| 来源类型 | Publication |
| Optimal Matching Approaches in Health Policy Evaluations Under Rolling Enrolment | |
| Samuel D. Pimentel; Lauren Vollmer Forrow; Jonathan Gellar; and Jiaqi Li | |
| 发表日期 | 2019-10-07 |
| 出版者 | Journal of the Royal Statistical Society: Series A (online ahead of print, subscription required) |
| 出版年 | 2019 |
| 语种 | 英语 |
| 概述 | Comparison group selection is paramount for health policy evaluations, where randomization is seldom practicable. Rolling enrolment is common in these evaluations, introducing challenges for comparison group selection and inference.", |
| 摘要 | Comparison group selection is paramount for health policy evaluations, where randomization is seldom practicable. Rolling enrolment is common in these evaluations, introducing challenges for comparison group selection and inference. We propose a novel framework, GroupMatch, for comparison group selection under rolling enrolment, founded on the notion of time agnosticism: two subjects with similar outcome trajectories but different enrolment periods may be more prognostically similar and produce better inference if matched, than two subjects with the same enrolment period but different pre‐enrolment trajectories. We articulate the conceptual advantages of this framework and demonstrate its efficacy in a simulation study and in an application to a study of the effect of falls in Medicare Advantage patients. |
| URL | https://www.mathematica.org/our-publications-and-findings/publications/optimal-matching-approaches-in-health-policy-evaluations-under-rolling-enrolment |
| 来源智库 | Mathematica Policy Research (United States) |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/489738 |
| 推荐引用方式 GB/T 7714 | Samuel D. Pimentel,Lauren Vollmer Forrow,Jonathan Gellar,et al. Optimal Matching Approaches in Health Policy Evaluations Under Rolling Enrolment. 2019. |
| 条目包含的文件 | 条目无相关文件。 | |||||
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