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

用于评估现实世界抗精神病药物治疗方案对老年精神分裂症患者的效果和效果异质性的稳健学习方法

• 批准号: 10584971
• 负责人: Marcela V Horvitz-Lennon
• 金额: $ 86.66万
• 依托单位: HARVARD MEDICAL SCHOOL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-12-01 至 2027-10-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584971
• 关键词: Acute; Adherence; Adult; Affect; Age; Antipsychotic Agents; Area; Benefits and Risks; Caring; Chronic; Chronic Disease; Clinical; Clinical Trials; Cognition Disorders; Complex; Crime; Data; Databases; Dependence; Diagnosis; Dose; Drug Prescriptions; Drug usage; Economics; Effectiveness; Elderly; Environment; Equilibrium; Ethnic Origin; Ethnic Population; Exposure to; Face; Financial cost; Geography; Health behavior; Heterogeneity; Household; Income; Individual; Insurance Carriers; Interruption; Learning; Link; Low income; Machine Learning; Maintenance; Medical; Medicare; Medicare/Medicaid; Methods; Modeling; National Institute of Mental Health; Outcome; Outcome Assessment; Patient-Focused Outcomes; Patients; Persons; Pharmaceutical Preparations; Pharmacotherapy; Phase; Population; Prevalence; Probability; Race; Regimen; Research Personnel; Risk; Role; Safety; Schizophrenia; Social Environment; Statistical Methods; Strategic Planning; Subgroup; Time; Translating; Treatment Effectiveness; Treatment Protocols; Treatment outcome; Trees; Variant; Vulnerable Populations; Work; adverse outcome; age group; aged; beneficiary; burden of illness; cohort; common treatment; comorbidity; contextual factors; cost; disability; disability-adjusted life years; disparity reduction; effectiveness outcome; efficacy trial; ethnic diversity; ethnic minority population; health care availability; high dimensionality; human old age (65+); improved; indexing; interest; longitudinal database; longitudinal dataset; medication compliance; novel; older patient; outcome disparities; public health relevance; racial diversity; racial minority population; racial population; randomized trial; safety outcomes; semiparametric; social; social disparities; social health determinants; treatment adherence; treatment as usual; treatment comparison; treatment disparity; treatment effect; treatment pattern; treatment risk

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

项目摘要 大型纵向数据集的可用性描述了在通常护理中治疗精神分裂症的古老人群 设置有机会扩大有关抗精神病药治疗结果的有限证据的机会 这个人群并了解现实世界中有效的方法：哪些毒品，结合或 强度，为谁（在什么社会环境下哪些种族/族裔群体），以及有什么风险。同时 目标并不是什么新鲜事物，机器学习的进步和因果推断可以改善推断，从而提高了推论，从而 生成证据回答这些问题。利用在通常的护理设置中生成的数据，我们将（a） 翻译新的统计方法，以确保使用高级混杂因素的分布平衡 具有多种竞争抗精神病药（多价处理）的维度纵向数据和 纵向治疗模式（治疗方案）； （b）利用强大的非参数或半参数 方法; （c）将基于树的方法扩展到简单地建模有效性和安全结果 填补证据空白。我们将把大约/种族多样的古老的公共保险成年人群联系起来 精神分裂症使用抗精神病药对社会背景因素的地理指标 - 上游社会 确定健康（SDH），例如家庭收入和犯罪率，已知会影响 治疗依从性和其他健康行为。 AIM 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的抗精神病药药 高维混杂因素和（b）可能会因患者种族/种族和地区级别的SDH而主持。目标2 识别和表征经常观察到的治疗方案，这些治疗方案可能因种族/种族和SDH而异。 AIM 3估计AIM 2中确定的治疗方案的有效性和安全性，并确定是否是否 种族/民族或SDH修改治疗效果。 AIM 4估计治疗方案对 每个人的有效性和安全性结果，都可以利用患者的结果 依赖性。我们的建议很高