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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AI 根据所提供的论文材料归类：摘要研究随机试验中随机化后变量对治疗比较的影响，并联合分析受试者的重复测量数据。
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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
- 在随机试验中，依从性或合并用药等随机化后变量与主要终点共同变化时，如何补充意向治疗分析并比较条件性终点差异？
- 方法与思路
- 作者将随机化后变量视为另一个终点，用 SUR 联合建模两组重复测量，以向量 ARMA 描述扰动，并采用极大似然估计。
- 材料陈述的贡献
- 摘要称联合模型能容纳两个变量关系的演变并简化协方差结构，可在指定另一终点行为的条件下检验主要终点差异。
- 阅读关注点
- 应核对条件性比较的统计目标、随机化后变量与终点的关系，以及设定相同行为的比较是否具有因果解释及所需条件。
- 阅读边界
- OpenAlex 摘要版本未注明；所述条件比较的因果识别条件未说明，随机分组本身不能保证随机化后变量调整的因果有效性。
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.