Likely responder analysis and tests of model misspecification in randomized controlled trials of treatments for Alcohol Use Disorder

项目概要/摘要 我们已经开发了一种使用潜在结果分析随机临床试验（RCT）的策略 因果框架在试验结束时确定试验治疗T的可能应答者（LR）， 对该富集样品中T和安慰剂P之间的差异进行统计学检验。确定LR 在试验结束时，通过拟合一个模型，称为预后评分函数，估计预期的反应， 作为基线特征的函数。LR子集包括其预期响应超过预期响应的个体。 预先规定的临床定义的最小值。识别LR实现了精准医疗的重要目标。的 根据以下分层内观察到的结局，评估LR中T与P相比的因果效应 样本的预后评分相匹配。众所周知，特别是对于随机样本的子集， 模型的错误设定可能导致错误的结论。为了防止这种可能性， 为了估计预后评分，我们采用了一种新的方法，我们称之为RCT模拟试验 （DRrct）诊断。它正式评估了模型错误指定的可能性。LR方法的价值 已在一项大型多中心26周长的双盲RCT（缓释）的再分析中得到证实 加巴喷丁依那必尔（GE-XR）与安慰剂相比，用于治疗酒精使用障碍（AUD）。实质性 GE-XR治疗的益处被发现用于基于临床表现预测为LR的患者亚组， 功能.在这个研究项目中，我们将探索新的统计和机器学习建模策略， 预后评分函数，并扩展我们对LR和DRrct的统计特性的了解 方法.目标是最大限度地减少偏倚并提高预后评分模型估计的精度， 增加检验LR亚群治疗效果的把握度。为了做到这一点，我们将使用三个 策略：分析/理论方法（如可能），随机对照试验模拟和六个NIAAA的再分析 比较AUD治疗的RCT。虽然在六项试验中的大多数试验中，没有发现治疗差异， 可能是LR亚组可以识别其成员获得实质性的临床益处。每次再分析 将利用DRrct方法来评估模型错误指定的可能性。LR方法具有 可能改变RCT分析的标准实践，降低分析导致的失败率 仅限于整个样本的平均差异，并有助于个性化医疗; DRrct方法具有 有可能降低不可重复的RCT的发生率;对6项NIAAA研究的再分析表明， 发现患者特征与可能的 先前研究的候选AUD治疗的应答者。

Project Summary/ Abstract We have developed a strategy for the analysis of randomized clinical trials (RCTs) using a potential outcomes causal framework. Likely responders (LRs) to a test treatment T are identified at the end of the trial and a statistical test of the difference between T and placebo, P in this enriched sample is performed. LRs are identified at the end of the trial by fitting a model, called a prognostic score function, that estimates the expected response to T as a function of baseline features. The LR subset comprises individuals whose expected response exceeds a pre-specified clinically defined minimum. Identifying LR achieves an important goal of precision medicine. The causal effect of T compared to P among LRs is appraised based on the observed outcomes within strata of samples matched on their prognostic score. It is well known that, especially for subsets of a random sample, misspecification of the model can lead to spurious conclusions. To protect against this possibility in the estimation of the prognostic score, we have adapted an approach, novel to RCTs, that we call the RCT dry run (DRrct) diagnostic. It formally evaluates the potential for model misspecification. The value of the LR method has been demonstrated in a reanalysis of a large multisite 26-week long double-blind RCT of extended release gabapentin enacarbil (GE-XR) compared to placebo for the treatment of alcohol use disorder (AUD). Substantial benefits of treatment with GE-XR were found for the subset of patients predicted to be LRs based on their clinical features. In this research project, we will explore new statistical and machine learning modeling strategies for the prognostic score function and expand our knowledge of the statistical properties of the LR and DRrct methods. The goal is to minimize bias and increase precision in estimation of the prognostic score model and increasing power to test treatment effects in the LR subpopulation. To accomplish this we will use three strategies: analytic/theoretical methods where possible, simulation of RCTs and the reanalysis of six NIAAA RCTs comparing treatments for AUD. Although in most of the six trials, no treatment differences were found, it may be that LR subgroups can be identified whose members obtain substantial clinical benefit. Each reanalysis will utilize the DRrct method to appraise the possibility of model misspecification. The LR method has the potential to change standard practice for the analysis of RCTs, reduce the rate of failure caused by analyses limited to whole sample mean differences, and facilitate personalized medicine; the DRrct method has the potential to reduce the rate of irreproducible RCTs; and the reanalysis of the six NIAAA studies has the possibility of uncovering clinically meaningful relationships between patient characteristics and likely responders to previously studied candidate AUD treatments.