Abstract We develop a semiparametric Bayesian approach to missing outcome data in longitudinal studies in the presence of auxiliary covariates. We consider a joint model for the full data response, missingness, and auxiliary covariates. We include auxiliary covariates to “move” the missingness “closer” to missing at random. In particular, we specify a semiparametric Bayesian model for the observed data via Gaussian process priors and Bayesian additive regression trees. These model specifications allow us to capture nonlinear and nonadditive effects, in contrast to existing parametric methods. We then separately specify the conditional distribution of the missing data response given the observed data response, missingness, and auxiliary covariates (i.e., the extrapolation distribution) using identifying restrictions. We introduce meaningful sensitivity parameters that allow for a simple sensitivity analysis. Informative priors on those sensitivity parameters can be elicited from subject-matter experts. We use Monte Carlo integration to compute the full data estimands. Performance of our approach is assessed using simulated datasets. Our methodology is motivated by, and applied to, data from a clinical trial on treatments for schizophrenia. Supplementary materials for this article are available online.

中文翻译：

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使用辅助协变量的纵向研究中辍学的半参数贝叶斯方法

摘要 我们开发了一种半参数贝叶斯方法，用于在存在辅助协变量的情况下在纵向研究中缺失结果数据。我们考虑完整数据响应、缺失和辅助协变量的联合模型。我们包括辅助协变量以将缺失“移动”到“更接近”随机缺失。特别是，我们通过高斯过程先验和贝叶斯加性回归树为观测数据指定了一个半参数贝叶斯模型。与现有的参数方法相比，这些模型规范使我们能够捕捉非线性和非加性效应。然后，我们使用识别限制分别指定给定观察数据响应、缺失和辅助协变量（即外推分布）的缺失数据响应的条件分布。我们引入了有意义的灵敏度参数，允许进行简单的灵敏度分析。可以从主题专家那里获得关于这些敏感性参数的信息先验。我们使用 Monte Carlo 积分来计算完整的数据估计量。我们的方法的性能是使用模拟数据集评估的。我们的方法论受到精神分裂症治疗临床试验数据的启发并应用于这些数据。本文的补充材料可在线获取。来自精神分裂症治疗临床试验的数据。本文的补充材料可在线获取。来自精神分裂症治疗临床试验的数据。本文的补充材料可在线获取。