Accounting for Covariates Observed Post Randomization for Discrete and Continuous Repeated Measures Data
离散与连续重复测量中随机化后协变量的处理AI 解读 ↓
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离散与连续重复测量中随机化后协变量的处理
- 研究问题
- 随机临床试验中，主要终点与随机化后观测的协变量随时间共同变化时，如何建模并对关注的行为轨迹进行推断？
- 方法与思路
- 作者将随机化后协变量视为结果，用广义估计方程分别建模两组重复测量，再以似不相关回归联合并施加参数限制。
- 材料陈述的贡献
- 摘要提出联合分析框架，通过协变量回归参数限制刻画所关注的行为轨迹，说明估计与识别程序并提供应用示例。
- 阅读关注点
- 应核对参数限制对应的目标、识别条件、两类测量的关联处理，以及联合模型中的推断能否获得所需因果解释。
- 阅读边界
- 仅据所给 Crossref 摘要，具体识别条件和效应定义未说明；随机化本身不足以确认所述随机化后调整具有因果解释。
原文摘要
SUMMARY Adjusting for covariates observed post randomization is a difficult issue in randomized clinical trials. One approach is to enter these covariates as time-dependent covariates in a general linear model. However, this approach fails to account for interactions between the primary end point and confounding variable as they evolve over time. In this paper, we adopt the view that, since the confounder is observed following randomization, it should be treated as an outcome and analysed accordingly. We consider the repeated measures design where both the primary end point and the confounding measure are observed repeatedly over patient follow-up. A generalized estimating equation model is applied to allow each set of repeated measures to be modelled in terms of important explanatory variables. Seemingly unrelated regression combines the two models into an overall framework for analysis. Inference is then performed by imposing restrictions on the confounder regression parameters to reflect the behaviour profile of interest. Estimation and identification procedures are described and the methodology is illustrated with an example.