An optimal individualized treatment rule (ITR) is a function that takes a patient's characteristics, such as demographics, biomarkers, and treatment history, and outputs a treatment that is expected to give the best outcome for that patient. Major Depressive Disorder (MDD) is a common and disabling mental health condition for which an optimal ITR is of interest. Unfortunately, the power to detect treatment-covariate interactions in individual studies of MDD treatments is low. Additionally, all treatments of interest are not compared head-to-head in a single study. Network meta-analysis (NMA) is a method of synthesizing data from multiple studies to estimate the relative effects of a set of treatments. Recently, two-stage ITR NMA was proposed as a method to estimate ITRs that has the potential to improve power and simultaneously consider all relevant treatment options. In the first stage, study-specific ITRs are estimated, and in the second stage, they are pooled using a Bayesian NMA model. The existing approach is vulnerable to model misspecification and fails to address missing outcomes, which occur in the MDD data. We overcome these challenges by proposing Bayesian Bootstrap dynamic Weighted Ordinary Least Squares (BBdWOLS), a doubly-robust approach to ITR estimation that accounts for missing at random outcomes and naturally quantifies the uncertainty in estimation. We also propose an improvement to the NMA model that incorporates the full variance-covariance matrix of study-specific estimates. In a simulation study, we show that our fully Bayesian ITR NMA method is more robust and efficient than the existing approach. We apply our method to the motivating dataset consisting of three studies of pharmacological treatments for MDD, and explore how ITR NMA results can support personalized decision making in this context.

翻译：最优个体化治疗策略（ITR）是一种以患者人口统计学特征、生物标志物及治疗史等个体特征为输入，输出预期能为该患者带来最佳疗效的治疗方案。重度抑郁症（MDD）作为一种常见且致残的精神健康疾病，其最优ITR的构建具有重要临床意义。然而，在针对MDD治疗的独立研究中，检测治疗-协变量交互作用的统计功效普遍较低。此外，现有研究往往未能在单一试验中对所有关注的治疗方案进行头对头比较。网络荟萃分析（NMA）是一种整合多研究数据以评估一组治疗方案相对效应的统计方法。近期提出的两阶段ITR NMA方法，通过首阶段估计各研究特异性ITR、次阶段采用贝叶斯NMA模型进行合并，有望提升统计功效并同时考量所有相关治疗方案。但现有方法易受模型设定偏误影响，且未处理MDD数据中普遍存在的结局缺失问题。本研究通过提出贝叶斯自助动态加权普通最小二乘法（BBdWOLS）克服了这些局限——该双重稳健的ITR估计方法能够处理随机缺失结局，并自然量化估计过程中的不确定性。同时，我们改进了NMA模型，使其能够纳入研究特异性估计量的完整方差-协方差矩阵。模拟研究表明，我们提出的全贝叶斯ITR NMA方法较现有方法具有更强的稳健性与更高的统计效率。我们将该方法应用于包含三项MDD药物治疗研究的实证数据集，并探讨ITR NMA结果如何支持该临床情境下的个体化决策制定。