Premium
Designing precision medicine trials to yield a greater population impact
Author(s) -
Zhao YingQi,
LeBlanc Michael L.
Publication year - 2020
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13161
Subject(s) - clinical trial , precision medicine , clinical study design , population , medicine , research design , construct (python library) , computer science , medical physics , intensive care medicine , statistics , mathematics , pathology , environmental health , programming language
Traditionally, a clinical trial is conducted comparing treatment to standard care for all patients. However, it could be inefficient given patients’ heterogeneous responses to treatments, and rapid advances in the molecular understanding of diseases have made biomarker‐based clinical trials increasingly popular. We propose a new targeted clinical trial design, termed as Max‐Impact design, which selects the appropriate subpopulation for a clinical trial and aims to optimize population impact once the trial is completed. The proposed design not only gains insights on the patients who would be included in the trial but also considers the benefit to the excluded patients. We develop novel algorithms to construct enrollment rules for optimizing population impact, which are fairly general and can be applied to various types of outcomes. Simulation studies and a data example from the SWOG Cancer Research Network demonstrate the competitive performance of our proposed method compared to traditional untargeted and targeted designs.