Many clinical questions involve estimating the effects of multiple treatments using observational data. When using longitudinal data, the interest is often in the effect of treatment strategies that involve sustaining treatment over time. This requires causal inference methods appropriate for handling multiple treatments and time-dependent confounding. Robins Generalised methods (g-methods) are a family of methods which can deal with time-dependent confounding and some of these have been extended to situations with multiple treatments, although there are currently no studies comparing different methods in this setting. We show how five g-methods (inverse-probability-of-treatment weighted estimation of marginal structural models, g-formula, g-estimation, censoring and weighting, and a sequential trials approach) can be extended to situations with multiple treatments, compare their performances in a simulation study, and demonstrate their application with an example using data from the UK CF Registry.

翻译：许多临床问题涉及利用观察性数据估计多种治疗方案的效应。在纵向数据研究中，研究者常关注需长期维持的治疗策略效果，这要求采用能处理多治疗方案与时变混杂因素的因果推断方法。罗宾斯广义方法（g-methods）是一类可处理时变混杂因素的方法族，其中部分方法已拓展至多治疗场景，但目前尚缺乏针对该情景下不同方法的对比研究。本文展示了如何将五种g-methods（边际结构模型逆概率加权估计、g-公式法、g-估计法、删失与加权法、序贯试验法）扩展至多治疗情境，通过模拟研究比较各方法性能，并以英国囊性纤维化登记数据为例进行实证应用。