, DTP, timelines, and meetings 2 | clinical trial, treatment arm, randomization | source | 8 |
| 10 | A Pain in My ISR - A Primer on Injection Site Reactions | Kjersten Offenbecker, Fox Mulder |
We are all familiar with Adverse Events (AEs) and how to report them. But what about those special AEs that occur when your study product is an injectable? These special AEs are known as injection site reactions or ISRs and they are reported very differently than traditional AEs. In this paper we will explore what makes these AEs different and why they tend to be such a pain to programmers everywhere. We will not only explain what an ISR is and how it is different, we will also look at some examples of how they are reported and even how to help make them a little less of a pain | adverse events, proc freq | source | 11 |
| 11 | A Quick Look at Fuzzy Matching Programming Techniques Using SAS Software | Stephen B. Sloan, Kirk Paul Lafler |
Data comes in all forms, shapes, sizes and complexities. Stored in files and datasets, SAS users across industries recognize that data can be, and often is, problematic and plagued with a variety of issues. Data files can be joined without problem when each file contains identifiers, or "keys", with unique values. However, many files do not have unique identifiers and need to be joined by character values, like names or E -mail addresses. These identifiers might be spelled differently, or use different abbreviation or capitalization prot ocols | 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 |
| 12 | A SAS Macro to Convert CSV files to SAS Datasets | Zemin Zeng, Bo Yuan |
Statistical programmer in the pharmaceutical industry often needs to convert Comma Separated Values (CSV) files to SAS datasets. This paper provides a short SAS macro to automatically convert CSV files to SAS datasets . We shall share the techniques in the macro on how programmatically to ide ntify the column variables in a CSV file without any manual inputs . The macro has been widely used at work and has been proven to increase programming efficiencies and provide time/cost savings |
dataset, macro variable, infile statement, macro definition, proc step, proc import | source | 7 |
| 13 | A SAS Macro to Perform Consistency Check in CSR Footnote References | Jeff Xia, Chandana Sudini |
Footnote is an important part of tables, figures, and listings (TLFs) for CSR, which might include but not limited to abundant information such as abbreviations, acronyms, additional explanations that have values to be added as a part of the TLFs. Footnote is normally provided in a sequential order such as in the format of Roman sequence, or alphabetically, etc. It is often to see inconsistency of footnote reference between the body of TLFs and the footnote section, i.e., a reference number appear in the TLF body, but there is no corresponding reference number in the footnote section, or vice versa. Catching these inconsistencies by eye-browsing is an attention demanding and error prone task to perform. This paper introduces a SAS macro that compares the list of footnote reference number between the body of TLFs and the ones in the footnote section, and flag any discrepancies of footnote reference for each TLF | macro definition, sample | source | 5 |
| 14 | A SAS System Macro to Quickly Display Discrepant Values that are too Long for the COMPARE Procedure Output. | Kevin R. Viel | At times, values being compared by the SAS System COMPARE procedure are truncated in the output (.lst) because they are too long and the differences are not shown. While the COMPARE procedure can produce various data sets, that approach can be cumbersome. Instead, the programmer might want to display the discrepant values in the log or redirect the log to a permanent file for a quick view. The goal of this paper is to introduce a SAS macro that quickly displays these overly long values in the log for quick comparison |
data cleaning, proc compare, data step, proc report, macro parameter, macro definition, macro function, proc step, output delivery system, proc sql | source | 20 |
| 15 | A utility to combine study outputs into all-in-one PDF for DSUR | Wei Shao | During clinical development, periodic analysis of safety information is crucial for the assessment of potential risks to trial participants. This information is important for health authorities (HAs) to evaluate the safety profile of the investigational drug on a regular basis. As one of the safety aggregate reports, the Development Safety Update Report (DSUR) provides a periodic update on drug safety information. The statistical programming team plays an important role in the DSUR process as they are responsible for generating both cumulative and reporting period outputs to support DSUR. This paper addresses the generation of two regional listings which are provided to DSUR region specific. Additionally, the paper introduces a SAS macro that generates consolidated PDF files from sets of individual study SAS listings. The resulting PDF file(s) contains all the converted listings with self-extracted and properly sorted bookmarks | adverse events, clinical trial, clinical research, macro parameter, regulatory submission, adam dataset, macro definition, cdisc | source | 8 |
| 16 | A utility tool to assist with the setup of the startup environment for remote access | William Wei, Shunbing Zhao |
Along the course of the clinical trial process, there are multiple deliverables for different purposes such as interim analyses, DMC, safety evaluations, etc. These will require programming updates for each deliverable, which will reside in multiple subfolders and may involve multiple programmers. Therefore, the study level information also needs to be updated in the programming environment settings, for example the data cut date, study level macro variable values for titles/footnotes, folder structures, etc. Usually, programmers keep many copies of startup/setup files for developer/validation purposes and for different SAS platforms. With this approach, it takes considerable effort to keep track of updates that are done in the main setup file. It is tedious and error prone. If we can maintain one central copy of the setup file, and the other setup files can call the central setup file, it will reduce the effort to track the many setup file updates | macro variable, data cleaning, macro definition, if-then-else, macro quoting function, %if-%then-%else | source | 5 |
| 17 | Acceleration and automation of genomic data analysis to meet corporate compliance standards using advanced cloud components | Gopal Joshi, Satyoki Chatterjee, Pankaj Choudhary, Sanjay Koshatwar, Shekhar Seera |
Recent advancements in High-throughput next-generation sequencing (NGS) technologies grew exponentially in genomic research revolutionizing biological data analysis, and enhancing the study of complex biological systems at an unprecedented scale. The technological limitations of the NGS system are the deluge of genomic data produced. It's difficult for a single workstation to execute sequential methods and produce results quickly. Efficacy decreases significantly with human interference and to mitigate them, we developed an in-house pipeline, with the help of AWS services and tools like snakemake, kallisto, etc., for automating RNA-seq data analysis. It's efficient, scalable, reproducible, version-controlled, transparent, and cost-effective for large volumes of data. In this study, we have reviewed the RNA-sequencing technique using AWS to analyze gene expression at the transcriptional level | data science, amazon s3, batch job, data cleaning | source | 11 |
| 18 | ADaM Datasets with Multiple Participations per Subject | Grace Fawcett, Sandra Minjoe, Elizabeth Dennis |
The SDTM team has proposed a standardized technique for accommodating multiple participations per subject. In that proposal, the domain DM (Demographics) contains all subjects, with one primary record per subject, and the domain DC (Demographics for Multiple Participations) contains all subjects, with each participation per subject as a separate record. There is often also a need to represent the multiple participations in analysis, so the multiple participations must be brought into ADaM. A technique similar to the SDTM solution may b e used, where the standard ADaM dataset ADSL still contains all subjects, with one primary record per subject, but another dataset, which we call ADPL (Participation-Level Analysis Dataset) is used to contain all participations per subject, with one record per participation per subject. This paper will summarize the SDTM DM vs. DC proposal and describe how a similar ADSL vs. ADPL solution could work in ADaM | adam dataset, study day, adsl, cdisc | source | 7 |
| 19 | ADaM Design and Programming Standardization for Oncology Efficacy Endpoints Based on RECIST 1.1 | Ellen (Zihui) Lin, Matt Ness, Yinghui Wang |
RECIST 1.1 tumor response criteria are commonly used in clinical trials to evaluate treatment response of solid tumors, such as breast and colorectal cancer. Common oncology efficacy endpoints, such as the best overall response (BOR), duration of response (DOR), and progression-free survival (PFS), are often based on RECIST 1.1 and usually involve complex data collection and derivations. While sponsors strive to make such collection and derivation consistent across study protocols, there are often study-specific considerations that further challenge the programming of these efficacy endpoints. In this paper, we will share an innovative, intuitive, highly structured ADaM design Seagen used successfully in several recent filings to support the analyses of such common oncology efficacy endpoints based on RECIST 1.1 | sensitivity analysis, best response, flag variable, progression-free survival, randomization, clinical trial, cdisc | source | 9 |
| 20 | Admiralonco - The Cross-Company R Package for Oncology Admirers | Neharika Sharma, Matthew Marino |
As companies across pharmaceutical industry are focusing on adopting R language for the creation of submission packages and data analysis, there becomes a growing requirement for coming out of silos and working collectively and efficiently towards developing functions and utilities that cater to the common industry-wide need. Through a collaborative effort between pharmaceutical companies like GlaxoSmithKline, Roche, Amgen, and Bristol Myers Squibb, Admiralonco was developed as an extension package to the Admiral package to assist in the creation of oncology specific CDISC compliant ADaM datasets like ADRS, ADTTE etc. In this presentation, we will elaborate the contents and usability of the package which is readily available to all statisticians and programmers and which we believe which could help one and all to efficiently increase the usage of R while analyzing clinical data | adam dataset, in=, pd, pharmaceutical industry, imputation, randomization, best response, r package | source | 25 |
| 21 | Adopting the New Integrated Analysis Data Reviewer-s Guide (iADRG) | Randi Olsen McFarland, Srinivas Kovvuri, Kiran Kumar Kundarapu, Satheesh Avvaru |
For pharmaceutical and biotechnology companies, the culmination of collecting clinical trials data on their investigational products is providing the integrated data and analyses to regulatory agencies for approval. Integrated safety and efficacy data are submitted for regulatory review along with Analysis Data Reviewer's Guides. After sharing the draft and addressing public review comments, the PHUSE Optimizing the Use of Data Standards (ODS) Working Group is finalizing the integrated Analysis Data Reviewer's Guide (iADRG) template and supporting documents. These iADRG documents provide clarity and guidance to integrated data and analysis reporting. This paper will discuss key points and examples in adopting and implementing the new iADRG template. The iADRG submission document describes the traceability, and transformation from individual study data to integrated analysis data |
clinical data, clinical trial | source | 11 |
| 22 | Advancing Premarket Safety Analytics | Yang Veronica Pei | source | 41 | ||
| 23 | Adventures in Independent Consulting: Perspectives from Two Veteran Consultants Living the Dream | Joshua M. Horstman, Richann J. Watson |
While many statisticians and programmers are content in a traditional employment setting, others yearn for the freedom and flexibility that come with being an independent consultant. In this paper, two seasoned consultants share their experiences going independent. Topics include the advantages and disadvantages of independent consulting, getting started, finding work, operating your business, and what it takes to succeed. Whether you're thinking of declaring your own independence or just interested in hearing stories from the trenches, you're sure to gain a new perspective on this exciting adventure | clinical trial, credit card | source | 7 |
| 24 | All You Need to Know about the New CDISC Analysis Results Standards! | Bhavin Busa, Richard Marshall, Bess LeRoy |
The CDISC Analysis Results Standard (ARS) Team is developing standards to improve and facilitate the automation, reproducibility, reusability, and traceability of analysis results (Tables, Figures and Listings, aka TFL). The team is working towards this objective by creating a logical model to incorporate the elements for both analysis results and associated metadata. In this paper, we will provide a comprehensive overview of the CDISC Analysis Results logical model, including its background and development. We will discuss the current state workflow and challenges faced by analysts, as well as the future state envisioned with the introduction of the machine-readable analysis results metadata and analysis results data structure to automate the generation of statistical outputs and displays | adverse events, analysis set, cdisc | source | 7 |
| 25 | Amazing Graph Series: Butterfly Graph Using SGPLOT and GTL | Tracy Sherman, Aakar Shah |
Have you been tasked to create a butterfly graph? If so, you might be asking yourself, what is a butterfly graph and which SAS procedure should I use to create one? Which procedure will be the most straight forward to learn but also has the flexibility for custom modifications? The two most common methods used to create these graphs are using the SGPLOT procedure and the GTL (graph template language). The documentation for these procedures to enhance the visual appearance of these graphs is lengthy and cumbersome. This paper will help you learn the syntax required and narrow the time it takes to produce high quality and amazing butterfly graphs which can be shared with upper management or in a conference presentation. In addition, the paper also compares the SAS 9.4 SGPLOT procedure and GTL so that you can choose which method fits well with your programming requirements |
ctcae grade, proc sgplot, proc step, adverse events, footnote statement, output delivery system, proc template | source | 16 |
| 26 | An End-to-End and Fully Integrated Clinical Development Platform with eDC/Labs data Data Management, Medical Review, Statistical Analysis and Adaptive Design etc. | Peter Wang, Vindyala Sunil |
LSAF (SAS Life Science Analytics Framework) and SPACE are Janssen's end-to-end, and fully integrated clinical development platform that pulls in raw data off eDC plus dozens of Central/Local labs by schedules into LSAF -- where data managers all over the world, leverage the automated workflows to check, correct, and validate clinical raw data in order to ensure its completeness and accuracy on regular daily and sometimes hourly basis. Data then feeds into Medical Review/Safety Portal for additional/in-depth analysis per protocol and pre-set standards. And it's feed further into AWS hosted SAS and R GRIDs for programming and statistical analysis. Pinnacle 21 and Adaptive Design are fully integrated into LSAF based on "real-time" clinical evidence that allows our clinical team to adjust development protocol timely. It took Janssen all together about 10 years to build this fully integrated platform | clinical research, adaptive design, raw data, clinical data, data governance, sas grid, data science | source | 6 |
| 27 | An Introduction to Obtaining Test Statistics and P-Values from SAS and R for Clinical Reporting | Brian K. Varney | Getting values of test statistics and p-values out of SAS and R is quite easy in each of the software packages but also quite different from each other. This paper intends to compare the SAS and R methods for obtaining these values from tests involving Chi-Square and Analysis of Variance such that they can be leveraged in tables, listings, and figures. This paper will include but not be limited to the following topics: * SAS ODS TRACE |
output, anova, dataset, proc step, proc freq, output delivery system, ods output | source | 9 |
| 28 | Application of Tipping Point Analysis in Clinical Trials using the Multiple Imputation Procedure in SAS | Yunxia Sui, Xianwei Bu, Yuanyuan Duan, Yihan Li, Xin Wang |
In phase 3 clinical studies, tipping point analysis has been increasingly requested by regulatory agencies as a sensitivity analysis under missing not at random (MNAR) assumption to assess the robustness of the primary analysis results. One way to implement the tipping point analysis is using the SAS procedure PROC MI, which includes two steps: step one is to impute missing data using multiple imputation (MI) under missing at random (MAR) assumption, and step two uses the MNAR statement to adjust the MI imputed values by a pre-specified set of shift parameters for each treatment group independently. The tipping points are outcomes where the significance of treatment effect is just reversed. In practice, the actual shifts to the MI imputed values are not always exactly the same as the shift parameters specified in the MNAR statement |
missing value, proc mi, imputation, proc step, clinical trial, regression analysis, longitudinal data, multiple imputation, sensitivity analysis, treatment effect, correlation, sample, logistic distribution | source | 12 |
| 29 | Are you a great team player? | Daryna Yaremchuk | It is well known that being a team player is essential for managing projects successfully and great collaboration within a team brings fruitful results. No matter the role and position, everyone is important in achieving common goals, meeting timelines, providing the exemplary service and reaching the best customer satisfaction. I am ready to bet that at least once during their career every person, whether it is a junior programmer or team lead, has doubts regarding their productive contribution to the project. After that it is normal to reflect on your teamwork performance. Moreover, it is so pleasant to receive an email saying: "It was a great pleasure to work with you and I hope there will be a new opportunity for our collaboration next time". Also, excellent team player skills are an essential part of the job description for all positions within the statistical programmer's society | clinical trial, sas enterprise guide | source | 8 |
| 30 | Are you planning to create/validate CDISC data set in R? Here is a step-by-step guide! | Ganeshchandra Gupta | In recent years, many pharmaceutical companies have adopted R as a data analysis tool. The main reasons for the increasing importance of R are the availability and ever-growing number of high-quality statistical methods, very good graphics capabilities and a wide range of useful programming extensions. However, no single programming language can solve every single problem you will encounter in your programming career. Though SAS is easy to learn and provides simpler coding options, R on the other hand has a stepwise learning, depending upon programming language. To work with R, by rule of thumb, you will have to know the basics of the R language which is quite easy. And still, no one ever talks about how simple it is to do clinical trial data manipulation and creation of CDISC: SDTM/ADaM data sets. Majority of Pharma/Biotech companies and CROs use double programming technique to validate SAS data sets | dataset, clinical data, cdisc | source | 8 |
| 31 | As Simple as Falling Off a Log?: An Unusual Case Study of Mapping Data into the SDTM DA (Drug Accountability) Domain | Susan Mutter | The SDTM DA (Drug Accountability) domain tabulates the amount of treatment units dispensed to a subject and the amount returned to gauge dosing compliance for each treated subject in a study and is often collected in a log form format. It seems like a fairly straightforward domain, so mapping subject data to it should be as easy as falling off a log. Or is it? Add together a sponsor and two different CRO's (Contract Research Organizations) with an evolving protocol, separate databases for the double- blind and open label extension portions of the study, and a creative data entry approach and you have a recipe for complexity. This paper will present a case study of mapping drug accountability data that was anything but simple | data governance, crf, cdisc | source | 9 |
| 32 | Assign Character Values from Logical Expression with CHOOSEC | Joel Campbell | If you've ever wished for a way to assign character values based on a logical expression in one line of code, well I've got great news for you: CHOOSEC, you'll see! Usually, character values are assigned in the DATA STEP using one line of code with a PUT using a format or $format. But this requires creation of the format in another procedure altogether, sometimes even requiring the programmer to actually scroll up, create the new format, and potentially lose track of the progress being made in the DATA STEP. At minimum, the format/$format method isn't a true One Line Solution since additional lines are needed to create the format before it's available in the DATA STEP. CHOOSEC can simulate a format or $format in one line of code in the DATA STEP |
data step, character constant | source | 3 |
| 33 | Auto-validation of SAS Macros through Regression Testing | Madhu Annamalai, Umayal Annamalai |
Validation of SAS macros is a critical step for reporting quality outcomes. Typically, statistical programmers spend extensive time and effort in validation of macros. Furthermore, frequent code changes demanding repetitive manual validation make it even more resource inte nsive and affect quality of outcomes. Regression testing is a key step in agile product development. Its benefits have been established with a proven record of accomplishment of testing automation in many other fields. Although, extremely limited content is published on the application of regression testing in SAS Macros validation. In this session, our goal is to demonstrate the comparison between both validation of SAS Macros manually versus auto - validation through regression testing and the efficiency in time and efforts for validation. We will be highlighting to you our experience where automated regression testing is possible for most of the standard macros | data cleaning, regression analysis, macro definition, dataset, regulatory submission | source | 5 |
