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). Pbt 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.

中文翻译：

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贝叶斯生物标志物驱动的结果自适应随机化与不完善的生物标志物检测

目标我们调查生物标志物测定的准确性对贝叶斯生物标志物驱动的结果自适应随机化设计的操作特征的影响。方法 在模拟研究中，我们假设试验有两种治疗方法、两种基于生物标志物的分层和二元临床结果（反应）。Pbt 表示生物标志物层 (b = 0 或 1) 中治疗 t (t = 0 或 1) 的响应概率。考虑了真实潜在反应概率方面的四种不同情景：无效（P00 = P01 = 0.25，P10 = P11 = 0.25）和一致（P00 = P10 = 0.25，P01 = 0.5）治疗效果情景，以及定量(P00 = P01 = P10 = 0.25, P11 = 0.5) 和定性 (P00 = P11 = 0.5, P01 = P10 = 0.25) 分层治疗相互作用。对于每个场景，我们将完美的情况与不完美的生物标志物检测的情况进行比较，灵敏度和特异性分别为 0.8 和 0.7。此外，研究了生物标志物阳性患病率值 P(B = 1) = 0.2 和 0.5。结果 结果表明，使用不完善的检测会影响基于贝叶斯生物标志物的结果自适应随机化设计的操作特征。特别是，错误分类导致功率大幅降低，同时 I 类错误概率显着增加。这些影响的程度取决于检测的灵敏度和特异性，以及生物标志物在患者群体中的分布。结论 由于生物标志物检测不完善，