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

酒精使用障碍治疗随机对照试验中的可能反应者分析和模型错误指定测试

• 批准号: 10522414
• 负责人: EUGENE M LASKA
• 金额: $ 62.26万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-16 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522414
• 关键词: Address; Characteristics; Clinical; Clinical Trials; Data; Data Set; Development; Diagnostic; Diagnostic Procedure; Double-Blind Method; Enrollment; Failure; Goals; Individual; Influentials; Knowledge; Lead; Machine Learning; Matched Group; Mental disorders; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Outcome; Outcome Measure; Patients; Performance; Placebos; Probability; Process; Property; Public Health; Publishing; Randomized; Randomized Clinical Trials; Randomized Controlled Trials; Reproducibility; Research; Research Project Grants; Sampling; Secondary to; Specific qualifier value; Subgroup; Testing; Time; Work; alcohol abuse therapy; alcohol use disorder; base; clinical trial analysis; gabapentin; improved; machine learning model; member; neural network; novel; novel strategies; patient subsets; personalized medicine; precision medicine; predicting response; predictive modeling; primary outcome; process repeatability; prognostic; prognostic model; random forest; response; secondary analysis; semiparametric; simulation; statistical and machine learning; treatment choice; treatment comparison; treatment effect

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.

项目摘要/摘要 我们已经开发了一种使用潜在结果来分析随机临床试验(RCT)的策略 因果框架。在试验结束时识别对测试处理T的可能应答者(LR) 对这一浓缩样本中T和安慰剂P之间的差异进行统计检验。识别出LR 在试验结束时，通过拟合一个被称为预后评分函数的模型来估计预期反应 T作为基线特征的函数。LR子集包括其预期响应超过 预先规定的临床定义的最低限度。识别LR实现了精准医学的一个重要目标。这个 在LRs中，T与P之间的因果效应是基于在层内观察到的结果来评估的 样本与他们的预后评分相匹配。众所周知，尤其是对于随机样本的子集， 错误的模型说明可能会导致错误的结论。为了防止这种可能性在 对于预后评分的估计，我们采用了一种新的方法来进行随机对照试验，我们称之为随机对照试验的演练 (DRrct)诊断。它正式评估了模型错误说明的可能性。LR方法的价值 在对一项为期26周的大型多部位延期释放的双盲随机对照试验的重新分析中得到了证实 加巴喷丁安非他明(GE-XR)与安慰剂治疗酒精使用障碍(AUD)的比较。相当可观 根据他们的临床情况，Ge-XR的治疗对预测为LRS的患者亚群有好处 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 预测分数函数，并扩展我们对LR和DRrct的统计特性的知识 方法：研究方法。目标是最大限度地减少偏差并提高预后评分模型的估计精度和 提高在LR亚群中测试治疗效果的能力。为了实现这一点，我们将使用三个 战略：可能的分析/理论方法、随机对照试验的模拟和对六个NIAAA的重新分析 AUD治疗的随机对照试验。虽然在六个试验中的大多数试验中，没有发现治疗差异，但它 可能是可以确定其成员获得实质性临床益处的LR亚群。每次重新分析 将利用DRRCT方法来评估模型误指定的可能性。LR方法具有 有可能改变随机对照试验分析的标准做法，降低分析造成的失败率 限于全样本均值差异，便于个体化用药；DRrct方法具有 有可能降低不可复制的随机对照试验的比率；对NIAAA的六项研究的重新分析具有 有可能发现患者特征和可能的临床意义之间的关系 对先前研究的候选AUD治疗的应答者。