| 34 | Automated Mockup Table and Metadata Generator | Jeff Cheng, Shunbing Zhao, Guowei Wu, Suhas R. Sanjee |
Preparation and production of an analysis and reporting (A&R) package for a regulatory filing is a resource intensive process. The A&R package includes and is not limited to analysis datasets, tables, listings, and figures (TLFs). To ensure that the TLFs meet the needs of the stakeholders, mockup TLFs are generated by the statisticians to communicate the TLF specifications to the statistical programming team. Currently the mockup TLF generation process is a manual and time -consuming process. In addition, a statistical programmer creates call programs and the related documentation based on the mockups. Populating and maintaining the same information consistently in multiple documents is challenging and tedious | statistical programmer, analysis set, baseline characteristics, clinical programming, regulatory submission | source | 12 |
| 35 | Automating Data Validations with Pinnacle 21 Command Line Interface | Philipp Strigunov | Pinnacle 21 Command Line Interface (CLI) allows automation of validation jobs resulting in higher reliability of results and better overall performance of the data preparation process. For example, using the command line tool allows us to integrate Pinnacle 21 software with programming environments including SAS LSAF, and to work with SAS native SAS7BDAT format. Pinnacle 21 CLI also supports the creation of a Define.xml file. In this paper, we will provide an overview of P21 CLI, explain its syntax parameters, and discuss new features introduced in recent updates. We will also present examples of automating datasets and define.xml validations using shell scripts and SAS programming language | data cleaning, rest api, excel, macro variable, cdisc | source | 13 |
| 36 | Automating Non-Standard New Study Set Up with a SAS Based Work Process | Xingshu Zhu, Bo Zheng, Li Ma |
It's essential and effective to start a new study utilizing broad institutional standard macros, especially for data quality review (DQR) and analysis and reporting (A&R) in real-world-evidence (RWE) studies. However, the rapid developmental pace of studies introduces the challenge of effectively managing standard macros and often requires the identification of what standard macros are needed and where they are located. Having a clearly defined work process and tool for automatically copying specific macros and folders to the new study directory significantly reduces the time spent on manual work and improves reliability and efficiency across multiple studies. This paper highlights the functional portions of a real-life work process and a SAS macro to easily automate this task while preserving the original file timestamps for traceability | macro definition, macro variable, macro parameter, macro loop, %if-%then-%else | source | 7 |
| 37 | Automating SDTM: A metadata-driven journey. | Keith Hibbetts | SDTM is one of the deliverables in every clinical trial. Efficient creation of SDTM can save hundreds of hours of effort, yet automation of SDTM has proven to be a very difficult proposition. This paper details the journey Eli Lilly has been on pursuing SDTM automation. To use a car analogy, you need four wheels and an engine to drive. On this particular journey, the four wheels are: a robust set of standards, a metadata repository to store and maintain those standards, a set of generic macros for data set creation, and a programming process to utilize those macros. The engine is metadata. By defining a metadata model that not only defines the source and target but also the logic to convert the source to the target, we can build out the rest of the components to make this vision a reality. A proof-of-concept project based on this idea achieved 96% automation of SDTM variables in a test study. Now we're on the road to making this concept a production-ready reality | sdtm domain, raw data, proof of concept, macro call, adverse events, missing value, work library, data cleaning, concomitant medications, cdisc | source | 11 |
| 38 | Automation of Dataset Programming Based on Dataset Specification | Liqiang He | In the clinical trial field, standard datasets, such as SDTM domains and ADaM datasets, are an integral part of electronic submission package, and are also a prerequisite for TFL generation. Dataset programming is a time consuming, tedious task for SAS programmer. A highly efficient, automatic programming for dataset generation will prevent manual programming typos, save programming time and resources, and deliver high-quality work. Dataset specification is a detailed instruction for dataset programming and a major reference for dataset validation. This paper demonstrates a new practical approach to automate dataset programming based on dataset specification. It is pivotal for successful auto-programming to rewrite variable derivation to SAS-readable with the aid of keywords and punctuation marks in the dataset specification | data step, adam dataset, sdtm domain, excel, cdisc | source | 7 |
| 39 | Automation of the SDSP CBER Appendix for Vaccine Studies | Nicole Jones, Pritesh Solanki |
The Study Data Standardization Plan (SDSP) is an important means used to communicate between the sponsor and FDA. The SDSP describes the data standards used by the sponsor in non-clinical and clinical studies across the drug development program. Vaccine studies that are submitted to the FDA Center of Biologics Evaluation and Research (CBER) require an additional appendix. Part of this appendix includes 4 sections of which two are SDTM sections: (i) list of all SDTM datasets with all the expected or permissible variables used/planned for each study in the analysis and (ii) list of all SUPPQUAL variables used/planned for use. Manually generating these tables could take weeks if no data are available. This manual process is also prone to errors in the form of missing variables/domains. In this paper, we present a Shiny application developed to semi -automate the generation of these tables | sdtm domain, clinical trial, sdtm programming, adam dataset, data standard, cdisc | source | 6 |
| 40 | Automation of Validation of QC Plan Entries and Managing SAS Programs | Venkatesh Nemalipuri, Ateet Shah |
Clinical trial data computing environment can be part of audit by regulatory agencies. Validation of QC plan dates against the date stamps of Production SAS programs is often a manual or a non-existent process and manual checking can take a considerable amount of time and resources, not to mention the risks of errors in process. The first macro can compare date stamps of Production SAS programs against the QC Plan and if there are any mismatches, it can email the list of issues to their authors automatically. Also, it can alert programmers and study lead if program is present in the QC plan and not in the folders or vice versa and can write the Date QC passed in the QC Plan based on date stamps of the QC SAS programs. Manually creating hundreds of standardized SAS programs in short time period can be a tedious task | quality control, proc compare, data cleaning, proc step | source | 8 |
| 41 | Battle of the Titans (Part II): PROC REPORT versus PROC TABULATE | Kirk Paul Lafler, Joshua M. Horstman, Ben Cochran, Ray Pass, Daniel E. Bruns |
Should I use PROC REPORT or PROC TABULATE to produce that report? Which one will give me the control and flexibility to produce the report exactly the way I want it to look? Which one is easier to use? Which one is more powerful? WHICH ONE IS BETTER? If you have these and other questions about the pros and cons of the REPORT and TABULATE procedures, this presentation is for you. We will discuss, using real-life report scenarios, the strengths (and even a few weaknesses) of the two most powerful reporting procedures in SAS (as we see it). We will provide you with the knowledge you need to make that difficult decision about which procedure to use to get the report you re ally want and need |
proc step, proc report, proc tabulate, excel, proc means | source | 18 |
| 42 | Be a Lazy Validator - Let Your Code Do the Work | Cara Lacson, Ray de la Rosa, Carol Matthews |
Regardless of the study phase or indication, programs written to produce output that supports clinical trials submitted to regulatory agencies have one thing in common - they need to be validated. While code and output can be independently validated by a number of methods, the primary responsibility for ensuring that any code produces the correct result resides with the programmer who wrote that code. Both production and validation programmers are responsible for ensuring that their own code is valid before comparing the results. While many feel that this is a time-consuming process, there are many ways to quickly add code that effectively checks for data and logic issues so the act of validating your own code can be done efficiently. This paper will discuss a number of simple techniques to make your | numeric functions, proc step, data cleaning, sas log, temporary dataset, proc format, clinical trial, format statement, proc print, adverse events, cpu time, date variable, if-then-else | source | 17 |
| 43 | Best Practices for Efficiency and Code Optimization in SAS programming | Jayanth Iyengar | There are multiple ways to measure efficiency in SAS programming; programmers' time, processing or execution time, memory, input/output (I/O) and storage space considerations. As data sets are growing larger in size, efficiency techniques play a larger and larger role in the programmers' toolkit. This need has been compounded further by the need to access and process data stored in the cloud, and due to the pandemic as programmers find themselves working remotely in distributed teams. As a criteria to evaluate code, efficiency has become as important as producing a clean log, or expected output. This paper explores best practices in efficiency from a processing standpoint, as well as others | proc step, dataset, proc append, proc sql, cpu time, if-then-else, sas log, where statement, data merging, sample, proc sort, sample size, sas programmer, lookup, data=, drop statement, missing value, index, options statement, character variable, hash table, numeric variable, set statement, sas base, key performance indicator, raw data, subsetting if | source | 20 |
| 44 | Better Business Through Better Records | Alexa Lam | credit card, account | source | 18 | |
| 45 | Better CDISC Standards with Metadata Programming | Sunil Kumar Gupta, Abhishek Dabral |
Is metadata programming ready for prime time? Are you ready to leverage CDISC standards seriously and minimize coding? Are you ready to take advantage of metadata programming methods to enforce automation, standardize workflow, increase efficiencies and ensure higher quality control in addition to reduced resources and time? This paper shows several key applications as well as techniques for metadata programming. We will explore beyond the basic dictionary datasets and variable attributes (SAS dictionary.columns and dictionary.tables) to more advanced programming methods of creating macro lists, looping through each item as well as code generation. These components create a robust global macro utility to be used across multiple studies as well as establish a foundation for corporate global standards | proc step, proc sql, macro variable, macro definition, excel, proc format, call execute, controlled terminology, create table, date variable, lookup, sdtm programming, raw data, sas programmer, macro quoting function, cdisc | source | 14 |
| 46 | BIMO Package: Challenges and Perspectives while keeping up with the upgrades in the BIMO Technical Conformance Guide and the BDRG Guidelines | Mathura P. Ramanathan, Sowmya Gabbula |
The Food and Drug Administration (FDA) released an updated version (v3.0) of the Bioresearch Monitoring (BIMO) Technical Conformance Guide (TCG) in August 2022. Coincidentally during this time, P HUSE Working Group made its first release of the BIMO Data Reviewer's Guide (BDRG) draft package that included a well evolved BDRG template. As a part of an NDA submission, we prepared the BIMO Package in line with these latest BIMO guidance documents. In thi s paper, we highlight the updates from the latest version of BIMO TCG and their implications in our efforts in adapting to these upgrades while preparing the BIMO package. While BDRG is still an optional document, we also generated this document based on t he BDRG template. In particular, the BDRG template included ten required sections, and we share our perspectives and describe the elements needed for completing these required components | treatment arm, clinical data, clinical trial, data cleaning, adverse events, proc means, protocol deviations, safety population, logistic distribution, excel | source | 18 |
| 47 | Blastula for Communicating Clinical Insights with R via Email | Philip Bowsher | source | 27 | ||
| 48 | Bringing it All Together: Applying the Analytics Life Cycle for Natural Language Processing to Life Sciences | Sundaresh Sankaran, Tom Sabo |
Life Sciences stakeholders, including the pharmaceutical industry and regulators, seek timely, accurate, and interpretable safety signal analysis. However, much of this data is unstructured freeform text, including physicians' notes and patient narratives. Manual review of such data is time-consuming and labor-intensive. It involves considerable effort spent to understand the context behind safety signals. Volume pressure and lack of standardization in review increases the chance of error, which can have expensive repercussions through regulatory holds, financial penalties, and reputational risk. Artificial Intelligence techniques such as Machine Learning and Natural Language Processing (NLP) help achieve better analysis. These capabilities are accessible to subject matter experts and programmers. In this paper, we demonstrate the application of such techniques on SAS Viya including SAS Visual Text Analytics and show how they lead to faster realization of actionable insights | adverse events, machine learning, sas viya, data science, serious adverse event, unstructured data, champion model, life sciences, sas studio | source | 15 |
| 49 | Building an Internal R Package for Statistical Analysis and Reporting in Clinical Trials: A SAS User-s Perspective | Huei-Ling Chen, Heng Zhou, Nan Xiao |
The programming language R has seen an increase in usage in the analysis and reporting sector of t he pharmaceutical industry. Similar to how SAS programmers regularly write SAS macros, it is common for R users to write R functions to complete repetitive tasks, thus facilitating programming work. A n R package is similar to a well-built SAS macro library; this includes a collection of functions, instruction documentation, sample data, and testing code with validation evidence. An R package formalizes access to the R functions. Yet new users may find a steep learning curve associated with creating an R package from scratch. This paper outlines the essential components of an R package and the valuable tools to help create these components. Relevant online reference materials are provided as well | macro definition, clinical trial, data cleaning, data science, r package | source | 11 |
| 50 | Can We Do It Better? Real-Time Validation Of Sdtm Mapping Is Superior To Double Programming | Daniel Rolo, Bremer Louw |
Accurate, complete, and reliable clinical trial data is paramount to robust decision-making by regulatory authorities. Methodologies used to validate the data in question are resource and time intensive. The clinical industry typically relies on dependable but antiquated methods of validation to ensure that clinical subject data is robust. The most common approach is the use of double programming, predominantly using the SAS programming language. This paper introduces a systematic approach to the real-time validation of SDTM mapping of clinical trial data. The approach is module-based comprising of a thorough review of the mapping logic, verification that all source data points have been converted to SDTM (SDTM completeness ) and confirming compliance to industry validation rules (SDTM compliance) | data cleaning, sdtm programming, raw data, completeness, independent qc, clinical trial, sdtm domain, clinical data, macro definition, data validation rules, cdisc | source | 9 |
| 51 | CDISC Conformance and Compliance: So Many Resources, So Little Time! | Jennifer K. Fulton, Stephen Black |
If you are new to CDISC you may be overwhelmed by the variety and scope of reference resources required to produce a CDISC-compliant data package for submission to regulatory authorities. Or if you have been doing CDISC for a while, you may be realizing that it is time to expand your research beyond the implementation guides (IGs). Where to start? What if there is a conflict in the information provided? Which resources take precedent? How do I keep current? And how do I know what resources are out there? This paper attempts to answer those questions and more, putting the wide array of guidelines, software, educational tools, and contributing parties in one place. The paper will also provide links and descriptions so that when you are ready you will have the proverbial "CDISC World" at your fingertips | data cleaning, data validation rules, controlled terminology, data standard, clinical trial, data science, cdisc | source | 11 |
| 52 | CDISC SDTM IG v3.4: Subject Visits | Ajay Gupta | The Study Data Tabulation Model Implementation Guide for Human Clinical Trials (SDTMIG) Version 3.4 has been prepared by the Submissions Data Standards (SDS) team of the Clinical Data Interchange Standards Consortium (CDISC). Like its predecessors, v3.4 is intended to guide the organization, structure, and format of standard clinical trial tabulation datasets submitted to a regulatory authority. Version 3.4 supersedes all prior versions of the SDTMIG. In this paper, I will do a quick walk-through on the updates within the SDTM IG v3.4 from his predecessor. Later, I will go over the updated SUBJECT VISITS (SV) with examples e.g., new proposed mapping to include missed visits, how to use additional variables in SV | subject visits, data validation rules, cdisc | source | 7 |
| 53 | Challenges with Metadata Repository System Implementation: A Sponsor-s Perspective | Radhika Kale, Reema Baweja |
In the modern world, Metadata Repository (MDR) systems are a critical element of metadata management commonly used in a variety of industries, such as biotechnology and pharma, finance, healthcare, manufacturing, to improve data governance and performance. MDR is a database created to gather, store, and distribute contextual information (i.e., metadata) about data. The most efficient MDR system helps to implement frustration free end to end implementation of standards while adhering to quality, automation, and flexibility. There are certain challenges that can arise when implementing and working with MDR systems that are commercially available or built in-house. This paper gives an overview of specific challenges from sponsor's standpoint and helps set expectations from an ideal MDR system | clinical data, metadata management, data dictionary, excel, data standard | source | 9 |
| 54 | CMS VRDC - A simplified overview. What to expect in terms of data, system, code. | Zeke Torres | The Center for Medicare and Medicaid Services (CMS) Virtual Research Data Center (VRDC) is a resource that has strict stipulations on how to access and report on data. The ETL- related macros covered in this paper help with understanding the data. Also included is a useful SUMMARY procedure utility to help with obtaining useful facts from data (not just VRDC data). This set of ETL macros also includes a way to set Low Volume Limits and satisfy the reporting requirements of the VRDC. That "Low Volume Limits" format and the function in this tool box allows for an easier way of reporting and downloading data and meeting the requirements of the VRDC |
proc step, proc template, proc sql, proc format, proc freq, sas base, sample, sas enterprise guide, macro definition, proc means | source | 22 |
| 55 | Commit early, commit often! A gentle introduction to the joy of Git and GitHub | Isaiah Lankham, Matthew T. Slaughter |
audit trail | source | 9 | |
| 56 | Comparison of My Experience Between an Independent Consultant and an Employee | Kriss Harris | clinical endpoint, data validation rules, health insurance, statistical programmer | source | 11 | |
| 57 | Complementary Overlay - A Programmatic Approach to Figure Output Validation | Jesse M. Pratt, Rayce Wiggins |
Effective and efficient validation of figures for a clinical trial can be a challenging process. Common techniques for validating outputs, such as PROC COMPARE and RTF parsing are not viable for figure validation. Without these tools, programmers must rely on double programming the input data set and manually inspecting the output against corresponding tables and listings. Many times, the input data sets are not "one PROC away", and manual inspection of the output is prone to human error. This paper illustrates a novel programmatic method to figure validation by leveraging the combined usage of complementary colors and the DRAWIMAGE statement within the SAS Graph Template Language (GTL) |
data cleaning, proc step | source | 25 |
| 58 | Confirmation of Best Overall Tumor Response in Oncology Clinical Trials per RECIST 1.1 | Danyang Bing | In oncology clinical trials for solid tumor, the revised RECIST guideline (version 1.1) [1] is the standard guidance for response evaluation. In non-randomized trials where response is the primary endpoint, confirmation of partial respons e (PR) or complete respons e (CR) is required and handled in response analysis datasets. Instruction in RECIST v1.1 guideline does not provide the logic to handle response scenarios for all data. In this paper ov erall logic from RECIST 1.1 is explained, clarification of the confirmation logic to use for specific scenarios based on RECIST v1.1 is presented. Subsequent time requirements and minimum durations for stable disease (SD) is addressed. Finally , handling of intervening responses of SD or not evaluable (NE) between two CR or PR response time points is ex plained | best response, adrs, adam dataset | source | 3 |
