Supplementing the Intent-to-Treat Analysis: Accounting for Covariates Observed Postrandomization in Clinical Trials
补充意向治疗分析：处理临床试验中随机化后观测的协变量AI 解读 ↓
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补充意向治疗分析：处理临床试验中随机化后观测的协变量
- 研究问题
- 临床试验中，如何联合处理主要结局与随机化后测得的依从性或伴随用药，并分析给定后者行为时的组间结局差异？
- 方法与思路
- 作者将随机化后变量视为另一结局，以似不相关回归联合建模重复测量，用向量 ARMA 描述扰动，并通过 Fisher scoring 求极大似然估计。
- 材料陈述的贡献
- 摘要称该框架可描述两变量关系的动态演变，形成简约的联合协方差结构，并检验给定随机化后变量行为时的主要结局假设。
- 阅读关注点
- 应核对联合均值及协方差设定、条件假设的具体定义，以及等量伴随用药下的比较是否具有因果解释和所需条件。
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原文摘要
A problem that arises frequently in the analysis of clinical trials is accounting for covariates observed postrandomization. Two examples are patient compliance measured through pill counts and the dose of a concomitant medication. Typically these are measured concurrently with the primary endpoint at the regularly scheduled follow-ups during the course of the study. To account for these effects, the confounders are sometimes introduced as time-dependent covariates into the statistical model. But there are conceptual and statistical difficulties with this approach. In this article we adopt the view that because the confounder is observed postrandomization, it must be considered as an endpoint in its own right. A seemingly unrelated regression (SUR) model is proposed to relate the two sets of repeated measures to important explanatory variables, and a vector autoregressive moving average (ARMA) time series model is used to characterize the disturbance terms within and across the two series. This accommodates the evolving relationship between the two variables and results in a parsimonious structure in the joint covariance matrix. Maximum likelihood estimation is applied using a Fisher scoring algorithm. Inference in this model is then performed by considering hypotheses on the primary endpoint conditionally on some behavior for the confounder. For example, if the two treatment groups had received an equivalent amount of concomitant medication, is there a significant difference in the primary endpoint? The methodology is illustrated with an example.