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

Expanding Quantitative Medicines’ Reach: Systems and Methods to Enhance R&D Productivity and Decision-Making in the Agentic Era

October 6, 2026

Brian W. Corrigan, Eric Anderson, Andrew Tredennick, Luis Martinez Lomeli, Megan Cala Pane, Tyler Dunlap, Brian Davis, James Rogers, Marc R. Gastonguay
First published 05 September 2026 in Clinical and Translational Science, Volume 19, Issue 9

Despite integration of artificial intelligence (AI) into drug discovery and an expanding global medicines pipeline, new drug approvals have declined, highlighting a paradox in biopharma: More drugs discovered, but less approvals, higher costs, and longer timelines. Reasons for the decreased research and development (R&D) efficiency are multifactorial, in part driven by the complexity of new modalities, difficult targets and indications, and persistence of cognitive biases in clinical decision-making. One proposed solution to address R&D productivity challenges includes the adoption of organization-wide quantitative decision frameworks (QDFs). QDFs have the potential to increase R&D productivity by integrating quantitative assessments of program risk and value, clinical development costs, time, and probability of success into product valuations.

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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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