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Collaborative-contr...
Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data
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- Ju, Cheng (författare)
- Division of Biostatistics, University of California, USA
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- Wyss, Richard (författare)
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
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- Franklin, Jessica M (författare)
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
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- Schneeweiss, Sebastian (författare)
- Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA
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- Häggström, Jenny (författare)
- Umeå universitet,Statistik,Stat4Reg
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- van der Laan, Mark J (författare)
- Division of Biostatistics, University of California, USA
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(creator_code:org_t)
- 2017-12-11
- 2019
- Engelska.
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Ingår i: Statistical Methods in Medical Research. - : Sage Publications. - 0962-2802 .- 1477-0334. ; 28:4, s. 1044-1063
- Relaterad länk:
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https://europepmc.or...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Propensity score-based estimators are increasingly used for causal inference in observational studies. However, model selection for propensity score estimation in high-dimensional data has received little attention. In these settings, propensity score models have traditionally been selected based on the goodness-of-fit for the treatment mechanism itself, without consideration of the causal parameter of interest. Collaborative minimum loss-based estimation is a novel methodology for causal inference that takes into account information on the causal parameter of interest when selecting a propensity score model. This “collaborative learning” considers variable associations with both treatment and outcome when selecting a propensity score model in order to minimize a bias-variance tradeoff in the estimated treatment effect. In this study, we introduce a novel approach for collaborative model selection when using the LASSO estimator for propensity score estimation in high-dimensional covariate settings. To demonstrate the importance of selecting the propensity score model collaboratively, we designed quasi-experiments based on a real electronic healthcare database, where only the potential outcomes were manually generated, and the treatment and baseline covariates remained unchanged. Results showed that the collaborative minimum loss-based estimation algorithm outperformed other competing estimators for both point estimation and confidence interval coverage. In addition, the propensity score model selected by collaborative minimum loss-based estimation could be applied to other propensity score-based estimators, which also resulted in substantive improvement for both point estimation and confidence interval coverage. We illustrate the discussed concepts through an empirical example comparing the effects of non-selective nonsteroidal anti-inflammatory drugs with selective COX-2 inhibitors on gastrointestinal complications in a population of Medicare beneficiaries.
Ämnesord
- NATURVETENSKAP -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)
- NATURAL SCIENCES -- Mathematics -- Probability Theory and Statistics (hsv//eng)
Nyckelord
- Propensity score
- average treatment effect
- LASSO
- model selection
- electronic healthcare database
- collaborative targeted minimum loss-based estimation
- Statistics
- statistik
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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