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A Semi‐parametric Bayesian approach to population finding with time‐to‐event and toxicity data in a randomized clinical trial
Biometrics ( IF 1.4 ) Pub Date : 2020-05-07 , DOI: 10.1111/biom.13289
Satoshi Morita 1 , Peter Müller 2 , Hiroyasu Abe 1
Affiliation  

A utility-based Bayesian population finding (BaPoFi) method was proposed by Morita and Müller (2017, Biometrics, 1355-1365) 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 semi-parametric 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.

中文翻译:

在随机临床试验中使用事件发生时间和毒性数据进行人群发现的半参数贝叶斯方法

Morita 和 Müller (2017, Biometrics, 1355-1365) 提出了一种基于效用的贝叶斯种群发现 (BaPoFi) 方法来分析随机临床试验的数据,目的是确定良好的预测基线协变量,以优化目标人群未来的研究。该方法将人口发现过程与使用随机森林定义回归均值函数的灵活概率模型一起作为正式决策问题。BaPoFi 被构造为处理单个连续或二元结果变量。在本文中,我们开发了 BaPoFi-TTE 作为早期方法的扩展,用于具有删失的时间到事件 (TTE) 数据的临床重要病例,并考虑毒性结果。我们使用半参数故障时间模型对 TTE 数据与基线协变量的关联进行建模,其中 Pólya 树先验用于未知误差项,随机森林用于灵活回归均值函数。我们定义了一个效用函数,它解决了疗效和毒性之间的权衡,作为人群发现的重要临床考虑因素之一。我们在广泛的模拟研究中检查了所提出方法的操作特性。为了说明,我们将所提出的方法应用于随机肿瘤临床试验的数据。基于参数模型对相同数据进行初步分析的担忧促使提出了更通用的方法。我们定义了一个效用函数,它解决了疗效和毒性之间的权衡,作为人群发现的重要临床考虑因素之一。我们在广泛的模拟研究中检查了所提出方法的操作特性。为了说明,我们将所提出的方法应用于随机肿瘤临床试验的数据。基于参数模型对相同数据进行初步分析的担忧促使提出了更通用的方法。我们定义了一个效用函数,该函数解决了疗效和毒性之间的权衡,作为人群发现的重要临床考虑因素之一。我们在广泛的模拟研究中检查了所提出方法的操作特性。为了说明,我们将所提出的方法应用于随机肿瘤临床试验的数据。基于参数模型对相同数据进行初步分析的担忧促使提出了更通用的方法。
更新日期:2020-05-07
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