| 59 | Consistency Checks Automation across Regulatory Submission Documents in the eCTD M5 folder | Majdoub Haloui, Loganathan Ramasamy, Hemu Shere |
The preparation of the data reviewer's guide (DRG) and the define.xml is a key step in a regulatory submission package for clinical trials. DRGs provide regulatory agency reviewers with additional context and a single point of orientation for SDTM/ADaM datasets submitted as part of eCTD Module 5. The define.xml provides necessary information to describe the submitted datasets and their variables. High quality DRGs and define.xml files are important for a successful regulatory submission to FDA, NMPA, and PMDA agencies. In this paper, we will provide an overview of a web-based application that leverages Amazon Web Services (AWS) serverless architecture. The application performs consistency checks across DRGs, and SDTM/ADAM defines, which are not typically covered by commercially available tools such as Pinnacle 21 Enterprise. This paper focuses on how these checks are created and an efficient approach for running them | reviewer guide, regulatory submission, cdisc | source | 8 |
| 60 | Creating a Centralized Controlled Terminology Mapping Repository | Danny Hsu, Shreya Chakraborty |
Controlled Terminology (CT) is the set of code lists and valid values used with data items within CDISC - defined datasets for regulatory submission. The use of CT helps harmonize the data for all submitted studies and improves the efficiency of data review. It also opens the door to powerful internal au tomation for heightened quality and efficiency. The question how to most effectively map the collected data, especially any free-text values from CRFs, into CT plays a very important role in each study and product team's development of CDISC datasets that are not only consistent and compliant, but also support analyses and summaries efficiently. This article shares a concept of creating a centralized repository (such as a spreadsheet) to help streamline the process of CT mapping. A centralized CT mapping sheet is generated by collecting all mapped terms from previous studies and is used to identify the new terms which need attention | proc format, proc step, cdisc | source | 4 |
| 61 | Creating The cxtf Native SAS Test Framework | Magnus Mengelbier | The natural evolution of business processes will eventually gravitate towards standardization and further automation to not only improve upon time to delivery but to further quality and standards. SAS macro libraries play an integral part to drive the process forward. As the use of macros evolve from repetitive processing to utilities and shareable process components, the effort of testing requirements and validation approaches change. Although test frameworks that support SAS exist, they frequently require command line or external access, which is not always possible in advanced solutions or embedded systems. The design, evolution, and learnings from creating a native SAS test framework is discussed through practical examples, including the benefits and drawbacks of testing of SAS code and macros in situ | macro definition, macro variable, dosubl, work library, data cleaning | source | 8 |
| 62 | Data Access Made Easy Using SAS Studio | Kirk Paul Lafler, Shaonan Wang, Nuoer Lu, Zheyuan (Walter) Yu, Daniel Qian |
SAS OnDemand for Academics (ODA) gives students, faculty, and SAS learners free access to SAS software and the SAS Studio user interface using a web browser. SAS Studio provides users with a comprehensive and customizable integrated development environment (IDE) for all SAS users. A number of techniques will be introduced to showcase SAS Studio's ability to access a variety of data files; the application of point-and-click techniques using the Navigation Pane's Tasks and Utilities; the importation of external delimited text (or tab-delimited), comma-separated values (CSV), and EXCEL spreadsheet data files by accessing Import Data under Utilities; read JSON data files; and view the results and SAS data sets that are produced. We also provide key takeaways to assist users learn through the application of tips, techniques, and effective examples |
dataset, sas studio, proc step, sas library, excel, proc contents, delimited file, bar chart, work library, histogram, proc import, csv | source | 26 |
| 63 | Data Doesn't Lie - Real Time Monitoring and Projecting on Clinical Trial Enrollment Progression | Wenjun He | Determining enrollment target and projecting realistic enrollment rates are challenging in conducting a clinical trial. Underperforming participant recruitment has been a long-time problem for clinical trials. To address the question whether the ongoing recruitment will enable a trial to reach the planned target number fast enough while staying within budget, the real-time monitoring is key to a successful trial. This paper shows how to use SAS to automatically visualize the dynamic accrual progression, and simultaneously make a data-driven projection on the accrual rate and the completion date of enrollment target. This real data driven projection can work as a reference for the study team and sponsors to make necessary future amendment to enrollment planning and timeline scheduling | clinical trial, randomization | source | 5 |
| 64 | Deep Learning to Classify Adverse Events from Patient Narratives | Tom Sabo, Sundaresh Sankaran, Qais Hatim |
Patient narratives reported in clinical study reports (CSRs) provide clinical evidence of adverse events that occurred to patients and help scientific reviewers during pharmacovigilance activities. The manual review of these narratives by safety reviewers is time consuming and resource intensive. How can we improve the efficiency of identifying safety signals from patient narratives? This paper describes an implementation to accurately categorize one adverse event term, "Serotonin Syndrome", from postmarket narrative data as an example of what FDA is capable of when leveraging deep learning technology using SAS Viya. Furthermore, we leverage a supervised Boolean rule builder algorithm, which provides a layer of interpretability and the ability to interact with FDA subject matter experts to refine the models | deep learning, adverse events, neural network, machine learning, life sciences, data cleaning, clinical trial, validation set | source | 11 |
| 65 | 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 | sas programmer, sas enterprise guide | source | 4 |
| 66 | Digital Data Flow (DDF) and Technological Solution Providers | Prasoon Sangwan, Piyush K. Singh |
Digital Data Flow (DDF) is an initiative to organize and automate the processing of clinical data and study protocol. From a technology perspective, one of the key purposes of this initiative is to deliver the technical standards which can be utilized to mechanize the study execution process, create a flexible solution and minimize manual effort during the study life cycle. One of the most important principles of the DDF initiative is being vendor agnostic, which means that different organizations can implement their solution in their own way, using reference architecture (RA) from DDF, from both process and technology perspectives | clinical trial, controlled terminology, api, clinical data, data standard, clinical research, cdisc | source | 8 |
| 67 | Display Layout Specifications to Flexibly Design and Generate Tables | Songgu Xie, Michael Pannucci, Toshio Kimura |
Currently, tables are created in SAS by manually processing the data and defining PROC REPORT to generate a predefined fixed table. If different layouts are required, extensive data processing will be required to create alternative table layouts. We propose a new process to decouple the statistical analysis procedure from the display layout generation process. The first step is performing the statistical procedure and storing results in standard results datasets (RDS), similar to CDISC ARS. RDS will be presented separately, thus not in this paper's scope. The second step is processing RDS to generate displays. We propose a new specification called the display layout specifications (DLS) to drive display generation. Through DLS, we will flexibly design various table layouts. DLS will be an input into a flexible display generation macro that will process RDS according to DLS to greatly facilitate the creation of alternative table layouts |
macro definition, confidence interval, statistical programmer, macro call, cdisc | source | 11 |
| 68 | Divergent Nested Bar Plot and SAS Implementation | Brian Lin, Weiming Du, Toshio Kimura |
Bar plots are commonly used in visualizing categorical outcomes. However, multiple nested categorical outcomes in two directions (ex: improvement vs worsening) pose unique challenges in interpretation and implementation. One example in ophthalmology is the proportions of improving or worsening of at least 5, 10, or 15 ETDRS letters. In this scenario, outcomes are nested, for example patients with ≥ 10 -letter gain inherently have ≥5-letter gain. Existing solutions include: 1) multiple independent bars or accumulative bars, 2) stacked bars. The most significant downside of these solutions is the loss of inherited nested relationship between outcomes, making interpretations difficult. We propose a novel figure, the divergent nested bar plot, to solve this problem: placing t wo sets of nested bar plot on the opposing sides of the y-axis (positive: improvement; negative: worsening). This figure is intuitive to interpretate | bar chart, proc freq | source | 6 |
| 69 | Do it the smart way, renumber with PowerShell scripts! | Menaga Guruswamy Ponnupandy | Renumbering large numbers of SAS programs is often a time-consuming and tedious task. Traditional methods, such as manual renaming or using SAS macros, lack the flexibility to adapt to different numbering schemes or platforms. This paper presents a step- by-step guide to renumbering SAS programs efficiently using PowerShell scripts, offering a dynamic and adaptable solution. By leveragi ng PowerShell, data analysts can streamline the renumbering process, save time, and ensure accurate numbering according to client specifications | macro definition, txt | source | 5 |
| 70 | Do's and Don'ts of Being an Independent Consultant | Raymond Lee | clinical trial, sas programming | source | 13 | |
| 71 | Documenting your SAS programs with Doxygen and automatically generated diagrams. | Philip A. Mason | Doxygen has been used to document programs for over 25 years. It involves using tags in comments to generate HTML, RTF, PDF and other forms of high-quality documentation. It supports the DOT language for making diagrams from simple text directives. PROC SCAPROC can be used to generate a trace of a SAS programs' execution. My SAS code can then analyse the trace and produce DOT language directives to make a diagram of the execution of that SAS program. Those directives can then be put into the Doxygen tags to add the diagram to your documentation. And the analysis can also show the performance of the SAS program to be used for tuning purposes. This paper shows how to use Doxygen with SAS and provides the code to automatically produce diagrams for that documentation or tuning purposes |
proc step, txt, keyword parameter, macro variable, macro parameter, %macro parameters | source | 12 |
| 72 | Effective APIs for SAS Language Applications | T. Randall Betancourt, Oliver Robinson |
Pharmaceutical firms typically have a large number of validated SAS language programs. Many of these programs are run repeatedly with alterations to produce required output. A common improvement on this process is to pass parameters to validated autocall macros. Still, programs are often hand-edited, tested, then output is copied and pasted to distribute results. In many cases, multiple manual steps are required that are expensive, error-prone and, importantly in a regulated environment, hard to reproduce reliably. The use of SAS language autocall macro libraries provides a method for effective reuse of SAS program logic. To make best use of available skilled workforce, firms need to fully control and automate this logic as well as expand software tooling and process automation to encompass Py thon, R, SQL, Perl, Powershell, Linux shell scripts, batch scripts and other programming technologies | rest api, proc http, proc step, autocall macro, macro variable, data step, proc sql, excel | source | 20 |
| 73 | Exploring the spread of COVID-19 in the United States using unsupervised graph-based machine learning | Kostiantyn Drach, Iryna Kotenko |
Real-world data can be essential for our understanding of clinical data, especially with the emergence of phenomena such as the COVID-19 outbreak. In this paper, we analyze how the spread of the virus has advanced across the U.S. during the initial phase of the pandemic using novel graph-based machine-learning techniques. First, a cloud of graphs is extracted from several publicly available datasets. In these graphs, each node corresponds to a single county (>3000 nodes per graph), whereby two counties are connected with an edge if they have similar patterns in the advance of the pandemic spread over a specific timeframe. A graph (or a subset of graphs) from the cloud with the most robust geo- metric properties is subsequently revealed. This constitutes a topological model of data. Next, unsupervised machine learning algorithms discover communities of nodes within the chosen graph relying on pure geometric properties of the model | data mining, machine learning | source | 39 |
| 74 | Exploring the use of AI-based image recognition and Machine Learning to Improve the Efficiency and Accuracy of TLFs Validation Process. | Mor Ram-On, Ilan Carmeli |
Thevalidationof tables, listings, andfigures(TLFs) isaburdensomeandlengthyprocess. Especiallythecheckingof figuresneedsalot of visual comparisons. Inorder toreviewandvalidatethefigures, thestatistical programmer needstoopenthetableononescreenandthefiguresonasecondscreenandcompareit manually. Thestatistical programmer looksfor consistencyof thefiguresdata, consistencyofthefigureformat includinglegend, title, andfootnotes, consistencywiththemockshells, andaccuracyoftheXandYaxes. Bydoingthismanuallythechanceof overlookingdiscrepanciesishigh. Inthispresentationwewill present howusingAI-basedimagerecognitionbyusingmachinelearning(ML) anddeeplearning(DL) methodscanhelpinautomatingsuchcomplexmanual tasksandaligntotheclinicalstandards | data cleaning, feature extraction, mock shell, pharmaceutical industry | source | 5 |
| 75 | F2: FAIR and Filing - Assessing Data Fitness for FAIR and Filing | Bidhya Basnet, Dyuthi Yellamraju |
Have you ever asked the question: "How are study programming tasks progressing?" When working on programming or filing activities for a study, a lot of time is spent planning what datasets & variables will be required to create the TLGs for a reporting activity. Upfront planning is good; however, keeping track of the progress of the different deliverables such as SDTM/ADaM including conformance, aCRF and Define.xml generation, reviewers guide completion, and TLG creation, proves challenging, especially when there are a lot of data science members involved. The reliance for gauging progress often is on 'word of mouth,' 'guesstimates,' and a 'gut' feeling. This drove us to explore a better approach to assess Filing and FAIR (Findable, Accessible, Interoperable and Re-usable) progress which resulted in the creation of an R Shiny application | excel, database lock, cdisc | source | 7 |
| 76 | Facilitating Complex String Manipulations Using SAS PRX Functions | John M. LaBore | SAS programmers learn to use many basic SAS functions within the DATA STEP, but surprisingly few learn about the SAS PRX (Perl Regular Expression) functions and call routines. The SAS PRX functions provide a powerful means to handle complex string manipulations by enabling the same end result with fewer lines of code, or by enabling the analysis of data previously out of reach of the basic string manipulation functions. The PRX functions and call routines that became available in SAS version 9 are accessible within the DATA STEP, and are tools that every advanced SAS programmer should have in their toolkit. Examples are provided to give a quick introduction to the syntax, along with a review of the resources available to the programmer getting started with SAS PRX functions |
cas, data step, sas programmer, macro variable, proc sql | source | 15 |
| 77 | FDA's Study Data Policy Framework and How CDISC Properties Are Evaluated for Inclusion | Helena Sviglin | data governance, clinical endpoint, clinical trial, controlled terminology, data standard, data cleaning, cdisc | source | 20 | |
| 78 | Fitting Logitoid-Normal distributions with MLE estimate by SAS SEVERITY and FCMP procedures | Lili Huang, Helen Dong, Yuanyuan Liu |
The real-world data in manufacture are usually non- normally distributed, and the behavior of statistical procedure applied depends on the distribution family from which the data are. The knowledge of the distribution family is necessary in exploration of the behavior. The SEVERITY procedure of SAS is capable of fitting distributions with MLE estimate of parameters. One curb of PROC SEVERITY is that the default pool of probability distribution models i s quite limited. Distributions such as Johnson family (Johnson Su, Johnson Sl, and Johnson Sb), SHASH, and Logit -Normal are potentially applicable to the manufacture data, unfortunately they are not available for PROC SEVERITY in the latest version SAS/ET S 14.3. In this paper, four Logitoid-Normal distributions are used as examples in contrast with and Normal distribution to demonstrate that customized distributions could be defined with FCMP procedure, and hence the distribution model parameters can be fitted using PROC SEVERITY |
normal distribution, proc fcmp, likelihood function, sample, proc step, probability distribution, cumulative frequency, maximum likelihood estimation | source | 8 |
| 79 | Friends are better with Everything: A User-s Guide to PROC FCMP Python Objects in Base SAS | Matthew T. Slaughter, Isaiah Lankham |
Flexibly combining the strengths of SAS and Python allows programmers to choose the best tool for the job and encourages programmers working in different languages to share c ode and collaborate. Incorporating Python into everyday SAS code opens up SAS users to extensive libraries developed and maintained by the open-source Python community. The Python object in PROC FCMP embeds Python functions within SAS code, passing paramet ers and code to the Python interpreter and returning the results to SAS. User-defined SAS functions or call routines executing Python code can be called from the DATA STEP or any context where built -in SAS functions and routines are available. This paper provides an overview of the syntax of FCMP Python objects and practical examples of useful applications incorporating Python functions into SAS processes |
dataset, proc fcmp, proc step, sas function, excel, variable, sas log, work library, character variable, file statement, return, length statement, xls, sas library, sas viya, macro function | source | 28 |
| 80 | From FDA to PMDA submission: How to resolve CDISC non-compliance issues | Karin Steffensen, Carina Brixval |
From the perspective of both the US Food and Drug Administration (FDA) and Japan's Pharmaceuticals and Medical Devices Agency (PMDA), CDISC compliance is mandatory for a submission package. At the core of CDISC compliance are the validation rules specified by each regulatory agency and also within an agency's Technical Conformance Rules. Validation rules may differ between agencies, especially with respect to the assessment of the severity of non-compliance, which can cause an application review to be suspended. Along with a company acquisition, Lundbeck acquired a Biologics License Application (BLA) submission package that had been submitted to the FDA and that was accepted by the agency , which in time, is also intended for a PMDA submission | data validation rules, data cleaning, cdisc | source | 16 |
| 81 | From Sea to shining sea - End to End discussion on PR and LB data | Abraham Yeh | From the clinical case report form (CRF), many big pharma and healthcare companies have standard sets that could fit various purposes, from the study design to achieving the study's goal. The statistical analysis would be heavily dependent on these selections to fit the purpose of the CRF. On the other hand, we have standard tables that present the results using the data collected/analysis done and shared with the cross-functional study team. First, two options about the prior radiotherapy pages with specific dosages of drugs. One is directly a text field containing all information that can create a listing for the team. The other is to have a separate field containing the real numerical value and the unit that could be transformed. In such ways, further analysis can be performed and evaluated. Second, three types of Urine analysis were picked from the laboratory data. I will show different kinds of layouts on each and their implications. From one, frequency tables can be summarized | sample, ctcae grade, proc freq, data science | source | 7 |
| 82 | Gear up the Metadata - Automating Patient Profile Generation, a Metadata Driven Programming Approach | Tanmay Khole, Aman Sharma, Lili Li, Durga Prasad Chinthapalli |
Patient profiles are individual reports of subject's clinical data and provide a great benefit to clinical or medical teams when performing an on-going review in a clinical trial project. These reports are customized per reviewer's requests when safety, efficacy, and other key significant data points vary for each study design. The programming challenges for generating patient profile reports include the accommodation of variances in data source, structure, mapping, quality, and the customization in report outputs and formats. The application featured in this paper is an MS EXCEL VBA based utility featuring a user -friendly interface that uses SAS macros in the backend with a robust design to analyze all clinical trials data in CDSIC SDTM or ADaM format and generate patient profiles by GUI |
sample, macro definition, raw data, excel, clinical trial, macro variable, crf, sas programming, extract, ods pdf, clinical data, proc step, ods html, cdisc | source | 20 |
