Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at pre-determined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. IIW-GEEs have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We show that currently available methods can be biased for situations in which the health outcome of interest may influence a subject's dropout from the study. We have extended the IIW-GEE framework to adjust for informative dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for.

翻译：纵向数据在生物医学研究中十分常见，包括随机试验和回顾性队列研究。受试者通常在一段时间内接受随访，并可能按预定时间点安排复诊。然而，受试者可能错过预约或在非指定时间复诊，导致访视过程呈现不规则性。逆强度加权广义估计方程（IIW-GEE）正是为处理这种不规则性而发展的一种方法，该方法将访视强度模型的估计值作为权重，应用于具有独立相关结构的广义估计方程模型。我们证明，当所关注的健康结局可能影响受试者从研究中脱落时，现有方法可能产生偏倚。我们扩展了IIW-GEE框架以校正信息性脱落，并通过模拟研究证明该偏倚可被显著降低。我们使用STAR*D临床试验数据对该方法进行了示例分析，发现当未考虑信息性脱落时，疾病轨迹通常会被高估。