Daniel G. Polhamus, Ph.D

Group Leader Statistics, Principal Scientist II

Dan joined Metrum in September, 2010 and is an applied statistician with an emphasis on Bayesian biostatistics, clinical trial design, and high performance statistical computing. His dissertation topic was adaptive design for heterogeneous time-to-event data in a Bayesian decision theory framework.

Recent publications by this scientist

Exposure-safety Markov modeling of ocular adverse events in patient populations treated with tisotumab vedotin

October 8, 2025

Dynamic Dosing in Onco

While dose modification occurs in many therapeutic areas, it is a nearly universal feature in oncology.  Flexibility under the protocol is granted to modify dose amounts, frequency, or even pause dosing altogether with the intent of allowing a patient to remain on therapy, particularly when alternatives are scarce. These deviances of actual versus nominal dosing present unique challenges to analyses.  When a patient progresses, was it because the drug was ineffective or because dose intensity fell short?  Can we leverage exposure response and toxicity mitigation strategies employed by the clinicians to recommend best practices to future clinicians and patients?

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Cost Effectiveness of Individualized Dosing for Hypothetical New Drug in Atopic Dermatitis: A Pharmacometric-Pharmacoeconomic Simulation Study

May 27, 2025

A companion poster to the ASCPT print copy, this visual presentation emphasizes the simulation approach used to evaluate individualized dosing strategies and their economic implications. Results underscore the importance of reducing discontinuation rates to enhance value.

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Pharmacometric-Pharmacoeconomic Modeling and Simulation to Assess Target Product Profile Characteristics in Early Drug Development: Application to Atopic Dermatitis

May 27, 2025

This poster presents a modeling framework linking target product profile characteristics to cost-effectiveness outcomes in early drug development for atopic dermatitis. It highlights how persistence of therapy, more than efficacy metrics, may improve cost utility of new treatments.

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