| 83 | Generating Clinical Graphs in SAS and R - A Comparison of the Two Languages | Kriss Harris, Endri Elnadav |
kaplan-meier plot, hazard ratio, forest plot, median survival, proc step, dataset, graph template language | source | 48 | |
| 84 | Get a GPS to Navigate Your Skills to Find Career Purpose | Charu Shankar, Lisa A. Mendez |
The global pandemic saw employees take charge of their careers and focus on what inspired them in never seen ways. Finding purpose can seem overwhelming and a daunting task in these changing times. To address this issue, Charu and Lisa have written this paper to guide knowledge workers to find purpose and fulfillment in their work while bringing their best strengths forward. Using their combined skills and expertise in psychometrics, SAS, and testing and measurement, they will share self -assessment tools to understand one's skill set and then apply it to job fulfillment. Each participant will be provided with a self- assessment tool that will be discussed during the presentation | source | 5 | |
| 85 | Going Command(o): Power(Shell)ing Through Your Workload | Richann J. Watson, Louise S. Hadden |
Simplifying and streamlining workflows is a common goal of most programmers. The most powerful and efficient solutions may require practitioners to step outside of normal operating procedures and outside of their comfort zone. Programmers need to be open to finding new (or old) techniques to achieve efficiency and elegance in their code: SAS by itself may not provide the best solutions for such challenges as ensuring that batch submits preserve appropriate log and lst files; documenting and archiving projects and folders; and unzipping files programmatically. In order to adhere to such goals as efficiency and portability, there may be times when it is necessary to utilize other resources, especially if colleagues may need to perform these tasks without the use of SAS software. These and other data management tasks may be performed via the use of tools such as command-line interpreters and Windows PowerShell (if available to users), used externally and within SAS software sessions | txt, excel, sample, if-then-else, dataset | source | 45 |
| 86 | Guidelines for the Statistical Analysis in German Dossier Submissions | Meiling Gao, Xi Qian, Hui Mao |
German dossier submission is the regulatory submission process required by the German federal institute for drugs and medical devices for the approval of new drugs, generic drugs, and biosimilars in Germany. The submission contains comprehensive information on the quality, efficacy, and safety of the drug, and must follow specific guidelines. Pharmaceutical companies must submit such information to a German dossier before launching their products in Germany. However, the submission process can be complicated and challenging, as it requires a thorough review and evaluation of drugs with complex statistical method. This paper aims to fill the gap by providing guidelines for conducting statistical analysis in German dossier submissions. We will cover the statistical strategies and common pitfalls when preparing the dossier submission | clinical trial, change from baseline, sample, confidence interval, adverse events, relative risk, effect size, proc reg, survival analysis, odds ratio, data science, standard deviation, regression analysis, treatment effect, least squares mean, hazard ratio, likelihood function, overall survival | source | 8 |
| 87 | Handling Anti-Drug Antibody (ADA) Data for Efficient Analysis | Sabarinath Sundaram, Johnny Maruthavanan |
Large molecules have revolutionized the pharmaceutical industry. The complex nature of these therapeutics can be mistaken by the human body as foreign substances and their interactions with various endogenous proteins in the human body may induce an immunogenicity effect to produce anti - drug antibodies (ADAs). Based on their interaction with antigen binding sites, ADAs are classified as non- neutralizing antibodies (non-NAbs) and neutralizing antibodies (NAbs). These could impair the functionality of the drug by interfering with PK performance, decrease drug efficacy, and trigger ser ious hypersensitivity reactions. Monitoring ADA is key to evaluating safety, post-marketing surveillance, and defining risk mitigation strategies. High-quality programming support with solid understanding of ADA data is critical for the programmers to map it to relevant CDISC standard tests that serves as a base to create efficient and impactful ADA analysis | sample, clinical research | source | 7 |
| 88 | Handling CRS/NT Data in CAR-T Studies and Submission | Joe Xi, Yuanyuan Liu, Lorraine Fang |
In most CAR-T (Chimeric antigen receptor T) studies, Cytokine Release Syndrome (CRS) and Neurotoxicity (NT) are the most common types of toxicity caused by CAR-T cells. Thus, the data of CRS and NT becomes essential, and during regulatory submissions, agencies have required special or extra data and analysis beyond regular AE data reporting. This paper shares our experience from past two CAR-T compounds submissions, on CRS/NT data handling, specifically from the following two topics: 1. How we organized CRS/NT data in SDTM/ADaM package to support Clinical Study Report (CSR). 2. How we supported Health Authority (HA) review by providing supplemental CRS and NT data which were requested by FDA review team | sample, clinical trial, cdisc | source | 7 |
| 89 | Handling of Vaccine SDTM Data for FDA CBER/OVRR Submission Compliance | Pragathi Mudundi, Zhaoyu Xie |
SDTM (Study Data Tabulation Model) is one of the required standards for data submission to the FDA, and following SDTM-IG is the conventional practice. However, submitting Study Datasets for Vaccines to the Office of Vaccines Research and Review (OVRR) in FDA/CBER division has specific rules. They are often ignored in practice, which may result in compliance issues and even rejection of vaccine study submissions. In this paper, we will cover the rectification work for a vaccine study to address SDTM complia nce with FDA CBER/OVRR rules, which includes re-mapping reactogenicity data, recording solicited AEs to CE and FACE; mapping unsolicited AEs, MAAE (PIMMC, NOCD) and death to AE and FAAE domains; relocating rule-specified information into domains where they belong, and covering the unique mapping of biological assay data, immunogenicity, and efficacy data | adverse events, crf, cdisc | source | 5 |
| 90 | Hard Coding: What is it? Why Does It Matter? What Should You Do When Faced With Such A Request? | Michael Nessly | A clear operating definition of what constitutes hard coding is not readily available in the public domain. We define hard coding as when data, which are not present in the collected source data, or if present are incorrect, are inserted or corrected programmatically into the dataflow. Hard coding is the software development practice of embedding data directly into the source code of a program or other executable object, as opposed to obtaining the data from external sources. Outside of regulated activities such as analysis and reporting of clinical trials, it appears that this is a common and non-controversial practice. To understand why hardcoding is very poor practice not to be taken lightly in clinical trials, one must be familiar with the concept of traceability. Traceability is, in essence, the provenance of data. Traceability is the property that enables the understanding of the data's lineage and/or the relationship between an element and its predecessor(s) | adam dataset, clinical trial, csv, raw data, cdisc | source | 4 |
| 91 | Have a Date with ISO? Using PROC FCMP to Convert Dates to ISO 8601 | Richann J. Watson | Programmers frequently have to deal with dates and date formats. At times, determining whether a date is in a day-month or month-day format can leave us confounded. Clinical Data Interchange Standards Consortium (CDISC) has implemented the use of the International Organization for Standardization (ISO) format, ISO 8601, for datetimes in SDTM domains, to alleviate the confusion. However, converting "datetimes" from the raw data source to the ISO 8601 format is no picnic. While SAS has many different functions and CALL subroutines, there is not a single magic function to take raw datetimes and convert them to ISO 8601. Fortunately, SAS allows us to create our own custom functions and subroutines. This paper illustrates the process of building a custom function with custom subroutines that takes raw datetimes in various states of completeness and converts them to the proper ISO 8601 format | proc fcmp, proc step, return, sas log, sample, time., numeric variable, cdisc | source | 15 |
| 92 | Have Meaningful Relationships: An Example of Implementing SDTM Special Purpose Data Set RELREC with a Many-to-Many Relationship | Kaleigh Ragan, Richann J. Watson |
The Related Records (RELREC) special purpose dataset is a tool, provided in the Study Data Tabulation Model (SDTM), for conveying relationships between records housed in different domains. Most SDTM users are familiar with a one-to-one relationship type, where a single record from one domain is related to a single record in a separate domain. Or even a one-to-many relationship type, where a single record from one domain may be related to a group of records in another. But what if there are two groups of records related to one another? How do you properly convey the relationship between these sets of data points? This paper aims to provide a clearer understanding of when and how to utilize the, not often encountered, many-to-many relationship type within the RELREC special purpose dataset | concomitant medications, sample, adverse events, raw data, clinical trial, medical history, sdtm domain, cdisc | source | 11 |
| 93 | How Can I Put This? - Using a pre-defined spreadsheet to explain your Pinnacle 21 Enterprise Issues | Mike Lozano | clinical trial, data cleaning, adam dataset, data validation rules, cdisc | source | 12 | |
| 94 | How to generate questionnaire data compliance report | Phaneendhar Gondesi | Achieving acceptable compliance is an important factor for the success of any clinical trial. Periodic monitoring of study data is key to ensure the expected compliance. Although the acceptable compliance rate varies from study to study, from assessment to assessment, the basic concept of calculating compliance remains the same. In this paper, we are going to discuss how to programmatically generate compliance report for most questionnaire (QS) data | sample, extract, dose interruption | source | 12 |
| 95 | How to Promote Yourself | Joshua M. Horstman | life sciences, clinical programming | source | 5 | |
| 96 | Implementing Agile Methodologies: Using Trello to Generate and Optimize Kanban Boards for Recurring SAS Programming Tasks | Gina Boccuzzi, Patrick Dowe |
Managing Data Management programming requests in a CRO environment presents multiple challenges. Data Management requests come in all shapes and sizes, but they all have one thing in co mmon - the sponsor wants the results as soon as possible. While most requests are on a recurring schedule (quarterly, monthly, weekly), the exact timing of each request is partly dependent on the receipt of external data, either from the sponsor or a third-party vendor. The importance of completing the requests in a timely manner while the data is still current makes prioritization and resource planning difficult. With varying frequencies and timelines coming from multiple sponsors, a robust system for tracking is needed to fulfill project requirements. Trello is one online application that allows the generation and optimization of Kanban Boards in the Agile methodology. A Kanban Board is a visual representation of the life cycle of a task from start to finish | proc freq, proof of concept | source | 7 |
| 97 | Importance of Creating a Learning Portal for Statistical Programming End-to-End Processes | Donna Hyatt, Brandy Cahill, Yogesh Pande |
In a highly regulated pharmaceutical industry, there are multiple Standard Operating Procedures ( SOPs) written with respect to the mandatory processes that a clinical/statistical programmer need to follow/practice in their day-to-day programming activities. Multiple SOPs/processes can cause confusion and lead to non- compliance with the processes followed within the Statistical Programming Department (SPD). To avoid this situation, the statistical programming leadership team produced an idea of creating a site/portal that contains important topics listed in one place, as hyperlinks with each topic explained from start to end. For this very reason, the statistical programming end-to-end (SP E2E) learning portal gained a lot of popularity within SPD among junior, new hires, and even the senior programmers. The goal of understanding which process should be followed and when it should be followed was achieved | clinical programming, statistical programmer | source | 6 |
| 98 | Industry metrics for standards utilization and validation rules | Sergiy Sirichenko | In recent years the industry has completed the adoption of standards for study data. It opens new opportunities. One of them is the availability of industry-wide analytics which allows us to understand common trends and reveal potential issues. At this presentation, we will share metrics for standards utilization and compliance with regulatory requirements. We will discuss the potential use of industry-wide analytics for the enhancement of existing standards, improvement of validation rules, and refinement of current processes | data cleaning, data validation rules, type i error, data wrangling | source | 13 |
| 99 | Integrated Trial Design Model Datasets? | Christine McNichol | Creating integrated datasets for an Integrated Study of Safety or Efficacy can be a complicated process. Aside from the complexities of integrating dissimilar studies and the sheer size of some of the datasets, it can be confusing how much of the defined study level SDTM and ADaM requirements are applicable. ADaM guidance for integration has not yet been finalized, and the source for those integrated ADaMs is not fixed. Because of this, many of the decisions on the path to integrated analysis datasets need to be made by those working on the individual submission. Among the many considerations in creating integrated datasets, one area that has not had much attention is integrated Trial Design Model Datasets. The first decision to be made is whether or not they are necessary. From there, if it is decided that they will be created, there are different methods that could be used to create them , as well as unique considerations for each domain | data cleaning, trial design model, trial summary, sdtm domain, cdisc | source | 9 |
| 100 | Integrating Practices: How Statistical Programmers Differ and Align Within User Groups | Valeria Duran, Radhika Etikala, Haimavati Rammohan |
An expectation in the pharmaceutical industry is that every entity within an organization is aligned on day - to-day practices. Our organization strives to streamline processes to achieve traceable and reproducible outputs for Statistical Programmers coding in different languages, such as R and SAS . Although the fundamentals in programming practices are aligned, the day-to-day workflow deviates at departmental levels: stakeholders have different expectations for how data is processed and how workflows should proceed. This paper takes a closer look at (1) Statistical Programming practices, specifically with Validation, Version Control, Coding Practices, and Verification, (2) the tools and techniques that have contributed to the success of our analogous workflow, and (3) future work towards further alignment. The insight from this paper has implications for how different entities within a workplace can work towards the same goals while meeting regulatory standards | data cleaning, clinical programming, statistical programmer, clinical trial, data science, data wrangling, independent qc, raw data, adam dataset, macro definition, cdisc | source | 15 |
| 101 | Introducing TFL Designer: Community Solution to Automate Development of Mock-up Shells and Analysis Results Metadata | Bhavin Busa, Navin Dedhia |
Presentation | safety population, adverse events, system organ class, output delivery system, treatment arm, treatment-emergent adverse event, confidence interval | source | 13 |
| 102 | Introduction of AWS Cloud Computing and its future for Biometric Department | Kevin Lee | When statistical programmers or statisticians starts in open-source programming, we usually begin with installing Python and/or R on our local computer and writing codes in a local IDE such as Jupyter notebook or RStudio, but as biometric team grow, and advanced analytics become more prevalent, collaborative solutions and environments are needed. Traditional solutions have been SAS servers, but nowadays, there is a growing need and interest for Cloud Computing. The paper is written for those who want to know about the Cloud Computing environment (e.g., AWS) and its possible implementation for the Biometric Department. The paper will start with the main components of Cloud computing - databases, servers, applications, data analytics, reports, visualization, dashboards etc., and its benefits - Elasticity, Control, Flexibility, Integration, Reliability, Security, Inexpensive and Easy to Start | sas programming, analytics platform, sas programmer, proc sql | source | 4 |
| 103 | Introduction of Developing Resistance Dataset | Jenny H. Zhang, Shunbing Zhao |
Antimicrobial resistance (AMR) is increasingly being recognized as a global threat to public health. Microbiology data provides important information that is used to guide clinical development of a new investigational drug. We had an opportunity to develop a special resistance analysis dataset (ADRST) for an infectious disease clinical trial. In contrast to other typical ADaM datasets, ADRST was different, and it was challenging to make it analysis ready. In this paper, we will introduce key variables derived in the dataset following FDA guidance on Antiviral Product Development, and showcase SAS codes for genotypic data, providing positioning variables for hundreds of amino acid sequence data for PR (Protease) and RT (Reverse Transcriptase) endpoints. We will also highlight the challenges of creating PR and RT related specifications and how programming steps were implemented | sample, binary variable, cdisc | source | 6 |
| 104 | IRS Audits - How to Beat Them | Larry Pon | internal audit, problem solving | source | 26 | |
| 105 | Key Safety Assessments following Chimeric Antigen Receptor (CAR) T-cell Therapy in Early Development Oncology | Leanne Tenry, Tamara Martin, Olga Belotserkovsky, Ce Zhou, Ouying (Abraham) Zhao |
In 2017 the U.S. Food and Drug Administration (FDA) approved the first chimeric antigen receptor (CAR) T-cell therapy to treat cancer, and over the past five years there have been six approvals by the FDA.2 CAR T-cell therapy harnesses a patient's white blood cells and genetically engineered T-cell receptors to target and attack cancerous cells. While the success of CAR T-cell therapy is favorable, the two most common toxicities associated with this treatment, Cytokine Release Syndrome (CRS) and Immune Effector Cell-Associated Neurotoxicity Syndrome (ICANS), are often severe and periodically life- threatening. The purpose of this paper is to explore key safety assessments associated with CAR T-cell therapy, which are vital to accurately monitor the drug's safety for patients. You will find the analysis of these two Adverse Events of Special Interest (AESI(s)), CRS and ICANS, evaluated within an overall summary of events table and time-to-event analysis utilizing a high-low bar graph | adverse events, macro variable | source | 13 |
| 106 | Key Statistical Programming Considerations in External Collaborative Clinical Trials | James Zhao, Hong Qi, Mary N. Varughese |
Collaboration between pharmaceutical industry (sponsor) and external partners is increasingly popular in drug development as it can be mutually beneficial. While study activities from start-up to Interim Analysis are often performed by the external partner, who conducts the study, the collected study data is transferred to the sponsor to support a regulatory submission which requires CDISC compliant SDTM and ADaM datasets. This division of responsibility has created many challenges due to inconsistent data collection among partners, the quality or format of data used for Analysis and Reporting, and the timing to access the data by the sponsor for evaluation and transformation according to the regulatory requirements | regulatory submission, clinical programming, raw data, data science, cdisc | source | 4 |
| 107 | Lessons Learned from a Retired SAS Programmer | Carey G. Smoak | I had a successful 38-year career as an epidemiologist and as a statistical SAS programmer. I retired in August of 2021 and have had time to reflect on my career. I have seen a lot of innovation in my 38 years. I used to write reports using DATA _NULL_. The advent of ODS and PROC REPORT has made report writing much simpler. Today's servers are smaller, but more powerful than the mainframe computers that I used early in my career. But I have also learned a lot of lessons along the way, and I would like to share the lessons that I have learned. I'll also share some tips on preparing for retirement |
sas programmer, statistical programmer | source | 3 |
| 108 | Let the Data Drive it and set your hands free - A Macro to create indicators for Special Interest AEs | Yiwei Liu, Jameson Cai |
Adverse Events of Special Interest (AESI) form an important part of the safety profile for a compound in submission or clinical study report (CSR). AESI identification is sophisticated because its information originates from MedDRA, Standardized MedDRA Queries (SMQs), Customized Queries (CQs), and/or Adjudicated Events performed by an independent Adjudication Committee (AC). Semi -annual MedDRA updates further complicate the reporting process. As AESIs are defined at a compound level, reporting AESIs should be consistent across all clinical trials of the compound. A macro is developed to create a set of ADaM variables that identify multiple types of AESI to streamline the process. It can be executed to automatically create all AESI tabulation and analysis reporting for multiple studies with a common definition. This method not only simplifies and standardizes the AESI reporting process, but also increases accuracy in AESI classification | adverse events, clinical trial, cdisc | source | 6 |
