James A. Rogers, Ph.D.

Vice President of Statistics, Quantitative Sciences

Jim Rogers is the Vice President of Statistics in the Quantitative Sciences Business Unit at Metrum Research Group. After receiving his doctorate in statistics from The Ohio State University in 2001, Jim worked for two years on genomic and metabonomic analyses for biotechnology companies, followed by five years at Pfizer Global Research and Development where he worked initially and as a nonclinical statistician and later as a clinical biostatistician. In 2008, Jim joined Metrum Research Group in order to work in closer collaboration with quantitative biologists and pharmacometricians. Over the course of his 15 years at MetrumRG, Jim has worked on decision informatics across a wide range of therapeutic areas and therapeutic modalities. Recurring areas of focus have included problems related to dose selection and dose optimization, as well as platform development based on disease progression models and clinical trial simulation. From a methodological perspective, Jim’s focus in recent years has centered on the role of causal inference concepts in evidence integration. Jim believes that scientists trained in statistics can revolutionize the discipline of pharmacometrics and that scientists trained in pharmacometrics can revolutionize the discipline of statistics.

Recent publications by this scientist

Accounting For Dose Modifications In Exposure-Response Analyses In Oncology: The Case Example Of Brigimadlin.

December 6, 2024

Presented at ACoP 2024. A Bayesian model of the probability of dose modification as a function of platelet and neutrophil counts was developed to characterize the dynamic and probabilistic nature of dose decisions. The dose modification model was successfully integrated into a dynamic simulation framework accounting for the impact of safety on dose.

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Bayesian sparse regression for exposure–response analyses of efficacy and safety endpoints to justify the clinical dose of valemetostat for adult T-cell leukemia/lymphoma

October 2, 2024

The developed models characterized E–R relationships and covariate effects for efficacy and safety endpoints. The efficacious and safe exposure range was established and supported the clinical dose of 200 mg. The utility of logistic regressions in a Bayesian framework with spike and slab priors, in which all the covariate effects were included and simultaneously estimated, was demonstrated.

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Evaluating Conditional Exchangeability Assumptions for Bayesian Borrowing, With Application to Pediatric Extrapolation

June 20, 2024

Presented at the Graybill Conference 2024. Dr. Jim Rogers from MetrumRG as he demonstrates the application of causal selection diagrams as a statistical consulting tool for Bayesian prior elicitation. Bayesian dynamic borrowing is an increasingly important methodology in pediatric extrapolation, and its successful application requires quantitative scientists who can bridge the gap between qualitative notions of “similarity” and formal statistical notions of “exchangeability.”

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