Bayesian biomarker-driven outcome-adaptive randomization with an imperfect biomarker assay

Abstract

Objective:

We investigate the impact of biomarker assay’s accuracy on the operating characteristics of a Bayesian biomarker-driven outcome-adaptive randomization design.

Methods:

In a simulation study, we assume a trial with two treatments, two biomarker-based strata, and a binary clinical outcome (response). P _bt denotes the probability of response for treatment t (t = 0 or 1) in biomarker stratum (b = 0 or 1). Four different scenarios in terms of true underlying response probabilities are considered: a null (P₀₀ = P₀₁ = 0.25, P₁₀ = P₁₁= 0.25) and consistent (P₀₀ = P₁₀ = 0.25, P₀₁ = 0.5) treatment effect scenario, as well as a quantitative (P₀₀ = P₀₁ = P₁₀ = 0.25, P₁₁ = 0.5) and a qualitative (P₀₀ = P₁₁ = 0.5, P₀₁ = P₁₀ = 0.25) stratum-treatment interaction. For each scenario, we compare the case of a perfect with the case of an imperfect biomarker assay with sensitivity and specificity of 0.8 and 0.7, respectively. In addition, biomarker-positive prevalence values P(B = 1) = 0.2 and 0.5 are investigated.

Results:

Results show that the use of an imperfect assay affects the operational characteristics of the Bayesian biomarker-based outcome-adaptive randomization design. In particular, the misclassification causes a substantial reduction in power accompanied by a considerable increase in the type-I error probability. The magnitude of these effects depends on the sensitivity and specificity of the assay, as well as on the distribution of the biomarker in the patient population.

Conclusion:

With an imperfect biomarker assay, the decision to apply a biomarker-based outcome-adaptive randomization design may require careful reflection.