| 109 | Leverage and Enhance CDISC TAUGs to Build More Traceability for and Streamline Development of Efficacy ADaM in Oncology Studies | Xiangchen (Bob) Cui | CDISC Breast Cancer Therapeutic Area User Guide (TAUG-BrCa) [1] and Prostate Cancer Therapeutic Area User Guide (TAUG-PrCa) [2] presented 'ADEVENT' and 'ADDATES', independently in 2016, and 2017. One of primary reasons for the creation of the intermediate datasets is to support traceability by building into event dataset and/or date dataset through the triplet of SRCDOM, SRCVAR, SRCSEQ variables, and all potential dates from them are used to generate ADTTE (Data for the Time to Event Analyses). ADEVENT can also support another analysis dataset 'ADRESP' for best overall response, etc. FDA's guideline [3] provided examples for primary and supportive analysis of progression-free survival (PFS). The derivation of dates from tumor assessments is not straight forward and much more complex, especially when Response Evaluation Criteria in Solid Tumors (RECIST 1.1) [4] is applied in the derivation, where the confirmation of a complete response (CR) and partial response (PR) is required | adam dataset, progression-free survival, best response, data science, sas programming, procedures, primary endpoint, pd, sdtm domain, survival analysis, cdisc | source | 29 |
| 110 | Life Table Analysis for Time to First Event Onset | Abhinav Srivastva | Life Table Analysis is a useful way to study subject's survival with respect to an event of interest over a time period. It provides a good indicator of drug safety or toxicity over the course of a clinical trial due to the occurrence of a related event. For example, life table can be used to s tudy the relation between a drug which is highly immunogenic in nature and the type of events it can trigger over a time period, such as increased liver events indicating signs of liver disease . In this paper we take a graphical approach to represent this information which is then enhanced to add exploratory and interactive features for the reviewer. Data preprocessing is done in SAS, while all the plots are created in Python using open-source libraries such as matplotlib, seaborn, plotly and dash | life table, line chart, confidence interval, proc freq, clinical trial, adverse events | source | 11 |
| 111 | Low Code Approach to Clinical Application Development using SAS Studio Custom Step | David Olaleye | The SAS Custom Step feature offers a quick low-code application development platform that programmers can use to create code snippets and a user interface on top of the code snippets. It can be used to create dynamic user interfaces (UI) for end users with little or no programming experience to gain access to data assets at their site and perform tasks such as data exploration, analysis, and visualization. Custom steps come with cascading prompts and prompt hierarchies that enable the creation of data dependencies between control objects, thus enhancing the user experience as they query data and interact with the application. In this paper, I will show how to create a stand-alone SAS custom step for a demographic table and subgroup summary report and present another custom step for performing a propensity score match analysis. Finally, the two custom steps are added to a SAS Studio Flow to show how custom steps can facilitate and enhance a reporting and analysis process workflow | sas studio, demographics table, propensity score, class variable, by group processing, macro variable, date variable, macro definition, sample | source | 15 |
| 112 | Macro Code to Test Existence of Various Objects | Ronald J. Fehd | macro put, macro variable, macro function, %if-%then-%else, macro definition | source | 10 | |
| 113 | Macro to Automate Creation and Sync of Shell Document and TOC | Karen Walker, Jeff Cao |
In clinical trials, biostatisticians spend time in the development of Statistical Analysis Plan (SAP). The SAP contains mock-up shells for tables, figures and listings (TLF). The metadata provided by the mock shells in SAP/Shell documents includes titles, subtitles, footnotes, comments and formatting details. Statistical programmers are tasked with transferring this information from the mock-ups to an EXCEL file called table of content ( TOC), which SAS programs use to generate the final TFLs. Manua l transfer of this metadata can be labor -intensive, and prone to errors. Both Shell document and TOC are living documents, subject to changes over time. Several SAS users have developed EXCEL macro and others SAS macro to facilitate the generation of T OC. This paper introduces a novel approach that does much more beyond these. An add-in for W ord was developed to facilitate the creation of mock-up shell documents utilizing shell template library (STL) |
statistical programmer, excel, data science | source | 10 |
| 114 | Macro To Automate Crossover Review In Produced Outputs | Igor Goldfarb, Ella Zelichonok |
The goal of this work is to develop a macro that automates an important and time-consuming part of a final review process of produced tables, listings, and figure s (TLF) - crossover review. Performing this type of validation is a well-accepted practice typically performed manually by biostatisticians as the final stage in the multi-step quality check (QC) process. The propos ed tool can significantly simplify review work for biostatisticians who have to verify that TLF were ge nerated correctly, and they are consistent across the study and its different sections. Final review of the produced TLF represents an important task in a flow of raw data to final outputs ready for submission. Comparison, analysis and making sure that the actual outputs are all consistent across the study is a tedious procedure requiring scrupulous manual work that is subject to human errors. The proposed macro (developed in EXCEL VBA) automates this PROC ess |
excel, baseline characteristics, clinical trial, safety population, subgroup analysis, data science | source | 25 |
| 115 | Make You Holla' Tikka Masala: Creating User-Defined Informats Using the PROC FORMAT OTHER Option To Call User-Defined FCMP Functions That Facilitate Data Ingestion Data Quality | Troy Martin Hughes | You can't just let any old thing inside your tikka masala-you need to carefully curate the ingredients of this savory, salty, sometimes spicy delicacy! Thus, when reviewing a data set that contains potential tikka masala ingredients, an initial data qualit y evaluation should differentiate approved from unapproved ingredients. Cumin, yes please; chicken, the more meat the merrier; coriander, of course; turmeric, naturally; yeast, are you out of your naan -loving mind?! Too often, SAS practitioners first inge st a data set in one DATA STEP, and rely on subsequent DATA STEP s to clean, standardize, and format those data. This text demonstrates how user -defined informats can be designed to ingest, validate, clean, and standardize data in a single DATA STEP |
hash table, proc format, missing value, lookup, input statement, data cleaning, raw data, txt, data ingestion, proc fcmp | source | 10 |
| 116 | Making Multilingual Programmers - A Targeted Approach to R for Clinical Trials Training | Jagan Mohan Achi, Ashley Tarasiewicz |
Over the past few years many pharmaceutical organizations have encountered the same challenge - there is a wealth of training available on open-source languages like R, but very little training specific to the traditional clinical trial workflows we use on a daily basis. Companies are beginning to see and realize the benefits of having a multilingual programming team - they can incorporate the best parts of each programming language in their processes to maximize efficiency. In recent months, Jazz Pharmaceuticals has implemented several successful strategies to train their SAS programmers in the use of R. This paper will examine the challenges and successes of finding the right training content, format, and candidates for teaching clinical programmers how to use R | clinical programming, clinical trial | source | 5 |
| 117 | Making Publication Metric Tracking Easy: Using an Automated, Integrated System of R, SAS, and Microsoft Power BI to Ease the Pain of Assessing Publication Metrics | Joshua J. Cook, Jessica Truett |
Publication metrics are increasingly being used to measure individual- and organization-level productivity and impact within academia and industry. Additionally, these metrics, including publication counts over time, are being used to make business decision. Historically, publication metrics have not been the easiest thing to quantify and manage. This is primarily due to the manual nature of querying PubMed from the web, which is difficult to do for multiple highly published authors with non-unique names. Instead of manually obtaining this data, it is much more feasible to leverage the R programming language and various reporting systems to manage this data. Importantly, many of these tools are free! This text demonstrates how R, a few R packages, and reporting systems such as Quarto and Microsoft Power BI can be integrated into a reproducible system capable of streamlining the collection, wrangling, and reporting process of publication data for stakeholders | data wrangling, bar chart | source | 16 |
| 118 | Masters of the Table Universe: Creating Table Shells Consistently and Efficiently Across All Studies | Michael Hagendoorn, Ran Li, Mimi Vigil, Shan Yang |
Creating specifications for tables, listings, and figures (TLFs) is traditionally not for the faint of heart. Over the course of weeks or more, our brave author valiantly translates the statistical analysis plan into sometimes hundreds of pages of detailed Microsoft Word-formatted specifications for their study. Meanwhile, two offices over, another author is going through the same painful process of creating a separate Word shell for their own study… and so on. While capturing TLF shells for each study in this manner is the norm, we developed an alternate approach by establishing a single set of shells at the compound level that provides the metadata for all CSR and integration TLFs across the entire product | excel, mock shell, data science, clinical trial | source | 15 |
| 119 | Meta-Analysis in R | Lin Gu | With the accumulation of a large body of information and data in clinical trial studies, questions are often raised to estimate the pooled effect size for similarly designed studies for a particular research topic. Meta-analysis is such a statistical technique employed for this purpose and has become a universally accepted research tool. In drug development, the U.S. Food and Drug Administration (FDA) has published draft guidance on how to conduct a meta-analysis of randomized clinical trials to evaluate safety risks associated with the use of human drugs or biological products within the framework of regulatory decision making (FDA, 2018). SAS has been a widely used tool to perform a wide range of statistical analyses, but SAS procedures for meta-analysis have been lacking. On the other hand, R is a powerful and increasingly popular software not only for statistical analysis but also for visualization | effect size, hazard ratio, proc calis, sample size, forest plot, data science, sample, proc mixed | source | 8 |
| 120 | Metaverse in the Healthcare - More Real than imagined! | Aman Bahl | The metaverse is a term used to describe a collective virtual shared space created by the convergence of physical and virtual worlds. In healthcare, the metaverse can be a virtual space where healthcare professionals and patients can interact, exchange information, and collaborate on treatments and research. Healthcare professionals can use the metaverse to simulate medical procedures and surgeries, collaborate on research and development, and engage in virtual medical training and education. The metaverse can also provide a platform for medical conferences and events, enabling professionals to connect and share knowledge and expertise from around the world. Overall, the metaverse has the potential to revolutionize healthcare by providing more accessible and personalized care, improving the efficiency and effectiveness of medical treatments, and fostering greater collaboration and innovation among healthcare professionals | clinical trial, clinical research, artificial intelligence, pharmaceutical industry, clinical data, machine learning, health insurance, insurance claims | source | 14 |
| 121 | Methods of a Fully Automated CONSORT Diagram Macro %CONSORT | Jeffrey P. Meyers | The CONSORT diagram is commonly used in clinical trials to visually display the patient flow through the different phases of the trial and to describe the reasons patients dropped out of the protocol schedule. They are very tedious to make and update as they typically require using different software, such as Microsoft Visio or Microsoft PowerPoint, to manually create, align, and enter the values for each textbox. There have been several previous papers that explained methods of creating a CONSORT diagram through SAS1, but these methods still required a great deal of manual adjustments to align all of the components. The %CONSORT macro removes these manual adjustments and creates a fully automated yet flexible CONSORT diagram completely from data. This presentation is a description of the | adverse events, group by, data step, macro call, proc sql, macro parameter, proc freq, macro variable, character variable, unique key, macro loop, %if-%then-%else | source | 21 |
| 122 | No More Manual PDF Bookmarks! An Automated Approach to Converting RTF Files to a Consolidated PDF with Bookmarks | Tyler Plevney | Preparing output documents for data review meetings and presentations can be a tedious process. What if I told you, it does not have to be? Instead of opening and closing multiple rich text format (RTF) file or EXCEL files with various sorts and filters to review, sometimes hundreds of outputs, why not consolidate them in an easy-to-review PDF document with all outputs easily separated with linked bookmarks? Doing this manually by converting RTF output files to PDF can take a long time. Add on the time to t ype out the bookmarks for each individual output and it does not seem worth it. In this paper I will present an automated way to convert an entire folder of RTF outputs to PDF while simultaneously changing the filenames to the bookmark text once consolidated into a combined PDF file. This is all done via a SAS program containing some VBScript code for file moving and converting in conjunction with an EXCEL spreadsheet containing the RTF output filenames and the title of the outputs |
ods rtf, ods pdf, excel, call execute, macro variable | source | 11 |
| 123 | Novel Applications of Real World Data in Clinical Trial Operations: Clinical Trial Feasibility | Sherrine Eid, Samiul Haque, S. Robert Collins |
INTRODUCTION: Today Real World Evidence (RWE) derived from Real World Data (RWD) is essential to explore target patient populations (TPP) and more accurately inform robust initial trial hypotheses and expected performance. Applying RWD creates more patient-centric protocols, identifies and reduces operational risk and development costs, while also increasing the likelihood of regulatory approval and patient retention. METHODS: Three scenarios of conventional and novel methods of informing target patient populations intended for Phase II and III clinical trials were explored and compared on overall population counts, demographic and baseline characteristic distributions, and scientific robustness using SAS Viya and Python. The first scenario (STRINGENT) implements a conventional approach where a subject matter expert defines the study TPP based on expertise and literature | clinical trial, baseline characteristics | source | 13 |
| 124 | Novel Applications of Real World Data in Clinical Trials: External Control Arms | Sherrine Eid, Samiul Haque, S. Robert Collins |
INTRODUCTION: Regulatory agencies have issued guidance on selection and evaluation of Control Groups in clinical trials. External control arms (ECA) may be sourced from prior clinical trial data (individual or pooled), or observational, real-world data (RWD), such as registries, electronic health records, and medical or pharmacy claims. By reducing or eliminating the need to enroll control participants, a synthetic or external control arm can increase efficiency, reduce delays, lower trial costs, and speed lifesaving therapies to market. ECAs should ideally be temporally and clinically relevant to the treatment arm to minimize bias and require sufficient patient-level data to ensure a statistically robust comparison. We address key considerations, risks and possible mitigation strategies when employing ECAs in their trial designs | clinical trial, clinical data | source | 6 |
| 125 | OCS Update: OCS Clinical Services & the Traceability Assessment | Jesse Anderson | trial elements, record count, adverse events, data visualization, adam dataset, clinical data, data standard, cdisc | source | 25 | |
| 126 | Optimizing Efficiency and Improving Data Quality through Meaningful Custom Fix Tips and Explanations | Jen Manzi, Julie Ann Hood |
Prior to submitting study data to any regulatory agency, the standardized data is evaluated using at least one validation tool to ensure data conformance and quality. The issues in the resulting report are then triaged, first to determine the cause of the issue, then to decide on the best course of action to resolve or address each one. Knowing which issues should be researched and which can only be addressed through explanations in the Reviewer's Guide is crucial to efficiently utilize a validation report. Guidance provided by an organization on how to approach validation issues can help save time and ensure the issue is addressed correctly, resulting in higher quality and consistent data across studies. This paper will focus on steps to create meaningful Fix Tips that will enable users to quickly evaluate researching and resolving an issue. It will also include examples of when an explanation is the best option and the details needed to create comprehensive standardized explanations | data cleaning, adverse events | source | 7 |
| 127 | Patient's Journey using Real World Data and its Advanced Analytics | Kevin Lee | Real World Data (RWD) is data collected outside of clinical trial study, and Real-World Evidence (RWE) could be achieved through the insight from RWD. RWD sources come from EMR, health insurance claims, genomic data, and IoT from apps and wearables. RWD anonymized patient data has revolutionized how companies view patient data since it captures longitudinal pharmacy prescription, medical claims, and diagnosis. The paper is written for those who want to understand how RWD patient data are collected and how they could be analyzed to support pharmaceutical companies. Mainly, RWD patient data could support patient analytics, commercial analytics, and payer analytics such as source of business, switch of prescription, payment method, market analysis, promotional activities, drug launch and forecasting. The paper also discusses the technology that data scientists use for RWD such as Data Warehouse, Data Visualization, Opensource Programming, Cloud Computing, GitHub, and Machine Learning | clinical trial, machine learning, life sciences, medical history | source | 4 |
| 128 | PHUSE Safety Analytics Working Group - Overview and Deliverables Update | Nancy Brucken, Clio Wu, Mary Nilsson, Greg Ball |
The PHUSE Safety Analytics Working Group, a cross-disciplinary cross-industry collaboration, is working to improve the strategy and implementation of clinical trial safety assessments for medical research, leading to better data interpretations and increased efficiency in clinical drug development and review processes. The Working Group has produced numerous deliverables (Conference Posters and Presentations, White Papers, Publications, Blogs, etc.,) over the past 10 years and has many ongoing projects. This presentation will provide an overview of the Working Group and its associated project teams, and share an update of the teams' progress, key deliverables for awareness and a summary of ongoing projects | adverse events, clinical trial, stage 1, stage 2, trial elements, phase ii | source | 5 |
| 129 | Picking Scabs and Digging Scarabs: Refactoring User-Defined Decision Table Interpretation Using the SAS Hash Object To Maximize Efficiency and Minimize Metaprogramming | Troy Martin Hughes, Louise S. Hadden |
Decision tables allow users to express business rules and other decision rules within tables rather than coded statically as conditional logic statements. In the first author's 2019 book, SAS Data -Driven Development, he describes how decision tables embody the data independence that data -driven programming requires, and demonstrates a reusable solution that enables decision tables to be interpreted and operationalized through the SAS macro language. In their 2019 white paper Should I wear pants?, the authors demonstrate the configurability and reusability of this solution by utilizing the same data structure and underlying code to interpret unrelated business rules that describe unrelated domain data -pants wearing and vacationing in the Portuguese expanse | macro facility, hash table, data validation rules, macro definition, if-then-else, response variable, proc sql, dataset, proc fcmp, proc import, macro variable, lookup | source | 15 |
| 130 | Planning Your Next Career Move - Resume Tips for the Statistical Programmer | Sapan Patel, Lisa Pyle |
The statistical programming market is HOT! No doubt about it, and you want to take the opportunity to build your career and move to the next level. A resume is your first impression when submitting to a company. It is circulated and used by many people in an organization to screen, evaluate and determine whether you fit the job role. As a prospective candidate, you must take time and effort to ensure that your resume is current, streamlined and highlights essential work experience related to the job you are applying for. Interviewers like us review many resumes, and over time we've agreed that there are a couple of quick wins to help the interviewer evaluate your resume and determine if you will fit the job role. This paper provides quick tips to prepare and modernize your resume for high-impact review so that you get that next interview | statistical programmer, clinical programming | source | 5 |
