Identifying predictors of resilience to stressors in single-arm studies of pre-post change.
Identifying predictors of resilience to stressors in single-arm studies of pre-post change.
复制标题
在变革前后的单臂研究中确定压力源恢复力的预测因子。
DOI：
10.1093/biostatistics/kxad018
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Bandeen-Roche,Karen
中科院分区：
文献类型：
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作者：
Varadhan,Ravi;Zhu,Jiafeng;Bandeen-Roche,Karen
许多老年人在生活中的某个时候都会经历重大的压力源。在一个主要的压力源之后恢复良好的能力被称为弹性。老年医学研究的一个重要目标是确定影响压力恢复力的因素。对老年人恢复力的研究通常是用单臂进行的，每个人都会经历压力源。由于数学耦合和平均值回归（RTM），回归变化与基线的简单方法产生偏倚估计值。我们开发了一种方法来纠正偏差。我们扩展的方法，包括协变量。我们的方法认为，一个反事实的控制组，并涉及敏感性分析，以评估不同的控制组参数设置。只需要最小的分布假设。仿真研究证明了该方法的有效性。我们使用接受全膝关节置换术（TKR）的老年人（N7239）的大型登记研究来说明该方法。我们演示了如何利用外部数据来约束敏感性分析。初始分析涉及几种治疗效果修饰因素，包括基线功能、年龄、体重指数（BMI）、性别、合并症数量、收入和种族。校正分析显示，基线（应激前）功能与TKR后恢复无强相关性，在协变量中，只有年龄和合并症数量与所有功能领域的应激后恢复一致且呈负相关。校正数学耦合和RTM对于得出关于协变量和基线状态对前后变化的影响的有效推断是必要的。我们的方法为此提供了一个简单的估计。
Many older adults experience a major stressor at some point in their lives. The ability to recover well after a major stressor is known as resilience. An important goal of geriatric research is to identify factors that influence resilience to stressors. Studies of resilience in older adults are typically conducted with a single-arm where everyone experiences the stressor. The simplistic approach of regressing change versus baseline yields biased estimates due to mathematical coupling and regression to the mean (RTM). We develop a method to correct the bias. We extend the method to include covariates. Our approach considers a counterfactual control group and involves sensitivity analyses to evaluate different settings of control group parameters. Only minimal distributional assumptions are required. Simulation studies demonstrate the validity of the method. We illustrate the method using a large, registry of older adults (N7239) who underwent total knee replacement (TKR). We demonstrate how external data can be utilized to constrain the sensitivity analysis. Naive analyses implicated several treatment effect modifiers including baseline function, age, body-mass index (BMI), gender, number of comorbidities, income, and race. Corrected analysis revealed that baseline (pre-stressor) function was not strongly linked to recovery after TKR and among the covariates, only age and number of comorbidities were consistently and negatively associated with post-stressor recovery in all functional domains. Correction of mathematical coupling and RTM is necessary for drawing valid inferences regarding the effect of covariates and baseline status on pre–post change. Our method provides a simple estimator to this end.