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

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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Formalizing “Similarity” in Pediatric Extrapolation Plans using Causal Selection Diagrams

June 20, 2024

Presented at the 37th New England Statistics Symposium (NESS).Dr. Jim Rogers from MetrumRG as he illustrates the use of causal inference frameworks for evaluating pediatric extrapolation plans.

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Covariate modeling in pharmacometrics: General points for consideration

April 15, 2024

Covariate analysis is typically performed, and results are reported, based on the purpose of the analysis and the anticipated audience. Failure to use a standard for reporting parameters has made it difficult to find common features of models and use covariate analysis in a consistent way in order to discover new knowledge. This article aims to provide a useful basis for analysts to choose the most appropriate approach for their specific sets of circumstances.

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