Robust Learning Approaches for Assessing Effects and Effect Heterogeneity of Real World Antipsychotic Treatment Regimes in Elderly Persons with Schizophrenia

项目摘要 描述接受常规治疗的精神分裂症老年人群的大型纵向数据集的可用性 环境提供了机会，以扩大有限的证据，抗精神病药物治疗的结果， 这一人群，并了解什么工作在真实的世界：哪些药物，在什么顺序，组合，或 强度，为谁（什么种族/民族群体，在什么社会环境下），以及在什么风险。虽然这 目标并不是新的，机器学习和因果推理的进步可以改善推理， 来回答这些问题。利用在日常护理环境中产生的数据，我们将（a） 翻译新的统计方法，以确保使用高 多种竞争性抗精神病药物（多值治疗）的多维纵向数据， 纵向治疗模式（治疗方案）;（B）利用稳健的非参数或半参数 方法;以及（c）扩展基于树的方法，以同时建模有效性和安全性结果， 填补证据空白我们将把不同种族/民族的老年公共保险成年人群体与 精神分裂症使用抗精神病药物的社会背景因素的地理指标-上游社会 健康的决定因素（SDH），如家庭收入和犯罪率-这是众所周知的影响 治疗依从性和其他健康行为。目标1应用指数的因果效应估计 使用加权半参数或非参数方法处方的抗精神病药物，（a）取决于 高维混杂因素和（B）可能受患者种族/民族和地区水平SDH的调节。目的2 识别和描述经常观察到的治疗方案，这些治疗方案可能因人种/种族和SDH而异。 目标3评估目标2中确定的治疗方案的有效性和安全性，并确定 人种/种族或SDH改变治疗效果。目的4估计治疗方案对 利用患者内结局，同时评估每个个体的有效性和安全性结局 个依赖项我们的建议很高

Project Summary Availability of large longitudinal datasets describing elderly populations with schizophrenia treated in usual care settings present opportunities to expand the limited evidence on outcomes of antipsychotic drug treatment for this population and to learn what works in the real world: which drugs, in what sequence, combination, or intensity, for whom (what racial/ethnic groups, in what social circumstances), and at what risk. While this objective is not new, advances in machine learning and causal inference could improve inferences, and thus generate evidence to answer these questions. Leveraging data generated in usual care settings, we will (a) translate novel statistical methods to assure distributional balance on observed confounders using high- dimensional longitudinal data with multiple competing antipsychotic drugs (multi-valued treatments) and longitudinal treatment patterns (treatment regimens); (b) utilize robust non-parametric or semi-parametric methods; and (c) extend tree-based approaches to simultaneously model effectiveness and safety outcomes to fill evidence gaps. We will link racially/ethnically diverse cohorts of elderly publicly-insured adults with schizophrenia utilizing antipsychotics to geographical indicators of social contextual factors– upstream social determinants of health (SDH) such as household income and crime rates— that are known to influence treatment adherence and other health behaviors. Aim 1 applies causal effect estimation of the index antipsychotic drug prescribed using weighted semi-parametric or non-parametric methods that (a) depend on high-dimensional confounders and (b) may be moderated by patient race/ethnicity and area-level SDH. Aim 2 identifies and characterizes frequently observed treatment regimens that may differ by race/ethnicity and SDH. Aim 3 estimates effectiveness and safety of the treatment regimens identifed in Aim 2, and determines if race/ethnicity or SDH modify treatment effectiveness. Aim 4 estimates the impact of treatment regimens on each individual effectiveness and safety outcome simultaneously, making use of within-patient outcome dependencies. Our proposal has high