Marginal structural models have been increasingly used by analysts in recent years to account for confounding bias in studies with time-varying treatments. The parameters of these models are often estimated using inverse probability of treatment weighting. To ensure that the estimated weights adequately control confounding, it is possible to check for residual imbalance between treatment groups in the weighted data. Several balance metrics have been developed and compared in the cross-sectional case but have not yet been evaluated and compared in longitudinal studies with time-varying treatment. We have first extended the definition of several balance metrics to the case of a time-varying treatment, with or without censoring. We then compared the performance of these balance metrics in a simulation study by assessing the strength of the association between their estimated level of imbalance and bias. We found that the Mahalanobis balance performed best.Finally, the method was illustrated for estimating the cumulative effect of statins exposure over one year on the risk of cardiovascular disease or death in people aged 65 and over in population-wide administrative data. This illustration confirms the feasibility of employing our proposed metrics in large databases with multiple time-points.

翻译：边际结构模型近年来被分析人员越来越多地用于处理时变治疗研究中的混杂偏倚。这些模型的参数通常通过逆概率治疗加权法进行估计。为确保估计的权重能充分控制混杂，可在加权数据中检查治疗组间的残差不平衡。已有若干平衡指标在横断面案例中得到开发与比较，但尚未在时变治疗纵向研究中进行评估与比较。我们首先将多个平衡指标的定义扩展至存在或不存在删失的时变治疗场景，随后通过模拟研究评估这些平衡指标的估计不平衡水平与偏倚之间的关联强度以比较其表现。研究发现马氏距离平衡表现最优。最后，该方法被应用于基于人群行政数据中65岁及以上人群的他汀类药物一年累积暴露对心血管疾病或死亡风险影响的估计，以验证其可行性。本实例证实了所提指标在含多个时间点的大型数据库中的应用可行性。