Planning a method for covariate adjustment in individually-randomised trials: a practical guide
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个体随机试验中协变量调整方法的规划指南
- 研究问题
- 为个体随机试验制定统计分析计划时，如何依据目标效应、协变量平衡、样本量和缺失数据，在不同协变量调整方法间选择？
- 方法与思路
- 作者从分析计划制定角度比较直接调整、标准化和处理逆概率加权，讨论效率、模型误设、收敛、标准误与缺失数据处理。
- 材料陈述的贡献
- 摘要称三类方法各有适用情境，并指出边际目标量下直接调整的收敛问题及小样本加权标准误风险，以试验再分析展示选择考量。
- 阅读关注点
- 应核对边际与条件目标量的区别、效率结论的条件、随机化平衡的处理方式，以及小样本标准误和缺失结果分析所需假设。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实。摘要未给出完整适用条件和比较数值，方法选择建议不能脱离具体试验情境。
原文摘要
Background: It has long been advised to account for baseline covariates in the analysis of confirmatory randomised trials, with the main statistical justifications being that this increases power and, when a randomisation scheme balanced covariates, permits a valid estimate of experimental error. There are various methods available to account for covariates but it is not clear how to choose among them. Methods: Taking the perspective of writing a statistical analysis plan, we consider how to choose between the three most promising broad approaches: direct adjustment, standardisation and inverse-probability-of-treatment weighting. Results: The three approaches are similar in being asymptotically efficient, in losing efficiency with mis-specified covariate functions, and in handling designed balance. If a marginal estimand is targeted (for example, a risk difference or survival difference), then direct adjustment should be avoided because it involves fitting non-standard models that are subject to convergence issues. Convergence is most likely with IPTW. Robust standard errors used by IPTW are anti-conservative at small sample sizes. All approaches can use similar methods to handle missing covariate data. With missing outcome data, each method has its own way to estimate a treatment effect in the all-randomised population. We illustrate some issues in a reanalysis of GetTested, a randomised trial designed to assess the effectiveness of an electonic sexually-transmitted-infection testing and results service. Conclusions: No single approach is always best: the choice will depend on the trial context. We encourage trialists to consider all three methods more routinely.