Our Section Chairs are still reviewing paper submissions, and a complete list of accepted papers and e-Posters will be announced soon. In the meantime, here is a preview of some of the papers we expect to feature at PharmaSUG 2020!

Advanced Programming

Python-izing the SAS Programmer 2: Objects, Data Processing, and XML
Mike Molter, PRA Health Sciences

As a long-time SAS programmer curious about what other languages have to offer, I cannot deny that the leap from SAS to the object-oriented world is not a small one to be taken lightly. Anyone looking for superficial differences in syntax and keywords will soon see that something more fundamental is at play. Have no fear though, for languages such as Python have plenty of similarities to give the SAS programmer a strong base of knowledge from which to start their education. In this sequel to an earlier paper I wrote, we will explore Python approaches to programming tasks common in our industry, taking every opportunity to expose their similarities to SAS approaches. After an introduction to objects, we’ll see the many ways that Python can manipulate data, all of which will look familiar to SAS programmers. With a solid working knowledge of objects, we’ll then see how easy object-oriented programming makes the generation of common industry XML. This paper is intended for SAS programmers of all levels with a curiosity about, and an open mind to something slightly beyond our everyday world.


Applications Development

Using Data-Driven Python to Automate and Monitor SAS Jobs
Julie Stofel, Fred Hutchinson Cancer Research Center

This paper describes how to integrate Python and SAS to run, evaluate, and report on multiple SAS programs with a single Python script. It discusses running SAS programs in multiple environments (latin-1 or UTF-8 encoding; command-line or cron submission) and ways to avoid potential Python version issues. A handy SAS-to-Python function guide is provided to help SAS programmers new to Python find the appropriate Python method for a variety of common tasks. Methods to find and search files, run SAS code, read SAS data sets and formats, return program status, and send formatted emails are demonstrated in Step-by-Step instructions. The full Python script is provided in the Appendix.


Artificial Intelligence (Machine Learning)

Pattern Detection for Monitoring Adverse Events in Clinical Trials - Using Real Time, Real World Data
Surabhi Dutta, EG Life Sciences

Patient care involves data capture from disparate sources of care delivery. This includes clinical trials data, sensor data from wearable sensors, hand held devices and Electronic Health Records. We are accustomed to devices that generate health indicator data in large volumes and rapid rate. This paper discusses the benefits, challenges, methods of utilizing this real-time data for pattern detection using machine learning algorithms. This will be done using real world data that has been standardized and integrated during clinical trials. In our previous years paper we had discussed about "Merging Sensor Data in Clinical Trials” . The paper dealt with standardizing clinical trials data and making it ready to use for analysis. This year we will delve deeper in to using the sensors data for Pattern Recognition and AE Monitoring by classifying and segregating specific group of high risk patients, participating in clinical trials, right from first subject first data point. This kind of pattern detection will also be used in Patient Profiling and predicting risk factors for high risk patients in trials. Challenges with current method of Patient Monitoring in Clinical Trials: Any drug related AE’s are usually documented at the end of the episode. This poses significant time and monetary risks for sponsors for ensuring patient safety, achieving drug efficacy and conducting the trials in a timely manner. This paper would explore Patient Profiling using Machine Learning techniques like Clustering and PCA to segregate high risk patients for close monitoring and using predictive analytics for visit based monitoring.


Data Visualization and Reporting

r2rtf – an R Package to Produce Rich Text Format Tables and Figures
Siruo Wang, Keaven Anderson and Yilong Zhang, Johns Hopkins Bloomberg School of Public Health

In drug discovery, research and development, the use of open-source R is evolving for study design, data analysis, visualization, and report generation across many fields. The ability to produce customized rich text format (RTF) tables in the R platform becomes crucial to complement analyses. We developed an R package, r2rtf, that standardizes the approach to generate highly customized RTF tables, listings, and figures (TLFs) in RTF format. The r2rtf R package provides flexibility to customize table appearance for table title, subtitle, column header, footnote, and data source. The table size, border type, color, and line width can be adjusted in detail as well as column width, and row height, text format, font size, text color, and alignment, etc. The control of the format can be row or column vectorized by leveraging the vectorization in R. Furthermore, r2rtf provided pagination, section grouping, multiple tables concatenations for complicated table layouts. In this paper, we give an overview of the r2rtf workflow with three required and four optional easy-to-use functions. Code examples are provided to create customized RTF tables with highlighted features in drug development.


