Quantitative Sciences Division, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, 550 N. Broadway Street, Baltimore, MD 21205, USA.
Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA.
Biostatistics. 2024 Oct 1;25(4):1094-1111. doi: 10.1093/biostatistics/kxad018.
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 (N =7239) 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.
许多老年人在其一生中的某个时刻都会经历重大压力源。从重大压力源中恢复良好的能力被称为韧性。老年医学研究的一个重要目标是确定影响压力源韧性的因素。对老年人韧性的研究通常采用单臂研究，即每个人都经历压力源。由于数学耦合和向均值回归（RTM），回归基线变化的简单方法会产生有偏差的估计。我们开发了一种纠正偏差的方法。我们将该方法扩展到包含协变量。我们的方法考虑了反事实对照组，并进行了敏感性分析，以评估对照组参数的不同设置。仅需要最小的分布假设。模拟研究证明了该方法的有效性。我们使用一个大型的老年人注册表（N = 7239）来演示该方法，这些老年人接受了全膝关节置换术（TKR）。我们展示了如何利用外部数据来限制敏感性分析。直观分析表明，有几个治疗效果修饰因子，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。校正分析表明，基线（压力源前）功能与 TKR 后的恢复没有密切联系，在协变量中，只有年龄和合并症数量在所有功能领域与压力源后的恢复呈负相关且一致。纠正数学耦合和 RTM 对于关于协变量和基线状态对前后变化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器。
