Shen Junwei, Moodie Erica E M, Golchi Shirin
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec H3A 1G1, Canada.
Biometrics. 2025 Jul 3;81(3). doi: 10.1093/biomtc/ujaf082.
Individualized treatment rules tailor treatments to patients based on clinical, demographic, and other characteristics. Estimation of individualized treatment rules requires the identification of individuals who benefit most from the particular treatments and thus the detection of variability in treatment effects. To develop an effective individualized treatment rule, data from multisite studies may be required due to the low power provided by smaller datasets for detecting the often small treatment-covariate interactions. However, sharing of individual-level data is sometimes constrained. Furthermore, sparsity may arise in 2 senses: different data sites may recruit from different populations, making it infeasible to estimate identical models or all parameters of interest at all sites, and the number of non-zero parameters in the model for the treatment rule may be small. To address these issues, we adopt a 2-stage Bayesian meta-analysis approach to estimate individualized treatment rules which optimize expected patient outcomes using multisite data without disclosing individual-level data beyond the sites. Simulation results demonstrate that our approach can provide consistent estimates of the parameters which fully characterize the optimal individualized treatment rule. We estimate the optimal Warfarin dose strategy using data from the International Warfarin Pharmacogenetics Consortium, where data sparsity and small treatment-covariate interaction effects pose additional statistical challenges.
个体化治疗规则根据临床、人口统计学和其他特征为患者量身定制治疗方案。估计个体化治疗规则需要识别出从特定治疗中获益最大的个体，从而检测治疗效果的变异性。为了制定有效的个体化治疗规则，由于较小数据集检测通常较小的治疗-协变量相互作用的能力较低，可能需要多中心研究的数据。然而，个体层面数据的共享有时会受到限制。此外，稀疏性可能在两个方面出现：不同的数据站点可能从不同的人群中招募，使得在所有站点估计相同的模型或所有感兴趣的参数变得不可行，并且治疗规则模型中的非零参数数量可能很少。为了解决这些问题，我们采用两阶段贝叶斯荟萃分析方法来估计个体化治疗规则，该方法使用多中心数据优化预期患者结局，而无需在各站点之外披露个体层面的数据。模拟结果表明，我们的方法可以提供对完全表征最优个体化治疗规则的参数的一致估计。我们使用来自国际华法林药物遗传学联盟的数据估计最优华法林剂量策略，其中数据稀疏性和较小的治疗-协变量相互作用效应带来了额外的统计挑战。