Leadership Skills

One Boys’ Dream: Hitting a Home Run in the Bottom of the Ninth Inning
Carey Smoak, S-Cubed

One boys’ dream of hitting a homerun in the bottom of the ninth inning has been realized in my career. My career started out as an epidemiologist in academia. My SAS® skills were pretty basic back then. My SAS skills advanced tremendously as I transitioned to working as a statistical SAS programmer in the pharmaceutical and medical device industries. My career has been varied from strictly working as a statistical SAS programmer to managing statistical SAS programmer. My interest in statistics began with my interest in baseball. Little did I realize that my interest in statistics as a teenager would lead to a fulfilling career and, thus, fulfill my childhood dream.


Medical Devices

Successful US Submission of Medical Device Clinical Trial using CDISC
Phil Hall, Edwards Lifesciences

It is not yet mandatory for medical device trial data to be submitted using CDISC but The Center for Devices and Radiological Health (CDRH) accepts clinical trial data in any format, including CDISC. This paper serves as a case-study of the successful FDA submission of the Edwards Lifesciences’ PARTNER 3 trial which utilized SDTMS and ADaMs. There will be a review of the SDTM domains used for medical device-specific data and a general discussion of the submission approach.


Software Demonstrations (Tutorials)

Centralized Management and Resolution of Data Validation Issues
Amy Garrett, Pinnacle 21

Pinnacle 21 Validator identifies problems in data; however, diagnostics, assessment and resolution of reported validation issues may feel like a complicated, never-ending process. In this demonstration, we will review common challenges in managing data validation issues and how to handle them effectively. We will also show you how to identify the source of validation issues, and how to classify them to understand when to fix or when to explain. We will also discuss cross-team collaboration, ways to improve your process, and habits that lead to faster issue resolution.


Statistics and Analytics

A Doctor's Dilemma: How Propensity Scores Can Help Control For Selection Bias in Medical Education
Deanna Schreiber-Gregory, Henry M Jackson Foundation for the Advancement of Military Medicine

An important strength of observational studies is the ability to estimate a key behavior or treatment’s effect on a specific health outcome. This is a crucial strength as most health outcomes research studies are unable to use experimental designs due to ethical and other constraints. Keeping this in mind, one drawback of observational studies (that experimental studies naturally control for) is that they lack the ability to randomize their participants into treatment groups. This can result in the unwanted inclusion of a selection bias. One way to adjust for a selection bias is through the utilization of a propensity score analysis. In this paper we explore an example of how to utilize these types of analyses. In order to demonstrate this technique, we will seek to explore whether clerkship order has an effect on NBME and USMLE exam scores for 3rd year military medical students. In order to conduct this analysis, a selection bias was identified and adjustment was sought through three common forms of propensity scoring: stratification, matching, and regression adjustment. Each form is separately conducted, reviewed, and assessed as to its effectiveness in improving the model. Data for this study was gathered between the years of 2014 and 2019 from students attending USUHS. This presentation is designed for any level of statistician, SAS® programmer, or data scientist/analyst with an interest in controlling for selection bias.


Submission Standards

Updates in SDTM IG V3.3: What Belongs Where – Practical Examples
Lucas Du, William Paget, Lingyun Chen and Todd Case, Vertex Pharmaceuticals Inc.

CDSIC SDTM Implementation Guide (IG) Version 3.3 was released on 11/20/2018. New domains and implementation rules have been added to standardize SDTM implementation within the industry. Compared to version 3.2, a lot of information was updated during the 5 years between releases. It also brings a great challenge for people working in Pharma/Biotech to figure out all the details. For example, what are the new domains and how should we use the new domains? Furthermore, the same information may map to different domains due to the purpose- how should we decide which domain the information should go to? Also, In the Trial Summary (TS), Comments (CO), Trial Inclusion/Exclusion Criteria (TI) and other general observational class, variables with any context with more than 200 characters will be mapped to the corresponding SUPP domain. But the label for variables in SUPP domain varies within the industry. In addition, it’s not clear how to populate the EPOCH variables in events, findings and interventions domains or how to deal with subjects in DM domain who are randomized but never dosed. In this paper, updates will be highlighted, and examples will be provided.