Ji Lingyun, McShane Lisa M, Krailo Mark, Sposto Richard
Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
Biometric Research Program, Division of Cancer Treatment and Diagnosis, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA.
Clin Trials. 2019 Dec;16(6):599-609. doi: 10.1177/1740774519875969. Epub 2019 Oct 3.
BACKGROUND/AIMS: Biomarker-stratified outcome-adaptive randomization trials, in which randomization probabilities depend on both biomarker value and outcomes of previously treated patients, are receiving increased attention in oncology research. Data from these trials can also form the basis of investigation of additional biomarkers that may not have been incorporated into the original trial design. In this article, we investigate the validity of a standard analytical method that utilizes data from a biomarker-stratified outcome-adaptive randomization trial to assess the effect of a newly identified biomarker on patient outcomes.
In the context of an ancillary biomarker study for a two-arm phase II trial with a response endpoint, we conduct analytic and simulation studies to investigate bias in estimated biomarker effects under outcome-adaptive randomization. Conditions under which bias arises and magnitude of the bias are examined in several settings. We then propose unbiased estimators of biomarker effects with appropriate variance estimators.
We demonstrate that use of biomarker-stratified outcome-adaptive randomization perturbs the patient population and treatment assignments. Consequently, application of standard analysis methods to data from an outcome-adaptive randomization trial either to estimate prognostic effect of a new biomarker in uniformly treated patients or to estimate effect of treatment in relation to the new biomarker can lead to substantially biased estimates. The proposed adjusted estimators are asymptotically unbiased, and the proposed variance estimators correctly reflect the sample variability in the estimators.
This article demonstrates existence of bias when standard, naïve statistical methods are utilized to assess biomarker effects using data from a biomarker-stratified outcome-adaptive randomization trial, and hence that results from naïve analyses must be interpreted with great caution. These findings highlight that, in an era where data and specimens are increasingly being shared for biomarker studies, care must be taken to document and understand implications of the study design under which specimens or data have been obtained.
背景/目的：生物标志物分层的结局适应性随机试验中，随机化概率取决于生物标志物值和先前治疗患者的结局，这类试验在肿瘤学研究中受到越来越多的关注。这些试验的数据也可作为研究其他可能未纳入原始试验设计的生物标志物的基础。在本文中，我们研究了一种标准分析方法的有效性，该方法利用生物标志物分层的结局适应性随机试验的数据来评估新发现的生物标志物对患者结局的影响。
在一项针对双臂II期试验（有反应终点）的辅助生物标志物研究背景下，我们进行分析和模拟研究，以调查结局适应性随机化下估计的生物标志物效应中的偏差。在几种情况下检查偏差出现的条件和偏差的大小。然后我们提出了具有适当方差估计量的生物标志物效应的无偏估计量。
我们证明，使用生物标志物分层的结局适应性随机化会扰乱患者群体和治疗分配。因此，将标准分析方法应用于结局适应性随机试验的数据，无论是估计新生物标志物在统一治疗患者中的预后效应，还是估计与新生物标志物相关的治疗效应，都可能导致严重有偏的估计。所提出的调整估计量是渐近无偏的，并且所提出的方差估计量正确反映了估计量中的样本变异性。
本文证明，当使用标准的、简单的统计方法利用生物标志物分层的结局适应性随机试验的数据来评估生物标志物效应时，会存在偏差，因此必须极其谨慎地解释简单分析的结果。这些发现强调，在一个越来越多地共享数据和标本用于生物标志物研究的时代，必须注意记录和理解获取标本或数据的研究设计的影响。
