Irregular visit times in longitudinal studies can jeopardise marginal regression analyses of longitudinal data by introducing selection bias when the visit and outcome processes are associated. Inverse intensity weighting is a useful approach to addressing such selection bias when the visiting at random assumption is satisfied, i.e., visiting at time $t$ is independent of the longitudinal outcome at $t$, given the observed covariate and outcome histories up to $t$. However, the visiting at random assumption is unverifiable from the observed data, and informative visit times often arise in practice, e.g., when patients' visits to clinics are driven by ongoing disease activities. Therefore, it is necessary to perform sensitivity analyses for inverse intensity weighted estimators (IIWEs) when the visit times are likely informative. However, research on such sensitivity analyses is limited in the literature. In this paper, we propose a new sensitivity analysis approach to accommodating informative visit times in marginal regression analysis of irregular longitudinal data. Our sensitivity analysis is anchored at the visiting at random assumption and can be easily applied to existing IIWEs using standard software such as the coxph function of the R package Survival. Moreover, we develop novel balancing weights estimators of regression coefficients by exactly balancing the covariate distributions that drive the visit and outcome processes to remove the selection bias after weighting. Simulations show that, under both correct and incorrect model specifications, our balancing weights estimators perform better than the existing IIWEs using weights estimated by maximum partial likelihood. We applied our methods to data from a clinic-based cohort study of psoriatic arthritis and provide an R Markdown tutorial to demonstrate their implementation.

翻译：纵向研究中不规则的访视时间可能因访视与结局过程相关而引入选择偏倚，从而危及纵向数据的边际回归分析。当满足随机访视假设（即给定至时间$t$的观测协变量和结局历史，时间$t$的访视与纵向结局独立）时，逆强度加权是解决此类选择偏倚的有效方法。然而，随机访视假设无法通过观测数据验证，而信息性访视时间在实践中经常出现（例如，患者就诊由持续疾病活动驱动）。因此，当访视时间可能具有信息性时，有必要对逆强度加权估计量（IIWEs）进行敏感性分析。然而，现有文献中对此类敏感性分析的研究有限。本文提出了一种新的敏感性分析方法，用于处理不规则纵向数据边际回归分析中的信息性访视时间。我们的敏感性分析以随机访视假设为锚点，可轻松应用于现有IIWEs，借助标准软件（如R包Survival中的coxph函数）实现。此外，我们通过精确平衡驱动访视和结局过程的协变量分布开发了新的回归系数平衡权重估计量，以消除加权后的选择偏倚。模拟实验表明，在模型设定正确与错误两种情况下，我们的平衡权重估计量均优于使用最大偏似然估计权重的现有IIWEs。我们将方法应用于银屑病关节炎临床队列研究数据，并提供了R Markdown教程以演示其实现过程。