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: 10705711
• 负责人: EUGENE M LASKA
• 金额: $ 73.36万
• 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/10705711
• 关键词: Address; Characteristics; Clinical; Clinical Trials; Combined Modality Therapy; Data; Data Set; Development; Diagnostic; Diagnostic Procedure; Double-Blind Method; Enrollment; Failure; Goals; Individual; Influentials; Knowledge; Machine Learning; Matched Group; Mental disorders; Methods; Modeling; National Institute on Alcohol Abuse and Alcoholism; Outcome; Outcome Measure; Parameter Estimation; Patients; Performance; Placebos; Probability; Process; Property; Public Health; Publishing; Randomized; Randomized, Controlled Trials; Reproducibility; Research; Research Project Grants; Sampling; Secondary to; Site; Specific qualifier value; Subgroup; Testing; Work; alcohol abuse therapy; alcohol use disorder; 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; prognostic; prognostic model; random forest; randomized, clinical trials; 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 我们已经制定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。可能在试验结束时确定对测试治疗t的响应者（LRS）和 T和安慰剂之间的差异，在此丰富样本中进行P。确定LR 在试验结束时，通过拟合模型，称为预后分数函数，该模型估计了预期响应 t是基线特征的函数。 LR子集由其预期响应超过A的个体组成 预先指定的临床定义最小。确定LR实现了精确医学的重要目标。这 根据观察到的结果，根据观察到的结局，对LR的因果效应与LR相比。 样本与他们的预后分数相匹配。众所周知，特别是对于随机样品的子集， 该模型的错误指定可能导致虚假结论。防止这种可能性 预后分数的估计，我们已经改编了一种方法，用于RCT，我们称之为RCT Dry Run （DRRCT）诊断。它正式评估了模型错误指定的潜力。 LR方法的值 在重新分析大型多站点26周长的双盲RCT的重新分析中已被证明 与安慰剂治疗酒精使用障碍（AUD）相比，Gabapentin Enacarbil（GE-XR）。重大的 发现用GE-XR治疗的益处是根据其临床预测为LRS的子集的子集 特征。在该研究项目中，我们将探讨新的统计和机器学习建模策略 预后分数功能并扩展我们对LR和DRRCT的统计特性的了解 方法。目的是在估计预后分数模型和 在LR亚群中测试治疗效应的功率增加。为此，我们将使用三个 策略：在可能的情况下进行分析/理论方法，RCT的模拟和六个NIAAA的重新分析 RCT比较AUD的治疗方法。尽管在六项试验中的大多数中，都没有发现治疗差异，但 可能是可以确定其成员获得可观临床益处的LR亚组。每次重新分析 将利用DRRCT方法来评估模型错误指定的可能性。 LR方法具有 改变标准实践以分析RCT，降低分析引起的故障率 限于整个样本平均差异，并促进个性化医学； DRRCT方法具有 降低不可衍生RCT的速率的潜力；六项NIAAA研究的重新分析具有 发现患者特征与可能的临床有意义的关系的可能性 对先前研究的候选AUD治疗的响应者。