A semiparametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial
A semiparametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial
复制标题
使用半参数贝叶斯方法在随机临床试验中使用事件发生时间和毒性数据进行群体发现
DOI：
10.1111/biom.13289
复制
发表时间：
2021
期刊：
影响因子：
1.9
通讯作者：
Abe, Hiroyasu
中科院分区：
文献类型：
--
作者：
Morita, Satoshi;Müller, Peter;Abe, Hiroyasu
一种基于效用的贝叶斯人口发现(BaPoFi)方法是由Morita和Müler提出的，用于分析随机临床试验的数据，目的是确定良好的预测性基线协变量，以便为未来的研究优化目标人群。该方法将人口发现过程定义为一个正式的决策问题，并使用一个灵活的概率模型来定义一个回归均值函数。BaPoFi被构建为处理单个连续或二元结果变量。在这篇文章中，我们开发了BaPoFi-TTE作为早期方法的扩展，用于具有审查的临床重要病例的事件间隔时间(TTE)数据，并考虑了毒性结果。我们使用半参数故障时间模型来模拟TTE数据与基线协变量的关联，其中Pólya树先验用于未知误差项，随机森林用于灵活的回归均值函数。我们定义了一个效用函数，它解决了有效性和毒性之间的权衡，作为发现人群的重要临床考虑因素之一。我们在大量的仿真研究中检验了所提出的方法的操作特性。作为说明，我们将所建议的方法应用于来自随机肿瘤学临床试验的数据。在基于参数模型的相同数据的初步分析中的担忧促使了拟议的更一般的方法。
A utility‐based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller to analyze data from a randomized clinical trial with the aim of identifying good predictive baseline covariates for optimizing the target population for a future study. The approach casts the population finding process as a formal decision problem together with a flexible probability model using a random forest to define a regression mean function. BaPoFi is constructed to handle a single continuous or binary outcome variable. In this paper, we develop BaPoFi‐TTE as an extension of the earlier approach for clinically important cases of time‐to‐event (TTE) data with censoring, and also accounting for a toxicity outcome. We model the association of TTE data with baseline covariates using a semiparametric failure time model with a Pólya tree prior for an unknown error term and a random forest for a flexible regression mean function. We define a utility function that addresses a trade‐off between efficacy and toxicity as one of the important clinical considerations for population finding. We examine the operating characteristics of the proposed method in extensive simulation studies. For illustration, we apply the proposed method to data from a randomized oncology clinical trial. Concerns in a preliminary analysis of the same data based on a parametric model motivated the proposed more general approach.
影响因子：
1.7
作者：
Graf, Alexandra C.;Posch, Martin;Koenig, Franz
通讯作者：
Koenig, Franz
DOI：
10.1198/016214506000000384
发表时间：
2006-12-01
影响因子：
3.7
作者：
Hanson, Timothy E.
通讯作者：
Hanson, Timothy E.
影响因子：
11.8
作者：
Renfro LA;Mallick H;An MW;Sargent DJ;Mandrekar SJ
通讯作者：
Mandrekar SJ