| 131 | Post-surgical opioid pain medications usage evaluation using SAS and Excel | Marek K. Solak | Post-surgical pain management is often based on opioid administration. Opioids offer efficient pain control, but their use may, in some cases, lead to dependency. Opioid data for exploratory and clinical studies needs to be collected accurately and monitored periodically. To address potential dependence problem, this paper provides a systematic method, using EXCEL (with well-designed structure for data collection) and SAS for periodical opioid usage (converted to morphine equivalents) evaluation. There are many ways to approach evaluation and the presented method is only a proposal for consideration. So far, it is not being used or implemented, up to the author's knowledge, anywhere. Reports are provided in the form of tables and boxplots. If higher than expected patient opioid usage is detected, medical staff may try to offer alternate, pain reduction, protocols [1] |
excel, proc step, box plot, proc import, proc transpose | source | 11 |
| 132 | Practical Tips for Effective Coaching for Leaders and Managers in Organizations | Priscilla Gathoni | As an executive or senior-level member of the organization, do you wish you had better skills in coaching, similar to a skilled professional? As a people manager, do you want to help your team members improve their performance in all aspects important to them? Do you find it challenging to manage people and their problems? Is developing a coaching mindset hindering you from being truly present in your coaching conversations? Do you struggle to handle different personality types? Coaching is a vital tool for every leader and manager to have at their disposal if they are committed to fulfilling and advancing the potential of their individuals and teams and improving organizational commitment. Coaching relationships are built upon truth, openness, and trust and allow the person being coached to be responsible for their own results and think creatively | source | 10 | |
| 133 | Program It Forward! - Thinking Downstream when Coding | Frank Canale | Within the pharmaceutical industry during these times, we as SAS users are often faced with short timelines, demands for quick turnarounds, having to create reoccurring or multiple deliverables…all while doing so with decreasing resources. Couple this wit h ever increasing standards and standardization efforts, and we sometimes find there's less of a need to create programs from scratch. Instead, we try to harvest reusable code - copied from a prior study and modified to run on current study. However, in many instances this process causes unexpected headaches as one tries to understand what the original programmer wanted to do, why, and if it will even work for the next project! | proc step, macro loop | source | 19 |
| 134 | Programmer-s Perspective: Step into Awareness Regarding Clinical Trial Deliverables and Their Impact | Lyma Faroz, Jinit Mistry |
There are a variety of deliverables that statistical programmers work through in their careers. Those include producing data sets, tables, listings, figures, patient narratives, and patient profiles, among many others. There is often a natural tendency for programmers to focus on generating such outputs without taking a step back and understand why those outputs have been requested in the first place and how they help the clinical study teams in decision making and assessing trial outcomes . When a programmer understands the bigger picture behind why specific analytical output is requested, how it is used to gauge trial outcomes, and how it contributes to the overall strategy in getting investigational product to patients in need, such insights can help increase the quality of production and QC output | clinical trial, adverse events, interim analysis, statistical programmer, progression-free survival, duration of response, serious adverse event, sample, concomitant medications, clinical data | source | 10 |
| 135 | Proposal for New ADaM Paired Variables: PARQUAL/PARTYPE | Elizabeth Dennis, Monika Kawohl, Paul K. Slagle |
For more than a decade, producers have struggled to create unique PARAM values to fully describe each analysis parameter. Even when it is less efficient to have fully unique PARAMs, it has been the requirement. With ADaM IG v3.0 this is expected to change. PARQUAL (and the paired variable PARTYPE) are expected additions that will allow PARAM to identify multiple analysis parameters. These are special purpose variables that are intended to be an exception, not a common occurrence. In most cases they will be unnecessary. However, when the meaning of PARAM essentially remains unchanged except for a single qualifier (such as 'Investigator' or 'Central Reader'), PARQUAL can be a useful tool to simplify PARAM. This paper will summarize the current requirements of PARAM and PARCATy. It will review the history of past proposals for PARQUAL and its existence in Therapeutic Area User's Guides (TAUGs) and other documents | controlled terminology, adam dataset, adamig, cdisc | source | 7 |
| 136 | Pros and Cons of Freelancing | Pradeep Bangalore | sas programmer, confidence level, clinical trial, health insurance, life sciences, excel | source | 10 | |
| 137 | Put on the SAS Sorting Hat and Discover Which Sort is Best for You! | Charu Shankar, Louise S. Hadden |
Sorting in SAS is an expensive process in terms of both time and resources consumed. In this session, prepare to explore some of the common and lesser known sorts that SAS provides. Become like the sorting hat in Harry Potter! Instead of waiting with baited breath for your team (or data) to be sorted, get the inside scoop and learn about the dynamic processes that go on behind the scenes during sorting that will enable you to pick the very best sort for your circumstances. Learn about some fantastical, magical SAS sorting teams: bubble sort, quick, threaded and serpentine. Behold the effervescent bubble sort! In a hurry? Take a look at the quick sort. Looking for superior efficiency? Consider the threaded sort. See how the hissing serpentine sort in SAS, like the slithering serpent Nagini sliding surreptitiously through walls, can come in handy! Which sort will you choose - or which sort will choose you? | proc step, proc sort, proc surveyselect, proc sql, sample, order by | source | 8 |
| 138 | Questionnaires in ADaM: ADSF36 | Keith Shusterman, Megan O'Grady, Mario Widel |
The 36-Item Short Form Survey (SF-36) questionnaire is one of the most widely utilized generic health measures in clinical trials. The SF-36 User Manual recommends that population norm-based scores should be used to simplify interpretation of the data. The responses to the SF-36 are utilized to derive a physical component summary, and a mental component summary. Due to norm-based score methods, the process for deriving these two summary values from the raw scores is complicated. It is common for an independent party to derive the summary scores from the raw scores to be included as a source in the SDTM QS domain. However, there may be a compelling reason to derive these values in -house. For example, vendor algorithms are typically proprietary and cannot be incorporated into submission data and documentation. In this case, the process of deriving the summary scores needs to be appropriately documented in ADaM in a clear and traceable way | standard deviation, adam dataset, cdisc | source | 13 |
| 139 | R Package Qualification: Automation and Documentation in a Regulated Environment | Paul Bernecki, Nicole Jones, Uday Preetham Palukuru, Abhilash Chimbirithy |
In the recent years, there is an increasing trend in using open-source software, such as R, in clinical trial analysis and reporting (A&R). The qualification of an R package is a critical step in ensuring its quality and its compliance within the highly regulated clinical trial environment. Given the sheer number of R packages, automating qualification process is necessary to ensure consistent qua lity and increased efficiency. Aspects of our company's R package qualification process were presented in PharmaSUG 2022. The current paper focuses on some updates made since then. Firstly, an internal R package mkqualify streamlines the qualification process further while incorporating inputs from human reviewers. This package generates most of the documentation used to support the qualification. Secondly, the open source riskmetric package generates the risk scores associated with an R package | insurance risk score, clinical trial, data cleaning, error message | source | 16 |
| 140 | R Package Quality & Validation: Current Landscape | Philip Bowsher | Manyorganizationsaremigratingtoanopen-sourcebackboneforthenext-genClinicalTrialsenvironment.QualityteamsoftenhavequestionsaboutimplementingtheopensourceenvironmentsuchasmanagingRpackagesandprocessesaroundvalidation/qualification/verification.ThetermValidationcanhavemanymeaningsbutusuallyreflectstheinternalprocesseswithinanorganizationandcanbeimplementeddifferentlypertheinterpretationofguidance.PharmaceuticalcompanieswilloftenaskforsampleStandardOperatingProcedures(SOPs)forvalidatingopensourcesoftware.ThiscanbeachallengeasofteneachpharmaisuniqueintheexecutionofSOPs.TheinformationbelowcanbeusedtoaidtheQualityTeamsintheiruniqueexpectationsandSOPsaroundvalidation.Thispaperwillhighlightthecurrentlandscapeanddiscusskeyinformationforprogramleadersandqualityprofessionalsimplementingthesoftwareandoverseeingthechangemanagement.Manyofthekeyitemsdiscussedinthepapercanbefoundat:https://solutions.posit.co/envs-pkgs/environments/validation/ | data cleaning, clinical trial | source | 9 |
| 141 | R Tables via GT for Regulatory Submissions | Philip Bowsher | adverse events, trial elements | source | 19 | |
| 142 | Rate Setting | Joshua M. Horstman | health insurance, life sciences, clinical programming | source | 5 | |
| 143 | Real Projects, Real Transition, Really Revolutionary - Transitioning to R for Biometrics Work | Danielle Stephenson, Rebekah Oster, Alyssa M. Wittle |
Technology is developing quickly and staying on the cutting edge has its challenges. Often, diving into something new is an intimidating idea for programmers, companies, and sponsors - especially when the day-to-day work must continue. The pharmaceutical industry has been entrenched in SAS for decades, and the time has come to explore open-source and dedicate the time to figure this out. What are the ups and downs of this transition? How can the learning curve become a bit less curvy? Can TFLs and CDISC-compliant datasets be created using R? We will dive into what it looks like for a team to go from SAS -fluent to multi-lingual in real time, with real projects, and ways to ensure quality while making the transition | data wrangling, proc step, proc sql, missing value, cdisc | source | 11 |
| 144 | Real Time Analytical Reporting Using OpenFDA | Shubhranshu Dutta | One of the main challenges during the drug developm ent process is knowing how a drug in a clinical tri al interacts/might interact with other concomitant medications taken by a patient. The FDA has enabled access to real- world data via OpenFDA. With the use of OpenFDA APIs - specifically by creating daily refreshed data reports - we can track the emergence/development of common adverse reactions or drug interactions along with the se verity and seriousness of such adverse events across various p atients. I will be pulling data from OpenFDA APIs a nd creating reports that reflect real time data from the OpenFDA database for the purpose of analysis. I will also be exploring the metadata and discussing the interactive charts provided by OpenFDA to help with the right query selection from the database | adverse events, clinical trial | source | 8 |
| 145 | Real World Evidence in Distributed Data Networks: Lessons from a Post-Marketing Safety Study | Matthew T. Slaughter, Denis Nyongesa, John Dickerson, Jennifer Kuntz |
As a case study to illustrate both opportunities and challenges in distributed data networks, this paper will focus on the implementation of a post-marketing safety study in the Healthcare Systems Research Network (HCSRN) via the associated Virtual Data Warehouse (VDW) common data model. In response to an FDA post-marketing requirement, this study establishes the incidence of angioedema in chronic heart failure patients treated with sacubitril/valsartan and incorporates data from multiple distributed data networks, including HCSRN. Distributed data networks present exciting opportunities for gathering real-world evidence by pooling standardized datasets across institutions. Common data models facilitate the efficient allocation of programming work to conduct analysis while allowing participating sites to retain control of their own data | sample size, sample, independent qc, data cleaning, proc sql | source | 7 |
| 146 | Recent updates in BIMO Technical conformance guidance and use case scenario | Vaibhav Garg, Sreedhar Bodepudi |
Since 2017, FDA has recommended that Sponsors submit Bioresearch Monitoring (BIMO) outputs along with a study electronic submission (eSub) package. The BIMO package includes Clinical Study -Level Information, Subject -Level Data Listings by Clinical Site, a Summary-Level Clinical Site Dataset (clinsite.xpt), Data definition file (define.xml) and BIMO Data Reviewer guide (bdrg.pdf). FDA also released detailed requirements per the Bioresearch Monitoring (BIMO) Technical Conformance Guide (TCG) to detail BIMO submission package standards. The BIMO TCG provides detailed specifications, recommendations, and general considerations for preparing and submitting all submission related components | clinical data, clinical trial, protocol deviations, cdisc | source | 4 |
| 147 | Regulatory Data Submission and CDISC Compliance: Sponsor and Vendor Collaboration Best Practices | Tabassum Ambia | Submission of Electronic Data requires compliance to CDISC and evolving regulatory agency data standard requirements (e.g, FDA and PMDA). Close collaboration and partnership between sponsors and vendors throughout the data collection and analysis process is critical to ensure that data/submission packages efficiently meet requirements. Pinnacle 21 is widely used for data validation as well as for the creation and/or validation of define documents. Standards specified by FDA/PMDA can be pre -loaded in P21 to generate submission documents and validate datasets and define packages. Alnylam has been using P21 Enterprise to ensure compliance between internal programming and data management departments, and has expanded access to this platform/process to our data management vendors to centrally load data, generate defines, run validation, document data and mapping issues and collaborate to reconcile all issues | data cleaning, controlled terminology, clinical trial, cdisc | source | 5 |
| 148 | Repetitive Analyses in SAS - Use of Macros Versus Data Inflation and BY Group Processing | Bradford J. Danner, Indrani Sarkar |
While preparing clinical reports, we are commonly tasked to produce multiple outputs of the same analysis, using a different endpoint of interest, or slightly different populations of interest, or according to a suite of categorical subgroups. Naturally, we can accomplish such repetitive tasks efficiently using SAS with MACRO processing. Alternatively, "data inflation", an approach that does not employ MACRO processing, with careful use of OUTPUT statements in the SAS DATA STEP, we 'inflate' the source data, so that all variations of the multiple analyses are in one dataset, which can then pass-through analysis procedures once with BY group processing. The objective of this article is to demonstrate these two approaches, either of which can be used for the purpose of analysis and review. Outputs from both approaches can be consolidated and exported into one source which will make the review process less time-consuming |
&variable, by group processing, output, sample, analysis set, binary variable, macro variable, out=, proc phreg, proc step, regression analysis | source | 6 |
| 149 | RESTful Thinking: Using R Shiny and Python to streamline REST API requests and visualize REST API responses | Laura Elliott, Crystal Cheng |
rest api, data visualization | source | 9 | |
| 150 | Revolutionizing Statistical Outputs Validation: a Product Demonstration of Verify, an ML-powered Automation Solution Streamlining the Validation Process | Ilan Carmeli | clinical trial, feature extraction, pharmaceutical industry, data cleaning | source | 11 | |
| 151 | Running Parts of a SAS Program while Preserving the Entire Program | Stephen B. Sloan | The Challenge: We have long SAS programs that accomplish a number of different objectives. We often only want to run parts of the programs while preserving the entire programs for documentation or future use. Some of the reasons for selectively running parts of a program are: Part of it has run already and the program timed out or encountered an unexpected error. It takes a long time to run so we don't want to re-run the parts that ran successfully. We don't want to recreate data sets that were already created. This can take a considerable amount of time and resources and can also occupy additional space while the data sets are being created. We only need some of the results from the program currently, but we want to preserve the entire program. We want to test new scenarios that only require subsets of the program | proc step, run, macro variable, sas enterprise guide | source | 4 |
| 152 | Running Python Code inside a SAS Program | Jim Box | Did you know that you can execute Python code inside a SAS Program? With the SAS Viya Platform, you can call PROC PYTHON and pass variables and datasets easily between a Python call and a SAS program. In this paper, we will look at ways to integrate Python in your SAS Programs |
sas log, proc step, proc python, macro variable, macro definition, dataset, histogram | source | 9 |
| 153 | SAS Enterprise Guide: 8.x (What is new!) | Ajay Gupta | sas enterprise guide, sas studio, sas programming, sas 9, sas viya, output delivery system | source | 16 | |
| 154 | SAS LOG/LST Filename improvement for easier: code review, code audit and project development history and chronology. | Zeke Torres | The SAS config file is powerful and useful. In this example, we show how to customize the names of the Log and List files from the code we run, and obtain a useful new name as the | macro facility, macro definition, proc printto, proc step | source | 11 |
| 155 | SAS Macro Design Considerations to Generate Subgroup Table and Forest Plot in Oncology Studies | Yizhuo Zhong, Christine S Teng |
Subgroup analyses are essential when there is anticipated heterogeneity within a target population and potential inconsistency of therapeutic response to the study treatment. Subgroup tables and forest plots are commonly required for the efficacy analysis in oncology studies. It is important that the analysis programs can handle additional subgroups which may be added for exploratory analyses or agency requests. This paper will review some common scenarios using categorical variables. These subgroup variables are generally provided through the subject level dataset such as ADSL and efficacy datasets supporting the analysis of overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). The proposed design of a SAS macro would allow flexibility to add additional subgroups, handle different sorting orders within a subgroup, and display text of the output without the need to update the analysis programs | forest plot, in=, macro parameter, macro definition, sample | source | 10 |
| 156 | SAS Macro to Automate Programmer Status Sheet | Jake Adler, Denise Rossi, Assir Abushouk |
When programming in clinical trials, you typically have a Production and QC programmer. For many, tracking the programs completion is dependent on the Production and QC programmers manually updating an EXCEL sheet with the status of their programming. With in this sheet, both programmers report the date their program was completed. This method presents problems, as the manual input can result in user error; it can be very easy to forget to update the date after several rounds of feedback and updating, particularly when these comments and updates occur in the same day and there is no real way to indicate the new completion date. Using Dynamic Data Exchange (DDE) in a SAS macro can automatically input the completion dates and the user running the program. This macro will also be checking for log issues, as well as QC compare mismatches that are reported into the EXCEL sheet |
excel, clinical trial | source | 5 |
| 157 | SAS SQL 101 | Charu Shankar | Presentation | proc sql | source | 26 |
| 158 | SAS System Macros to Summarize the COMPARE Procedure Results and SAS Logs for a Directory or Single File. | Kevin R. Viel | A moderate project in clinical trial programming might have 30-50 ADaM data sets and 200-500 tables, listing, and figures. The convention is 100% independent programming with a comparison of the data sets. When the SAS System is used, this may be referred to as DP-PC: Double-Programming, PROC COMPARE. Further, a check of the SAS logs for certain words or phrases indicative of unacceptable NOTES, ERROR messages, or WARNING is also appropriate. At times, a lead programmer or biostatistician may want to verify progress or confirm the attestations that Validation is complete yet reviewing so many SAS .lst and .log files manually is cumbersome and a sample may not suffice |
proc step, data cleaning, proc sql, macro parameter, macro function, proc datasets, production run, call missing, clinical trial, proc compare, proc print, proc sort, proc append, macro call, macro variable, order by, macro quoting function, macro conditional, proc printto, cdisc | source | 41 |
| 159 | SAS-based Method for PK Noncompartmental Analysis and Validation | Hui Mao, Lixin Gao |
Pharmacokinetics (PK) analysis is an important part of clinical trials that explores drug effects in humans. Noncompartmental analysis (NCA) is a model-independent and cost-effective method as the industry consensus. PK commercial software WinNonlin and open-source tools are traditional approaches for such analyses. This paper presents an alternative SAS-based PK NCA method that derives and validates PK parameters like Cmax, tmax, λz, t1/2, AUClast, AUCinf, CL, Vz, etc. The key benefit of such a method is to promote operational efficiency and compliance because no intermediate software is needed. It can be directly embedded in the statistical programming to create CDISC SDTM PP datasets, PK summary tables, and figures. The SAS code can be directly used for regulatory submission | pk parameter, clinical trial, regression analysis, nca, macro definition, data science, data cleaning, cdisc | source | 5 |
