期刊
LIFETIME DATA ANALYSIS
卷 28, 期 3, 页码 512-542出版社
SPRINGER
DOI: 10.1007/s10985-022-09554-8
关键词
Data aggregation; Distributed regression; Dynamic weighted survival modelling; Effect modification; Precision medicine; Selective serotonin reuptake inhibitors
资金
- Canada Research Chair (Tier 1) in Statistical Methods for Precision Medicine
- Natural Sciences and Engineering Research Council (NSERC) of Canada
- Fonds de recherche du Quebec-Sante
- National Institute of Mental Health of the National Institutes of Health [R01 MH114873]
- Canadian Institutes of Health Research grant [CIHR TD3-137716]
- Natural Sciences and Engineering Research Council of Canada [NSERC 228203]
This study discusses the estimation of individualized treatment rules for depression treatment and privacy protection, using distributed regression and dynamic weighted survival modeling to blur individual-level data and address the challenges of data privacy and small treatment effect heterogeneity.
Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.
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