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

Confounded exposure metrics

November 28, 2023

Exposure-response (E-R) modeling frequently relies on the use of exposure metrics that summarize drug concentrations over time. This research presents simulations to demonstrate that certain commonly used exposure metrics, including average concentration up to an event time, are likely to lead to causal confounding under the very conditions that motivate their use.

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Expanding Statistical Influence in Pharmacometrics: What it Means, Why it Matters, and How to Make it Work

November 27, 2023

Many biostatisticians in pharma and biotech  have relatively little awareness of, let alone influence over, the heavily statistical “pharmacometric” reasoning that goes into CTD Section 2.7.2 of an FDA regulatory submission, the “Summary of Clinical Pharmacology Studies”.  This talk focuses on the value of increasing statistical influence in pharmacometrics and the path to get there.

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Bayesian Borrowing in the DINAMO Pediatric Study using Informative Priors Derived from Model-based Extrapolation

November 15, 2023

Presented at ACoP14. The DINAMO study assessed empagliflozin and linagliptin’s effectiveness and safety in treating type 2 diabetes in children. Using prior models based on adult data, the study predicted these medications’ effects in younger populations following FDA guidelines. Employing a Bayesian analysis with robust prior specifications, the study justified its approach by considering factors like weight, kidney function, age, race, and gender when extrapolating adult outcomes to pediatric populations.

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