Dynamic prediction of causal effects under different treatment regimes conditional on an individual's characteristics and longitudinal history is an essential problem in precision medicine. This is challenging in practice because outcomes and treatment assignment mechanisms are unknown in observational studies, an individual's treatment efficacy is a counterfactual, and the existence of selection bias is often unavoidable. We propose a Bayesian framework for identifying subgroup counterfactual benefits of dynamic treatment regimes by adapting Bayesian g-computation algorithm (J. Robins, 1986; Zhou, Elliott, & Little, 2019) to incorporate multivariate generalized linear mixed-effects models. Unmeasured time-invariant factors are identified as subject-specific random effects in the assumed joint distribution of outcomes, time-varying confounders, and treatment assignments. Existing methods mostly assume no unmeasured confounding and focus on balancing the observed confounder distributions between different treatments, while our method allows the presence of time-invariant unmeasured confounding. We propose a sequential ignorability assumption based on treatment assignment heterogeneity, which is analogous to balancing the latent tendency toward each treatment due to unmeasured time-invariant factors beyond the observables. We use simulation studies to assess the sensitivity of the proposed method's performance to various model assumptions. The method is applied to observational clinical data to investigate the efficacy of continuously using mycophenolate in different subgroups of scleroderma patients who were treated with the drug.

翻译：在精准医学中，根据个体特征和纵向历史动态预测不同治疗方案下的因果效应是一个关键问题。这在实践中具有挑战性，因为观察性研究中的结果和治疗分配机制未知，个体的治疗效果是反事实的，且选择偏差的存在往往难以避免。我们提出一个贝叶斯框架，通过改编贝叶斯g计算算法（J. Robins, 1986；Zhou, Elliott, & Little, 2019）以纳入多元广义线性混合效应模型，从而识别动态治疗方案的亚组反事实获益。未被测量的时不变因素被识别为结果、时变混杂变量和治疗分配联合分布假设中受试者特定的随机效应。现有方法大多假设不存在未测量的混杂，并侧重于平衡不同治疗组间可观测混杂变量的分布，而我们的方法允许存在时不变未测量混杂。我们基于治疗分配异质性提出一种序贯可忽略性假设，该假设类似于平衡由未测量时不变因素（超出可观测变量范围）导致的潜在治疗倾向。我们通过模拟研究评估所提方法对各种模型假设的敏感性。该方法应用于观察性临床数据，以评估持续使用霉酚酸酯对接受该药物治疗的硬皮病患者不同亚组的疗效。