Matthew Wiens, M.A.

Senior Scientist II

Matthew joined Metrum in 2019 as a Research Scientist. He holds an M.A. in Statistics from Boston University. Prior to Metrum, he worked for a variety of startup technology companies where he applied Bayesian methodologies in predictive models based on remote sensing data. His ongoing interests include communicating and leveraging uncertainty from a Bayesian perspective in scalable modeling and simulation projects.

Recent publications by this scientist

Bayesian estimation in NONMEM

December 11, 2023

In this tutorial, the principles of Bayesian model development, assessment and prior selection are outlined. An example pharmacokinetic model is used to demonstrate the implementation of Bayesian modeling using the nonlinear mixed-effects modeling software NONMEM.

Read Article

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.

Read Article

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.

Download PDF