Smith Bonnie B, Gao Yujing, Yang Shu, Varadhan Ravi, Apter Andrea J, Scharfstein Daniel O
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, United States.
Department of Statistics, North Carolina State University, Raleigh, NC 27695, United States.
Biometrics. 2024 Oct 3;80(4). doi: 10.1093/biomtc/ujae154.
Many trials are designed to collect outcomes at or around pre-specified times after randomization. If there is variability in the times when participants are actually assessed, this can 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. Methods have been developed that account for some types of such irregular and informative assessment times; however, since these methods rely on untestable assumptions, sensitivity analyses are needed. We develop a sensitivity analysis methodology that is benchmarked at the explainable assessment (EA) assumption, under which assessment and outcomes at each time are related only through data collected prior to that time. Our method uses an exponential tilting assumption, governed by a sensitivity analysis parameter, that posits deviations from the EA assumption. Our inferential strategy is based on a new influence function-based, augmented inverse intensity-weighted estimator. Our approach allows for flexible semiparametric modeling of the observed data, which is separated from specification of the sensitivity parameter. We apply our method to a randomized trial of low-income individuals with uncontrolled asthma, and we illustrate implementation of our estimation procedure in detail.
许多试验旨在收集随机分组后在预先指定时间或其前后的结果。如果参与者实际接受评估的时间存在变异性，这可能会对了解治疗效果构成挑战，因为并非所有参与者都在感兴趣的时间进行结果评估。此外，观察到的结果值可能无法代表给定时间所有参与者的结果。已经开发出一些方法来处理某些类型的此类不规则且信息丰富的评估时间；然而，由于这些方法依赖于无法检验的假设，因此需要进行敏感性分析。我们开发了一种敏感性分析方法，该方法以可解释评估（EA）假设为基准，在该假设下，每次的评估和结果仅通过该时间之前收集的数据相关联。我们的方法使用由敏感性分析参数控制的指数倾斜假设，该假设假定偏离EA假设。我们的推断策略基于一种新的基于影响函数的增强逆强度加权估计器。我们的方法允许对观察数据进行灵活的半参数建模，这与敏感性参数的设定是分开的。我们将我们的方法应用于一项针对未控制哮喘的低收入个体的随机试验，并详细说明了我们估计程序的实施情况。
