Many observational studies feature irregular longitudinal data, where the observation times are not common across individuals in the study. Further, the observation times may be related to the longitudinal outcome. In this setting, failing to account for the informative observation process may result in biased causal estimates. This can be coupled with other sources of bias, including non-randomized treatment assignments and informative censoring. This paper provides an overview of a flexible weighting method used to adjust for informative observation processes and non-randomized treatment assignments. We investigate the sensitivity of the flexible weighting method to violations of the noninformative censoring assumption, examine variable selection for the observation process weighting model, known as inverse intensity weighting, and look at the impacts of weight trimming for the flexible weighting model. We show that the flexible weighting method is sensitive to violations of the noninformative censoring assumption and show that a previously proposed extension fails under such violations. We also show that variables confounding the observation and outcome processes should always be included in the observation intensity model. Finally, we show that weight trimming should be applied in the flexible weighting model when the treatment assignment process is highly informative and driving the extreme weights. We conclude with an application of the methodology to a real data set to examine the impacts of household water sources on malaria diagnoses.

翻译：许多观察性研究涉及不规则纵向数据，其中观测时间在研究个体间并不统一。此外，观测时间可能与纵向结局相关。在此背景下，若未能考虑信息性观测过程，可能导致因果估计出现偏倚。这一问题还可能与其他偏倚来源相结合，包括非随机化处理分配和信息性删失。本文概述了一种用于调整信息性观测过程与非随机化处理分配的灵活加权方法。我们研究了灵活加权方法对非信息性删失假设违反的敏感性，检验了观测过程加权模型（即逆强度加权）的变量选择问题，并探讨了权重修整对灵活加权模型的影响。研究表明，灵活加权方法对非信息性删失假设的违反具有敏感性，且先前提出的扩展方法在此类违反情况下会失效。我们还证明，混淆观测过程与结局过程的变量应始终纳入观测强度模型。最后，我们指出当处理分配过程具有高度信息性并导致极端权重时，应在灵活加权模型中实施权重修整。本文最后通过实际数据集应用该方法，以检验家庭水源对疟疾诊断的影响。