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.
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.
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.
November 14, 2023
Presented at ACoP14. Deep compartment models (DCMs) are a proposed alternative to traditional nonlinear mixed effect (NLME) pharmacometrics approaches [1]. DCM uses neural networks to represent estimated pharmacokinetic parameters which can then be used in either closed-form or ordinary differential equation (ODE)-based representations of pharmacokinetic models