In the era of precision medicine, more and more clinical trials are now driven or guided by biomarkers, which are patient characteristics objectively measured and evaluated as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions. With the overarching objective to optimize and personalize disease management, biomarker-guided clinical trials increase the efficiency by appropriately utilizing prognostic or predictive biomarkers in the design. However, the efficiency gain is often not quantitatively compared to the traditional all-comers design, in which a faster enrollment rate is expected (e.g. due to no restriction to biomarker positive patients) potentially leading to a shorter duration. To accurately predict biomarker-guided trial duration, we propose a general framework using mixture distributions accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identitied. Re-assessments of two publicly available trials are conducted to empirically validate the prediction accuracy and to demonstrate the practical utility. The R package \emph{detest} is developed to implement the proposed method and is publicly available on CRAN.

翻译：在精准医疗时代，越来越多的临床试验由生物标志物驱动或引导。生物标志物作为客观测量和评估的患者特征，可作为正常生物学过程、致病过程或治疗干预药理学反应的指标。以优化和个性化疾病管理为总体目标，生物标志物引导的临床试验通过在设计过程中合理利用预后性或预测性生物标志物来提高效率。然而，这种效率提升往往未与传统全人群设计进行定量比较——传统设计因不限制生物标志物阳性患者而预期具有更快的入组率，可能导致更短的试验时长。为准确预测生物标志物引导的试验时长，我们提出一个采用混合分布处理异质性人群的通用框架。通过大量模拟实验评估异质性人群以及生物标志物特征和疾病动态变化对试验时长的影响，识别出中位生存时间、入组率、生物标志物流行率和效应量等关键参数。基于两项公开试验的重分析验证了预测准确性并展示了实际应用价值。我们开发了R语言包\emph{detest}来实现该方法，该包已在CRAN平台公开发布。