Leveraging Innovative Methods and Tools for Interactive Quantitative Safety and Benefit-Risk Assessment

Melvin Munsaka Chair
Saurabh Mukhopadhyay Discussant
Melvin Munsaka Organizer
Monday, Aug 7: 2:00 PM - 3:50 PM
Topic-Contributed Paper Session 
Metro Toronto Convention Centre 
Room: CC-717B 



Main Sponsor

Section for Statistical Programmers and Analysts

Co Sponsors

Biopharmaceutical Section
International Indian Statistical Association


Advanced Visual Analytics for Drug Safety Review

Drug safety data present many challenges with regard to curation, analysis, interpretation, and reporting. Visual analytics presents an alternative to the traditional tabular outputs for exploring, assessing, and reporting safety data and presents an opportunity to enhance and facilitate the evaluation of drug safety and help convey multiple pieces of information concisely and more effectively than tables. Graphical depictions of safety data can help facilitate better communication of drug safety findings by blending data visualization, statistical, and data mining techniques to create visualization modalities that help users make sense of safety data with an emphasis on how to complement computation and visualization to perform effective and meaningful analyses. Developing readily available tools for visual analytics of drug safety data that take into account considerations revolving around structured assessment driven by safety questions of interest is desirable along with considerations for user interface parlor. This discussion will highlight a tool from a joint collaboration of the ASA, PHUSE, and FDA. 


Neetu Sangari

A New Perspective on Visualization of Laboratory Data using elaborator

In clinical studies there are huge numbers of laboratory parameters available that are measured at several visits for several treatment groups. The status quo for presenting laboratory data in clinical trials consists in generating large numbers of tables and data listings. Such tables and listings are required for submissions to health authorities. However, reviewing laboratory data presented in the form of tables and listings is a lengthy and tedious process. To enable efficient exploration of laboratory data we developed elaborator, a comprehensive and easy-to-use interactive browser-based application. The elaborator app comprises three analysis types for addressing different questions, for example about changes in laboratory values that frequently occur, treatment-related changes and changes beyond the normal ranges. This discussion will focus on the elaborator app that can be used to identify safety signals in a clinical trial as well as generating hypotheses that are further inspected with detailed analyses and possibly data from other sources.  


Madhurima Majumder, Bayer


Erya Huang, Bayer HealthCare

Quantitative Benefit-Risk Assessments: Demonstration of Innovative Methods and Tools through a Case Study

This talk will present a case study of how quantitative benefit-risk analysis can be used to inform drug approval decisions along with a description of methods and software that can be used. The focus will be on multiple criteria decision analysis (MCDA) and stochastic multi-criteria acceptability analysis (SMAA), two methods to quantify the aggregate performance of different alternatives across multiple benefits and risk endpoints, explicitly incorporating the relative importance of these endpoints. Starting with a description of the process of selecting endpoints and eliciting preferences the talk will provide an overview of how to conduct and present quantitative benefit risk analyses including examples of visual representations that can be used to summarize the results. Along the way, methods to incorporate prior data using mixture priors will also be discussed. The talk will end with a discussion of how the findings can be interpreted and briefly introduce an R Shiny tool that has been developed. 


Sai Dharmarajan, FDA

Bayesian Benefit Risk Analysis Using the brisk R Package

Quantitative methods for benefit-risk analysis help to condense complex decisions into a univariate metric to describe the overall benefit relative to risk. One such approach is the multi-criteria decision analysis (MCDA) framework. Bayesian benefit-risk analysis incorporates uncertainty through posterior distributions which are inputs to the benefit-risk framework. This talk will focus on the brisk R package (available on CRAN) which provides functions to assist with Bayesian benefit-risk analyses, such as MCDA. The package requires users to input posterior samples, utility functions, weights, and the package outputs posteriors of benefit-risk scores. The software and methods will be illustrated with several examples. 


Richard Payne


Richard Payne