Auxiliary variable–enriched biomarker‐stratified design

Abstract

Summary Clinical trials in the era of precision medicine require assessment of biomarkers to identify appropriate subgroups of patients for targeted therapy. In a biomarker-stratified design (BSD), biomarkers are measured on all patients and used as stratification variables. However, such a trial can be both inefficient and costly, especially when the prevalence of the subgroup of primary interest is low and the cost of assessing the biomarkers is high. Efficiency can be improved and costs reduced by using enriched biomarker-stratified designs, in which patients of primary interest, typically the biomarker-positive patients, are oversampled. We consider a special type of enrichment design, an auxiliary-variable-enriched design (AEBSD), in which enrichment is based on some inexpensive auxiliary variable that is positively correlated with the true biomarker. The proposed AEBSD reduces the total cost of the trial compared to a standard BSD when the prevalence rate of true biomarker positivity is small and the positive predictive value (P P V) of the auxiliary biomarker is larger than the prevalence rate. In addition, for an AEBSD, we can immediately randomize the patients selected in the screening process without waiting for the result of the true biomarker test, reducing the treatment waiting time. We propose an adaptive Bayesian method to adjust the assumed P P V while the trial is ongoing. Numerical studies and an example illustrate the approach. An R package is available.

Simulation study for evaluating an adaptive-randomisation Bayesian hybrid trial design with enrichment

“…The enrichment strategy described here achieves findings previously reported by other biomarker adaptive designs [ [17] , [44] , [45] ]. For example, the proposed enriched biomarker stratified design and the auxiliary-variable- enriched biomarker stratified design also showed that applying an enrichment strategy can result in a more cost efficient design in terms of power and reduce the average sample size [ 46 , 47 ]…”