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

基本信息

  • 批准号:
    10584971
  • 负责人:
  • 金额:
    $ 86.66万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 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

项目摘要

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
项目总结

项目成果

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Marcela V Horvitz-Lennon其他文献

Marcela V Horvitz-Lennon的其他文献

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{{ truncateString('Marcela V Horvitz-Lennon', 18)}}的其他基金

Improving Minority Health by Monitoring Medicaid Quality, Disparities and Value
通过监测医疗补助质量、差异和价值来改善少数群体的健康
  • 批准号:
    9911999
  • 财政年份:
    2017
  • 资助金额:
    $ 86.66万
  • 项目类别:
Improving Minority Health by Monitoring Medicaid Quality, Disparities and Value
通过监测医疗补助质量、差异和价值来改善少数群体的健康
  • 批准号:
    10169888
  • 财政年份:
    2017
  • 资助金额:
    $ 86.66万
  • 项目类别:
Improving Value of Publicly Funded Mental Health Care
提高公共资助的精神卫生保健的价值
  • 批准号:
    9275022
  • 财政年份:
    2016
  • 资助金额:
    $ 86.66万
  • 项目类别:
Improving Value of Publicly Funded Mental Health Care
提高公共资助的精神卫生保健的价值
  • 批准号:
    9027083
  • 财政年份:
    2016
  • 资助金额:
    $ 86.66万
  • 项目类别:
An In-Depth Investigation of Racial & Ethnic Disparities in Schizophrenia Care
对种族的深入调查
  • 批准号:
    8139114
  • 财政年份:
    2010
  • 资助金额:
    $ 86.66万
  • 项目类别:
An In-Depth Investigation of Racial & Ethnic Disparities in Schizophrenia Care
对种族的深入调查
  • 批准号:
    7982863
  • 财政年份:
    2010
  • 资助金额:
    $ 86.66万
  • 项目类别:
An In-Depth Investigation of Racial & Ethnic Disparities in Schizophrenia Care
对种族的深入调查
  • 批准号:
    8301733
  • 财政年份:
    2010
  • 资助金额:
    $ 86.66万
  • 项目类别:
An In-Depth Investigation of Racial & Ethnic Disparities in Schizophrenia Care
对种族的深入调查
  • 批准号:
    8519860
  • 财政年份:
    2010
  • 资助金额:
    $ 86.66万
  • 项目类别:

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结构性种族主义、药房关闭以及老年人医疗保险 D 部分受益人的药物依从性差异
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  • 财政年份:
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利用技术提高青少年和年轻肾移植或肝移植受者的药物依从性
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Mhealth 促进年轻 MSM 遵守暴露前预防
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