| 160 | SDTM Variables You Might Forget About | Nadiia Pukhliar, Dariia Tsyhanenko, Iryna Kotenko |
With every new version of the SDTM/SDTMIG more and more examples of raw data mapping are presented, more details on specific variables are described. However, in practice the same mapping rules are transferred from study to study with no changes and everything less common is either mapped to the Supplemental Qualifiers SUPP-- datasets, the Findings About Events or Interventions (FA) domain or is not submitted at all. The paper collects several cases of SDTM mapping providing more coherent and detailed representation of collected data. Special attention in the paper is given to the Supplemental Qualifiers datasets examining standard supplemental qualifiers name codes per the SDTMIG. Further, we are sharing tricks on using the ADaM IG to get standard qualifier names in SUPP-- domains | adverse events, clinical trial, data science, concomitant medications, sdtm domain, sdtm programming, cdisc | source | 10 |
| 161 | Secret SAS - You Can Write eCTD Using ODS Word in SAS | Peter J. Lund, Anusha Minnikanti, Calins Alphonse, Julie Stofel |
The Electronic Common Technical Document (eCTD) standard is the standard format for submitting applications, amendments, supplements, and reports to FDA's Center for Drug Evaluation and Research (CDER) and Center for Biologics Evaluation and Research (CBER) (https://www.fda.gov/drugs/electronic- regulatory-submission-and-review/electronic-common-technical-document-ectd). In a nutshell, the eCTD standard requires linked information in tables of contents, tables and figures. It also requires specific formatting for the sections of the report and the tables of contents. These requirements are typically met with manually inserting report information into a Microsoft Word document and using the formatting capabilities of Word, a time consuming and error-prone process. However, the SAS ODS Word destination (pre-production) available in version 9.4 M7 provides functionality to create reports in eCTD format with only a few manual steps, all point-and-click, in Word when complete |
proc step, macro quoting function | source | 9 |
| 162 | Sensitivity Analysis for Missing Data Using Control-based Pattern Imputation | Jun Feng, Jingmin Liu |
Randomized controlled clinical trials are known to be an effective way to minimize bias and draw convincing conclusions on the efficacy and safety of a drug, but the data quality and the statistical methods used for the analyses will highly influence the results. Inevitable scenarios such as protocol deviations and subjects lost to follow-up will lead to missing data. How the missing data are handled is crucial for the integrity of the statistical analysis, especially for efficacy endpoints. Sensitivity analysis is a useful method to stress-test the credibility of statistical conclusions and explore the impact of the missing records. Using imputation methods to fill in these missing observations and consider imputation simulation results through sensitivity analysis will render a more robust statistical conclusion | missing value, imputation, proc step, sensitivity analysis, treatment arm, proc mixed, clinical trial, proc mi, proc mianalyze, data science, confidence interval, treatment effect | source | 7 |
| 163 | Shifting the drug development paradigm with Adaptive Design and master protocol | Aman Bahl | Just as other industries have moved toward more flexible methodologies that foster continual improvement and operational efficiencies, the clinical development industry is slowly adopting innovative trial designs, after encouragement from regulatory agencies to speed progress, reduce inefficiencies, and improve success rates. In recent years there has been a rise in more flexible trial designs compared to traditional adaptive and group sequential trials. To get medicines to patients faster, clinical trials need to be more efficient, with fewer delays and lower costs. Adaptive trials, basket studies, platform trials, synthetic control arms and dose optimization studies are some of the options for achieving these goals. Innovative clinical trial designs have the potential to shift the drug development paradigm towards more patient-centered, data-driven, and efficient approaches | clinical trial, adaptive design, clinical research, sample size, sample, interim analysis, data science, sample size re-estimation, phase ii, likelihood function, multiplicity, treatment arm, randomization, group sequential design | source | 16 |
| 164 | Smart Use of SAS Output System and SAS Macro for Statistic Test Selection | Mengxi Wang | Choosing the optimum statistical test is crucial to generate accurate results in a quantitative research study. However, digging through piles of diagnostic test reports for useful results may, at times, be a tedious task for beginners in the field of biostatistics, especially when there are many variables to examine. The purpose of this paper is to share an efficient way to automate the process of selection between non-parametric and parametric tests with the SAS output system and SAS macro. The statistical tests that are used as examples are some of the most widely used tests in Table 1, the summary table of population characteristics most medical publications: Chi-square test, Fisher exact test, Independent two-sample T-test, Wilcoxon-Mann-Whitney test, ANOVA test, and Kruskal-Wallis test. The test selection is based on variable type, variable category, sample size, and sample distribution | sample, normality test, p-value, proc step, interval variable, parametric test, fisher exact test, macro variable, sample size, chi-square test, exact test, proc ttest, nominal variable, qq plot, proc means, proc sql, anova, kruskal-wallis test, temporary dataset, proc freq | source | 10 |
| 165 | Soft Skills to Master as a Freelance Consultant | Kirk Paul Lafler | proc sql, problem solving, proc step, excel | source | 18 | |
| 166 | Sorting a Bajillion Variables: When SORTC and SORTN Subroutines Have Stopped Satisfying, User-Defined PROC FCMP Subroutines Can Leverage the Hash Object to Reorder Limitless Arrays | Troy Martin Hughes | The SORTC and SORTN built-in SAS subroutines sort character and numeric data, respectively, and are sometimes referred to as "horizontal sorts" because they sort variables rather than observations. That is, all elements within a SORTC or SORTN sort must be maintained in a single observation. A limitation of SORTC and SORTN is their inability to sort more than 800 variables when called inside the FCMP procedure. To overcome this disagreeable, arbitrary threshold, user-defined subroutines can be engineered that leverage the h ash object to sort limitless variables. The hash object orders values that are ingested into it using the ORDERED argument, which can specify either ASCENDING or DESCENDING. This text demonstrates three failure patterns that occur when the OF operator specifies an array inside the FCMP procedure, which affect both character and numeric arrays, and which cause all built -in functions and subroutines to fail with runtime errors |
proc fcmp, hash table, character variable, missing value, array, integer, character functions | source | 16 |
| 167 | Sound SDTM, Sound ADaM - Orchestrating SDTM and ADaM Harmonization | Nancy Brucken, David Neubauer, Soumya Rajesh |
Sound SDTM data is integral to having sound ADaM data. The ADaM model says "Whereas ADaM is optimized to support data derivation and analysis, CDISC's Study Data Tabulation Model (SDTM) is optimized to support data tabulation". Often times those who implement SDTM are not implementing ADaM and vice versa and they may not be working in harmony. If how the data will be analyzed has not been considered, tasks such as the definition of treatment arms and elements, and the assignment of collected data to SDTM domains can present various challenges during analysis and with traceability. This paper will cover some of the situations the authors have encountered and discuss how SDTM and ADaM implementation can be better in tune | disease response, best response, adam dataset, sdtm domain, concomitant medications, cdisc | source | 8 |
| 168 | Standardization of Reactogenicity Data into Findings | Charumathy Sreeraman | Reactogenicity event(s) is the key safety assessment of the vaccines TA. It refers to a particular expected or generic reaction following vaccine administration. The term reaction usually implies that the adverse event has a causal relationship with the vaccination, or at least there exists a distinct possibility. Reactogenicity is evaluated by observing a pre -specified set of adverse events over a pre -defined observation period. Standardizing the reactogenicity data in the SDTM datasets implements the 'FLAT MODEL' strategy prescribed by the Vaccines TAUG. This paper will be discussing on stan dardizing the diary data from study subjects into finding domains as applicable rolling it into 'Flat Model' strategy | adverse events, sample, clinical trial, sdtm domain, cdisc | source | 9 |
| 169 | Standardized data handling framework for wearables | Madhu Annamalai, Umayal Annamalai |
Wearable devices have revolutionized the way clinical research is conducted in recent times, thereby bringing technology closer to the patients. While wearables can help continuous and near -real-time monitoring of patient data, analysis and reporting conti nue to be challenging as the quantum of data continues to grow. Problem and solutions framework for handling data from wearables to improve analysis and reporting is continuously changing and challenging. The key takeaways will include data integration strategies, data mapping based on CDISC standards, creating wearable device data -specific analysis models, and the generation of custom reports based on the study requirements | clinical trial, adam dataset, cdisc | source | 4 |
| 170 | Statistical Considerations and Methods for Handling Missing Outcome Data in Clinical Trials During the Era of COVID-19 | Xi Qian, Chengfei Lu |
Missing data occurs when the values of variables of interest are not collected for all subjects at all scheduled visits in clinical trials. It is usually caused by subject dropout and/or loss to follow -up. Although missing data is a common problem for any clinical trials, COVID-19 pandemic has been presenting unprecedented challenges for many aspects of clinical trial studies, particularly for missing data analysis. Statistical methods for dealing with missing data are dependent on the assumptions regarding the data missing mechanisms and patterns. In this paper, we first introduce data missing mechanisms and patterns and summarize commonly used statistical methods for handling missing data | missing value, imputation, clinical trial, proc step, proc mi, sensitivity analysis, proc calis, mean imputation, regression analysis, proc mianalyze, inferential statistics, change from baseline, treatment effect, interval variable, inverse probability weighting, baseline characteristics, multiple imputation, likelihood function, sample, adverse events, data science | source | 11 |
| 171 | Strategies for Code Validation at Statistical Center for HIV/AIDS Research and Prevention (SCHARP) | Marie Vendettuoli, Xuehan (Emily) Zhang, Rodger Zou |
Code validation presents a common challenge to programming teams embedded in the pharmaceutical data sphere. How much effort is enough? Is it possible to have a lightweight and nimble approach to validation that accommodates the variety that statistical programmers see in our daily codebase? At Statistical Center for HIV/AIDS Research and Prevention, Fred Hutchinson Cancer Center (SCHARP) we have adopted the R Validation Framework (PHUSE, 2021) as a language-agnostic paradigm for validating code at various stages of maturity, during both internal development efforts and when adopting community-authored resources. We will share examples from three major use categories: (1) Validation of community-authored resources (2) Validation integrated into development, and (3) Validation separate from R packages. A brief review of the R Validation Framework will be provided | data cleaning, correlation, statistical programmer, likelihood function, spearman correlation, clinical programming, sample | source | 13 |
| 172 | Study Data Technical Rejection Criteria (TRC) Considerations for Multiple Data Packages in a Single Submission | Christine S Teng, Janet Low, Si Ru Tang |
The Study Data Technical Conformance Guide (TCG) provides technical recommendations to sponsors for the submission of animal and human study data and related information in a standardized electronic format in INDs, NDAs, ANDAs, and BLAs. Study datasets and their supporting files should be organized into a specific file directory structure. Submission files within the appropriate folders allows automated systems to detect and validate the presence of expected data and datasets for review and minimizes the need for manual processing. Effective September 15, 2021, the FDA implemented the Study Data Technical Rejection Criteria (TRC) validation which can reject a submission if criteria are not met through the automated inbound process, Electronic Submission Gateway (ESG). This paper will share the challenges and successful approaches that were considered when preparing multiple data packages for submission | final analysis, interim analysis, data cleaning, adam dataset, clinical trial, database lock, data validation rules, cdisc | source | 10 |
| 173 | Submission survival guidelines for Statistical Programmers | Mekhala Acharya, Katlyn Buff, Norihiko Oharu |
With accelerated cycle times whether it is *PRIME, RTOR or SAKIGAKE, submission processes are being redesigned in major markets. As a result, pharmaceutical companies must expedite drug approval through streamlined submission activities. This makes the statistical programming function a key stakeholder who needs to focus on a consistent approach across the portfolio in creating high quality submission deliverables, while maintaining speed and efficiency. This can only be accomplished through proactive planning, education, and awareness of submission requirements at every programming milestone. The intent of this poster is to provide a visualization of the programming life cycle for a successful submission. This will serve as a one-stop shop, consolidating guidelines from multiple sources and simplistically explaining each submission component | database lock, statistical programmer | source | 9 |
| 174 | Survival Methods for Crossover in Oncology Trials | Brian Mosier | In oncology trials, patients are often allowed to switch from one treatment arm to the other when their disease progresses. This crossover of patients, which is typically not at random, may lead to bias in the estimates of overall survival for the study. As such, when patient crossover is allowed, the intent -to-treat population is no longer appropriate for analysis. Various methods have been developed to handle the crossover of patients. This paper presents two SAS macros to implement methods that account for treatment crossover in oncology trials. The methods included are (1) the rank preserving structural failure time (RPSFT) model, and (2) the inverse probability of censoring weighting (IPCW) model | proc phreg, randomization, treatment arm, log-rank test, confidence interval, binary variable, overall survival, rank test, time-dependent covariate, treatment effect, logistic distribution, logistic regression | source | 18 |
| 175 | Taming the Large SAS Dataset by Splitting it Up | David W. Franklin | Large SAS datasets tie up computer resources and are often not useful to have as stand-alone entities. You may also want to split a dataset set up into pieces either for the practicalities of sending a dataset in small chucks to a single recipient, or send subsets of that data to different recipients. This paper will show the development of two macros that are useful in splitting the large dataset, the first being specifying the number of observations that should go into an output dataset, and the second being split by values | macro variable, out= | source | 5 |
| 176 | Ten Rules for Better Charts, Figures and Visuals | Kirk Paul Lafler | The production of charts, figures, and visuals should follow a process of displaying data in the best way possible. However, this process is far from direct or automatic. There are so many ways to represent the same data: histograms, scatter plots, bar charts, and pie charts, to name just a few. Furthermore, the same data, using the same type of plot, may be perceived very differently depending on who is looking at the figure. A more inclusive definition to produce charts, figures, and visuals would be a graphical interface between people and data. This paper highlights the work of Nicolas P. Rougier, Michael Droettboom, and Philip E. Bourne by sharing ten rules to improve the production of charts, figures, and visuals | bar chart, proc sgplot, histogram, proc step, scatter plot, proc sgpanel, excel, vertical bar chart, box plot, bubble plot, proc freq, data science, series plot, class variable, correlation | source | 19 |
| 177 | The Effect of Bethanechol on Tracheobronchomalacia in Infants | Chary Akmyradov | This single-center retrospective cohort study included 33 subjects (11 cases, 22 controls) hospitalized between 2017 and 2022. Cases treated with Bethanechol were matched with controls who did not receive Bethanechol. Case-control matching of 1:2 was performed based on birth gestational age, sex, the severity of Tracheobronchomalacia (TBM), the severity of bronchopulmonary dysplasia, and respiratory support. The primary outcome was the effect of Bethanechol on the mean Pulmonary Severity Score. This presentation demonstrates the statistical methods, visualization, and SAS programming behind this clinical study | standard deviation, conditional logistic regression | source | 6 |
| 178 | The Flexible Ways to Create Graphs for Clinical Trial | Hong (Lily) Zhang, Kaijun Zhang |
Without a doubt, SAS programming can create high-quality graphics. In SAS language, PROC TEMPLATE with SGRENDER procedure is a powerful and commonly used tool to create a customized figure. PROC TEMPLATE allows any figure type to overlay in one plotting space, lattice any figure type side by side, and control every visual aspect of the graphical field . However, the R language's more flexible tools have gained popularity recently by using the ggplot2 package, especially combined with the patchwork package. The combination of patchwork and the ggplot2 package can provide you with a more powerful and effective tool to create all kinds of graphs in clinical trial reports[1,2]. This paper will introduce the R package patchwork combined with the ggplot2 using typical graphs in the clinical trial as examples. This paper will also include PROC TEMPLATE with SGRENDER procedure of SAS, as the comparison |
clinical trial, box plot, proc template, proc step | source | 11 |
| 179 | The implementation of Scrum in Pharmaceutical Data Analytics and Statistical Programming | Jagan Mohan Achi, Eliana D'Angelo |
Scrum is a popular agile project management framework that is useful for addressing complex environments, such as Pharmaceutical Data Analytics and Statistical Programming. By using Scrum, teams can control risk, minimize investments, and improve predictability. Scr um teams focus on value and learning, rather than relying on opinions, which increases the chances of a good outcome and a high return on investment. In comparison to the traditional waterfall approach, which is commonly used in our industry, Scrum allows teams to constantly adjust and adapt to changes, leading to more efficient and effective project delivery. In this paper, we discuss Scrum's usefulness in complex environments and the benefits and learnings that we have experienced while implementing Scrum across our Data Analytics and Statistical Programming team | clinical programming, sample | source | 7 |
| 180 | The Interview Process: An Autistic Perspective | Laura Needleman | Neurodiversity is an emerging topic within our industry. It is a newer diversity category that we as an industry are just beginning to understand and incorporate into Diversity, Equity, and Inclusion ( DE&I) plans. Many companies understand there are many benefits to seeking out and hiring this talent. Topics include discussing various interview techniques and dissecting them from an autistic perspective. This presentation will also offer interviewing ideas that would better help to showcase neurodivergent t alent during the interview process. I'll also be sharing my recommendations around providing interview questions in advance as well as formal skill testing as it relates to neurodivergent candidates | repeated measures, sas programming | source | 7 |
| 181 | The Phantom of the ADaM: adding missing records to BDS datasets | Anastasiia Drach | Missing data is a 'pain' of any study. There are many imputation techniques available, but sometimes all we need to know is just that the data is missing. In these cases, it is useful to add derived records to your ADaM datasets with missing AVAL/AVALC to indicate missed visits or timepoints. Such records are called phantom records. In this paper, we discuss how to add them into BDS ADaM dataset using PRO data as an example. We will start with an overview of different ways to represent missing data in SDTM. The paper will present several types of analysis which require the inclusion of phantom records to account for missing data. It will cover various scenarios of adding such records, from the most straightforward to more complex ones. Finally, we will provide some ready-to-use solutions for the creation of phantom records, which could be easily adjusted to your individual needs | missing value, adam dataset, clinical trial, imputation, baseline, cdisc | source | 10 |
