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Designing a study to evaluate the benefit of a biomarker for selecting patient treatment
Author(s) -
Janes Holly,
Brown Marshall D.,
Pepe Margaret S.
Publication year - 2015
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6564
Subject(s) - context (archaeology) , medicine , sample size determination , clinical trial , surrogate endpoint , biomarker , randomized controlled trial , breast cancer , selection (genetic algorithm) , selection bias , oncology , computer science , cancer , statistics , machine learning , pathology , mathematics , paleontology , biochemistry , chemistry , biology
Biomarkers that predict the efficacy of treatment can potentially improve clinical outcomes and decrease medical costs by allowing treatment to be provided only to those most likely to benefit. We consider the design of a randomized clinical trial in which one objective is to evaluate a treatment selection marker. The marker may be measured prospectively or retrospectively using samples collected at baseline. We describe and contrast criteria around which the trial can be designed. An existing approach focuses on determining if there is a statistical interaction between the marker and treatment. We propose three alternative approaches based on estimating clinically relevant measures of improvement in outcomes with use of the marker. Importantly, our approaches accommodate the common scenario in which the marker‐based rule for recommending treatment is developed with data from the trial. Sample sizes are calculated for powering a trial to assess these criteria in the context of adjuvant chemotherapy for the treatment of estrogen‐receptor‐positive, node‐positive breast cancer. In this example, we find that larger sample sizes are generally required for assessing clinical impact than for simply evaluating if there is a statistical interaction between marker and treatment. We also find that retrospectively selecting a case‐control subset of subjects for marker evaluation can lead to large efficiency gains, especially if cases and controls are matched on treatment assignment. Copyright © 2015 John Wiley & Sons, Ltd.