On Enrichment Strategies for Biomarker Stratified Clinical Trials.

TitleOn Enrichment Strategies for Biomarker Stratified Clinical Trials.
Publication TypeJournal Article
Year of Publication2018
AuthorsWang, Xiaofei, Jingzhu Zhou, Ting Wang, and Stephen L. George
JournalJ Biopharm Stat
Date Published2018
KeywordsBiomarkers, Tumor, Breast Neoplasms, Computer Simulation, Endpoint Determination, Female, Humans, Lung Neoplasms, Patient Selection, Precision Medicine, Randomized Controlled Trials as Topic, Research Design, Sample Size, Treatment Outcome

In the era of precision medicine, drugs are increasingly developed to target subgroups of patients with certain biomarkers. In large all-comer trials using a biomarker stratified design, the cost of treating and following patients for clinical outcomes may be prohibitive. With a fixed number of randomized patients, the efficiency of testing certain treatments parameters, including the treatment effect among biomarker-positive patients and the interaction between treatment and biomarker, can be improved by increasing the proportion of biomarker positives on study, especially when the prevalence rate of biomarker positives is low in the underlying patient population. When the cost of assessing the true biomarker is prohibitive, one can further improve the study efficiency by oversampling biomarker positives with a cheaper auxiliary variable or a surrogate biomarker that correlates with the true biomarker. To improve efficiency and reduce cost, we can adopt an enrichment strategy for both scenarios by concentrating on testing and treating patient subgroups that contain more information about specific treatment parameters of primary interest to the investigators. In the first scenario, an enriched biomarker stratified design enriches the cohort of randomized patients by directly oversampling the relevant patients with the true biomarker, while in the second scenario, an auxiliary-variable-enriched biomarker stratified design enriches the randomized cohort based on an inexpensive auxiliary variable, thereby avoiding testing the true biomarker on all screened patients and reducing treatment waiting time. For both designs, we discuss how to choose the optimal enrichment proportion when testing a single hypothesis or two hypotheses simultaneously. At a requisite power, we compare the two new designs with the BSD design in terms of the number of randomized patients and the cost of trial under scenarios mimicking real biomarker stratified trials. The new designs are illustrated with hypothetical examples for designing biomarker-driven cancer trials.

Alternate JournalJ Biopharm Stat
Original PublicationOn enrichment strategies for biomarker stratified clinical trials.
PubMed ID28933670
PubMed Central IDPMC5842146
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
R21 AG042894 / AG / NIA NIH HHS / United States