Many trials are designed to collect outcomes at or around pre-specified times after randomization. In practice, there can be substantial variability in the times when participants are actually assessed. Such irregular assessment times pose a challenge to learning the effect of treatment since not all participants have outcome assessments at the times of interest. Furthermore, observed outcome values may not be representative of all participants' outcomes at a given time. This problem, known as informative assessment times, can arise if participants tend to have assessments when their outcomes are better (or worse) than at other times, or if participants with better outcomes tend to have more (or fewer) assessments. Methods have been developed that account for some types of informative assessment; however, since these methods rely on untestable assumptions, sensitivity analyses are needed. We develop a sensitivity analysis methodology by extending existing weighting methods. Our method accounts for the possibility that participants with worse outcomes at a given time are more (or less) likely than other participants to have an assessment at that time, even after controlling for variables observed earlier in the study. We apply our method to a randomized trial of low-income individuals with uncontrolled asthma. We illustrate implementation of our influence-function based estimation procedure in detail, and we derive the large-sample distribution of our estimator and evaluate its finite-sample performance.

翻译：许多试验设计在随机化后于预设时间点或附近收集结局。实践中，参与者的实际评估时间可能呈现显著变异性。这种不规则评估时间给学习治疗效果带来挑战，因为并非所有参与者在感兴趣的时间点都有结局评估。此外，观察到的结局值可能无法代表给定时间点所有参与者的真实结局。当参与者在结局较好（或较差）时倾向于接受评估，或结局较好的参与者评估次数较多（或较少）时，便会产生所谓的信息性评估时间问题。现有方法虽能处理某些类型的信息性评估，但由于这些方法依赖不可检验的假设，因此需要进行敏感性分析。我们通过拓展现有加权方法，开发了一套敏感性分析方法。即使控制了研究早期观测到的变量，该方法仍能考虑如下可能性：给定时间点结局较差的参与者相较于其他参与者，在该时间点接受评估的可能性更大（或更小）。我们将该方法应用于一项针对未受控制哮喘低收入患者的随机试验。我们详细阐述了基于影响函数的估计过程实现，推导了估计量的大样本分布，并评估了其有限样本性能。