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Characterizing Highly Benefited Patients in Randomized Clinical Trials.
International Journal of Biostatistics ( IF 1.2 ) Pub Date : 2017-05-20 , DOI: 10.1515/ijb-2016-0045
Vivek Charu , Paul B. Rosenberg , Lon S. Schneider , Lea T. Drye , Lisa Rein , David Shade , Constantine G. Lyketsos , Constantine E. Frangakis

Physicians and patients may choose a certain treatment only if it is predicted to have a large effect for the profile of that patient. We consider randomized controlled trials in which the clinical goal is to identify as many patients as possible that can highly benefit from the treatment. This is challenging with large numbers of covariate profiles, first, because the theoretical, exact method is not feasible, and, second, because usual model-based methods typically give incorrect results. Better, more recent methods use a two-stage approach, where a first stage estimates a working model to produce a scalar predictor of the treatment effect for each covariate profile; and a second stage estimates empirically a high-benefit group based on the first-stage predictor. The problem with these methods is that each of the two stages is usually agnostic about the role of the other one in addressing the clinical goal. We propose a method that characterizes highly benefited patients by linking model estimation directly to the particular clinical goal. It is shown that the new method has the following two key properties in comparison with existing approaches: first, the meaning of the solution with regard to the clinical goal is the same, and second, the value of the solution is the best that can be achieved when using the working model as a predictor, even if that model is incorrect. In the Citalopram for Agitation in Alzheimer's Disease (CitAD) randomized controlled trial, the new method identifies substantially larger groups of highly benefited patients, many of whom are missed by the standard method.

中文翻译:

在随机临床试验中描述高获益患者的特征。

仅当预计某种治疗对该患者的情况有很大影响时,医生和患者才会选择该治疗。我们考虑随机对照试验,其中临床目标是确定尽可能多的可以从治疗中受益的患者。对于大量协变量配置文件来说,这是一个挑战,首先,因为理论上的精确方法不可行,其次,因为通常的基于模型的方法通常会给出不正确的结果。更好、更新的方法使用两阶段方法,其中第一阶段估计工作模型,以生成每个协变量概况的治疗效果的标量预测因子;第二阶段根据第一阶段预测器凭经验估计高收益组。这些方法的问题在于,两个阶段中的每个阶段通常不知道另一个阶段在实现临床目标中的作用。我们提出了一种通过将模型估计直接与特定临床目标联系起来来表征高度受益患者的方法。结果表明,与现有方法相比,新方法具有以下两个关键特性:第一,解对于临床目标的意义是相同的;第二,解的值是可以得到的最佳值。使用工作模型作为预测变量时可以实现这一目标,即使该模型不正确。在西酞普兰治疗阿尔茨海默病躁动症 (CitAD) 随机对照试验中,新方法确定了更多的高度受益患者群体,其中许多人被标准方法遗漏了。
更新日期:2019-11-01
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