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

项目概要 描述接受常规护理治疗的精神分裂症老年人群的大型纵向数据集的可用性 环境提供了扩大关于抗精神病药物治疗结果的有限证据的机会 并了解哪些药物在现实世界中有效：哪些药物、以什么顺序、组合或 强度、针对谁（什么种族/族裔群体、在什么社会环境下）以及面临什么风险。虽然这 目标并不新鲜，机器学习和因果推理的进步可以改善推理，从而 生成证据来回答这些问题。利用日常护理环境中生成的数据，我们将 (a) 翻译新的统计方法，以确保使用高的观察到的混杂因素的分布平衡 多种竞争性抗精神病药物（多值治疗）的维度纵向数据以及 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; (c) 扩展基于树的方法，以同时对有效性和安全性结果进行建模 填补证据空白。我们将把种族/族裔多样化的公共保险老年人群体与 精神分裂症利用抗精神病药物来衡量社会背景因素的地理指标——上游社会 健康的决定因素 (SDH)，例如家庭收入和犯罪率——已知会影响健康 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开出的抗精神病药物 (a) 取决于 高维混杂因素和 (b) 可能会受到患者种族/民族和地区级别 SDH 的调节。目标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种族/民族和 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，并确定是否 种族/民族或 SDH 会改变治疗效果。目标 4 估计治疗方案对 利用患者内部的结果，同时实现每个个体的有效性和安全性结果 依赖关系。我们的建议具有很高的