| 182 | Tips and traps on how to efficiently accelerate clinical trials to successful submission, approval, and launch | Michael Nessly | Scenario 1: your review division team has suggested that if your submission can be accelerated by 6 months, then your company will be at the advisory committee meeting along with your competition Scenario 2: The preliminary results from your study have been presented at a clinical meeting and a health authority has contacted you indicating that this study could be the basis of a successful submission as it may provide effective treatment for patients who have none. These represent actual historic situations demanding rapid development and submission. I present here some basic approaches that have been effective in the past in such accelerations. These require up front efforts, maturity, and discipline. As cultural change is involved, these practices are not standard nor even commonly encountered | clinical trial, data cleaning | source | 5 |
| 183 | Tips to Read In and Output Excel Spreadsheets in SAS | Chao Su, Jaime Yan, Changhong Shi |
EXCEL is a powerful and versatile tool widely used in clinical trials to save and transfer data. Especially in organizations where research software like SAS and R is not commonly used, researchers can only manipulate data by relying on IT. Analysis and reporting programmers play an essential role in receiving different types of EXCEL spreadsheets and creating customized EXCEL reports according to requests from different departments. Usable code to read in different types of spreadsheets and generate friendly interface EXCEL outputs is beneficial and meaningful. In this paper, different methods are presented to output EXCEL files to meet different complex requirements. This will significantly relieve the Analysis and reporting (A&R) programmers' work and improve efficiency. It also helps to smooth the collaboration among different functional groups to successfully achieve the common goal |
excel, proc step, libname engine, proc import, sample, proc export, proc sql | source | 5 |
| 184 | Tired of Manual Language Translation? Give it a REST! | Shawn Hopkins, Matthew Ness |
Working in a global environment or partnership settings, we are bound to encounter situations where we need to translate content from one language to another in order to analyze clinical trial data. With respect to SAS data sets with content in a foreign language, the FDA requires a verified English translation, which is often performed by a third party. However, this may take considerable time and could squeeze programming development too much to meet upcoming timelines. With that in mind, is there a way to get a "good-enough" data set translation in place quickly and efficiently, so programming can start development long before the official translation enters the scene? In this paper we'll share an innovative, fully automated approach to informal translation of SAS data sets received in Extended Unix Code simplified Chinese characters (EUC-CN) into UTF-8 encoding in English | character variable, numeric variable, proc http, proc step, macro variable, web service | source | 7 |
| 185 | Top 5 gotchas for getting cloud ready in SAS Viya | Charu Shankar | As a sas9 programmer, you may be interested to learn about the cloud and why SAS Viya is such a big leap forward. In addition to hearing about the big picture and getting cloud savvy, you are also interested in a deeper dive in how things are going to be in SAS Viya vs SAS 9. This paper will discuss the top 5 gotchas for getting cloud ready in SAS : from database connections to data types, exploring metadata, loading tables in memory, new engines. All levels welcome. Some awareness of SAS 9 would be beneficial | sas viya, sas 9, parallel processing | source | 8 |
| 186 | Traceability: Not just about Data | Rohit Nagpal, Kavitha Mullela |
Sometimes in clinical trials we see gaps between data, documentation and process which leads to questions from cross-functional teams and Regulatory Agencies. To overcome these issues, we can consider traceability, not just in the context of data, but as a combination of data, documentation, and process involving all cross functional teams from data collection to submission. Traceability has many facets and is a joint effort that requires collaboration across all cross -functional teams. To accomplish this during the lifecycle of clinical trials, we use different documentation trackers like change control, database migration change, study status, process checklists and delivery approval form besides the standard documents | clinical trial, data lineage, adam dataset, quality control, sdtm domain, raw data, clinical research, cdisc | source | 11 |
| 187 | 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 | 4 |
| 188 | U.S. Conference | Liping Sun | data standard | source | 22 | |
| 189 | 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 contents, proc format, proc sgplot, proc sql, deduplication, group by, service provider, sample, proc report, sas programming, by group processing, dataset, lookup, proc means | source | 24 |
| 190 | Undo SAS Fetters with Getters and Setters: Supplanting Macro Variables with More Flexible, Robust PROC FCMP User-Defined Functions That Perform In-Memory Lookup and Initialization Operations | Troy Martin Hughes | Getters and setters are common in some object-oriented programming (OOP) languages such as C++ and Java, where "getter" functions retrieve values and "setter" functions initialize (or modify) variables. In Java, for example, getters and setters are constructe d as conduits to private classes, and facilitate data encapsulation by restricting variable access. Conversely, the SAS language lacks classes, so SAS global macro variables are typically utilized to maintain and access data across multiple DATA steps and procedures. Unlike an OOP program that can categorize variables across multiple user -defined classes, however, SAS maintains only one global symbol table in which global macro variables can be maintained. Additionally, maintaining and accessing macro variables can be diff icult when quotation marks, ampersands, percentage signs, and other special characters exist in the data | macro variable, proc fcmp, symbol table, global symbol table | source | 8 |
| 191 | Using Bundles for R Package Management | Magnus Mengelbier | The broader use of R within Life Sciences from a niche personal statistical analysis tool to an organization wide environment is making R package management a more complex and comprehensive task. As the organization grows and the types of analysis are ever expanded, a substantial increase in the number of packages is certain to follow. The use of R packages for dependency management, such as packrat and renv, is very applicable for personal R environments or where users are permitted to fully customize their collection of packages, but not necessarily validated GCP environments. The R bundle approach discussed is designed to provide a similar high degree of flexibility for dependency management while staying within the constraints imposed by GCP compliance. Additional benefits, such as significant decrease in complexity and improved overall life cycle of the R environment, are further explored through examples | data cleaning, gcp, r package | source | 8 |
| 192 | Using P21 Checks to Help Your DM Out! | Chad Fewell, Jesse Beck |
There is a lot of work that goes into the cleaning of data. Data Management (DM) issues queries on an ongoing basis and Statistical Programming (SP) escalates data issues they see within their programs. DM will create edit checks within the database to help with cleaning. In addition, they manually review the data on an ongoing basis looking for data that appears incorrect. Statistical Programming works to program raw data into SDTMs checking for data issues along the way. There appears to be a "gap" in data issues that are looked at from a continuous basis to issues that are discovered prior to submission. This is where Pinnacle 21 comes into play (P21). P21 uses SDTMs to help clean the data, but sometimes due to resourcing SDTMs are not completed until close to study termination. This causes a delay in issues that are difficult to find manually by DM but are essential to be cleaned prior to Database Lock (DBL) | raw data, data validation rules | source | 4 |
| 193 | Using R to Automate Clinical Trial Data Quality Review | Melanie Hullings, Emily Murphy, Derek Lawrence, Michelle Cohen, Andrew Burd |
Over the past 20 years, the detection of the majority of clinical research site quality issues has been the remit of Clinical Research Associates (CRAs) performing on-site monitoring. While there have been advances that enable some remote data review of electronic data capture (EDC) systems, there remains a large amount of manual work by CRAs and Clinical Data Managers (CDMs). Deviations from clinical trial requirements, including critical quality issues such as drug noncompliance and patients who were treated but did not meet all eligibility criteria, are logged in order to track and monitor data integrity and patient safety. To more efficiently facilitate detection, our CDM team created a script in R Studio which can surface potential protocol deviations in the EDC dataset. The script produces a list of findings for CRAs to confirm with sites and enter into the deviation database | protocol deviations, clinical trial, clinical research, clinical data, data cleaning, windowing | source | 6 |
| 194 | Using R to Create Population Pharmacokinetic Dataset | Yangwei Yan, Prema Sukumar, Neelima Thanneer |
Population Pharmacokinetic (PopPK) analyses are essential to evaluate the drug safety and efficacy among population subgroups in drug development. One of the key steps in PopPK modeling is to provide a structured input data set for NONMEM and/or other modeling software. While SAS has long been a dominant tool for statistical programming and analysis in the pharmaceutical industry, R has become a trending programming tool and is widely used in multiple areas due to its power and flexibility in supporting statistical analysis and advanced visualization. However, the use of R in PopPK data set preparation has not been discussed much. This paper demonstrates a step-by-step process to generate data sets for PopPK analyses in RStudio using R markdown with example study data. It offers flexibility to use another programming language for programmers and pharmacometricians. We utilized typical R packages including tidyverse, lubridate, etc | sample, imputation, population pk, raw data, missing value, time., data science | source | 10 |
| 195 | Using SQL Dictionaries to Research the Global Symbol Table | Ronald J. Fehd | proc sql, proc step, create table, macro definition, macro variable, &variable, group by, order by, global symbol table, macro facility, symbol table, proc contents, proc format | source | 23 | |
| 196 | Using the R interface in SAS to Call R Functions and Transfer Data | Bruce F. Gilsen | Starting in SAS 9.3, the R interface enables SAS users on Windows and Linux who license SAS/IML software to call R functions and transfer data between SAS and R from within SAS. Potential users include SAS/IML users and other SAS users who can use PROC IML just as a wrapper to transfer data between SAS and R and call R functions. This paper provides a basic introduction and some simple examples. The focus is on SAS users who are not PROC IML users, but who want to take advantage of the R interface |
proc step, proc iml, dataset, sysdate9, missing value, proc options, estimate, variable, date variable, options statement, work library, regression analysis, proc reg | source | 16 |
| 197 | Validating novel maraca plots - R and SAS love story | Nicole Major, Srivathsa Ravikiran, Monika Huhn, Samvel Gasparyan, Martin Karpefors |
Hierarchical composite endpoints (HCE) are complex endpoints combining outcomes of different types and different clinical importance into an ordinal outcome that prioritizes the clinically most important (e.g., most severe) event of a patient. HCE can be analyzed with the win odds, an adaptation of the win ratio to include ties. One of the difficulties in interpreting HCE is the lack of proper tools f or visualizing the treatment effect captured by HCE, given the complex nature of the endpoint. The recently introduced maraca plot solves this issue by providing a comprehensive visualization that clearly shows the treatment effects on the HCE and its components. The maraca package in the R software provides an easy-to-use implementation of maraca plots, building on powerful features provided by the ggplot2 package | data cleaning, create table, proc step, box plot, left join, treatment effect, order by, proc compare, class variable, regulatory submission, data science, proc sql, clinical trial | source | 17 |
| 198 | Visualization for Success: Driving KPIs and Organizational Process Improvements via Portfolio-level Analytics | Philip Johnston, Julie Ann Hood |
Cloud-based data diagnostic platforms enable organizations to build institutional memory and drive process improvements. Platforms that passively aggregate metrics spare teams from having to "wrangle KPIs" and instead visualize the macro-level trends in their data quality, conformance, standards adoption, submission risks, and team activity at a glance. This poster highlights how these data are showcased in P21 Enterprise's built-in Analytics module and suggests actionable steps based on these trends to support inter- and intra-departmental process improvements. It also demonstrates how the various portfolio-level reports, filters, and views now available within the application support organizations in their coordination efforts and the development of best practices | data cleaning, proc freq | source | 5 |
| 199 | Was the load okay? | Lisa Eckler, Lisa Eckler |
After a series of database tables are loaded from multiple data sources and before using the data to feed automated reports and business intelligence tools, we want to know whether the load was complete and correct. This goes beyond confirming that the jobs ran without errors. The more precise concerns are: Were all of the data sources ingested? Were the right number of rows of data added or updated in each table? Were all of the appropriate columns populated? Do the data values make sense? * Are the values of categorical variables different than expected? This paper describes a fully automated SAS process for comparing a new set of data loaded to one or more tables with previous sets to check for reasonableness and completeness and highlight potential problems. It can also be used more generally as a data comparison tool for two or more subsets of similar data | macro variable, proc step, dataset, proc means, proc freq, data cleaning, data profiling, missing value | source | 22 |
| 200 | We Can Work it Out: Dos and Don-ts for Small Biotech and CRO NDA/BLA submission partnership | Charity Quick, Jiaan Illidge |
Many pharmaceutical/biotech companies rely on outsourcing much of their biometric and regulatory work to CROs - even critical NDA or BLA submissions. At the start of 2020, as the reality of a global pandemic was setting in, a small biotech with only 2 statisticians and 5 programmers was able to file their company's first NDA with the support of a small CRO. The NDA was comprised of over 30 studies including 2 pivotal phase IIIs and additional phase III open label studies with back -to-back locks, as well as legacy and remediation work. As the sponsor programming manager and CRO programming lead, the authors of this paper will share our lessons learned from this multi-year successful collaboration that began as a single phase II outsourced study and ended in a big win for both companies | phase iii, phase ii, open label, adam dataset, cdisc | source | 8 |
| 201 | What is Machine Learning, anyway? | Jim Box | Heard lots of talk about Machine Learning and Artificial Intelligence, but not really sure what it all means and how it is different from the statistics you learned once? In this presentation, we'll look at what machine learning is, look at the basics of the approach and give an overview of some of the more popular algorithms and when they might be used | machine learning, proc hpsplit, neural network, logistic regression, regression analysis, confusion matrix, logistic distribution, proc princomp, response variable, supervised learning, sample, artificial intelligence, type ii error, type i error, missing value, association rules, nominal variable, roc curve, training set, proc hpforest, outlier | source | 11 |
| 202 | What PROC SQL can-t handle while Data Step can? | Deming Li | We've all learned and used Base SAS and PROC SQL but might not have time to compare the two. This paper gathers around some facts from the author's experiences and puts together some practical findings to guide beginners and hopefully refresh memories for the advanced SAS programmers |
proc sql, proc step, sas base, data step, macro variable | source | 3 |
| 203 | What-s the F in specialized people? Let-s talk FSP - the models, variations, and what it takes to be successful | Kathy Bradrick, Ershlena McDaniel, Lisa Stetler |
FSP means something different to every client and service provider. Why are there so many variations? What are the pros and cons? Why is everybody talking about it? Let's explore different models and approaches, what makes collaborations successful, and how the roles and expectations can differ from traditional CRO models. We will explore the variety of FSP models, how the roles and responsibilities vary by collaboration, the challenges and expectations, and what it takes to realize success. We strive to shed light on the benefits and detriments to the client company, the FSP provider company, and the workers within these models. Our take home message is that while FSP seems to have so many meanings and so many different configurations, some things remain unchanged despite size and model. Establishing clear communication, expectations, and escalation pathways is key to making the collaboration successful | service provider, clinical research, clinical trial | source | 5 |
| 204 | What-s the story in your subgroup analysis | Lucy Dai | Subgroup analysis is typically pre-defined in study analysis plan or ad hoc requested by study team or regulatory agency to further understand the treatment effect in the subgroups of interest. Through three subgroup analysis examples from real clinical trials this paper will try to give readers a glimpse of the impact they can have over the course of drug development and drug approvability | subgroup analysis, clinical trial, clinical research, primary endpoint | source | 6 |
| 205 | Why FDA Medical Queries (FMQs) for Adverse Events of Special Interest? Implementation and Case Study | Clio Wu | There are many challenges associated with safety analyses and reporting of adverse events in clinical trials, including, but not limited to, study design issue, coding of the AEs, selection of the AEs of special interest (AESIs), inadequate grouping of likely or potential related AEs, events present in d ifferent ways or are reported with different terms, or AEs that are too specific can result in underestimation of an event. To standardize the NDA/BLA safety data review process, the U.S. FDA/CDER has published two documents on 05 September 2022 and collaborated with the Duke-Margolis Center for Health Policy to host a public workshop on 14 September 2022 to introduce the FDA Medical Queries (FMQs) and Standard Safety Tables and Figures Integrated Guide | adverse events, adverse event of special interest, clinical trial, data science, excel | source | 12 |
| 206 | Why Incorporate is Smart Business | Margaret Hung | health insurance | source | 16 | |
| 207 | With a View to Make Your Metadata Function(al): Exploring the FMTINFO() Function | Louise S. Hadden | Many SAS programmers are accustomed to using SAS metadata on SAS data stores and processes to enhance their coding and promote data driven processing. SAS metadata is most frequently accessed through SAS "V" functions, SAS View tables, and dictionary tables. Information can be gained regarding SAS data stores and drilling down, attributes of columns in data files. However, few programmers are aware of SAS's similar resources and capabilities with respect to SAS formats. This quick tip will briefly discuss how SAS metadata may be exploited in general, and will demonstrate how to use the FMTINFO() function specifically | proc step, proc format, dictionary tables, proc catalog, format statement, view, dataset, proc sql | source | 11 |
| 208 | Working with Biomedical Concepts and SDTM Dataset Specializations for Define-XML using SAS Open CST | Lex Jansen | Biomedical Concepts are units of knowledge that relate to real-world entities. Getting Biomedical Concepts off the ground has been a long and challenging journey. There is little to debate that in theory Biomedical Concepts make sense, but implementation has been a great challenge with little to no realization of benefits. Perhaps that is because the scope has been too large and complex, making implementation extremely difficult. For this reason, CDISC has developed a simplified approach and model which includes an abstract conceptual layer that provides semantics as well as a simplified implementation layer of preconfigured Dataset Specializations (CDASH, SDTM, ...) linked to Biomedical Concepts. SDTM Dataset Specializations are ready to use building blocks for Define-XML. This provides immediate benefits to SDTM programmers and opens the door to efficient programming and automation. Biomedical Concepts are now available in CDISC Library via the API | sdtm domain, dataset, proc step, sample, proc http, rest api, cdisc | source | 23 |
| 209 | You can REST easy validating Synthetic Data | Ben Howell, Ben E. Bocchicchio |
Synthetic data is a promising approach to replace or supplement the control arm of a clinical trial. Effective use of synthetic data could theoretically cut costs, time, and patient burden of clinical trials in half. Machine learning models are key to evaluating synthetic data for its equivalence with real data. The necessary machine learning and statistical capabilities for this purpose may not come standard in a clinical data repository (CDR) and statistical computing environment (SCE). In this case, it i s necessary to integrate the CDR and SCE with a platform used to run machine learning models in order to keep sufficient electronic records for the regulatory compliance of clinical trial data during this process. Additionally, CDRs with REST APIs can be accessed remotely using simple HTTP requests from an external system. Curl commands can be called from SAS, R, Python, or the programming language of your choice | rest api, machine learning, clinical trial, macro variable, clinical data, data cleaning, life sciences, sas viya, proc http | source | 9 |
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