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Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data
Division of Biostatistics, University of California, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Womens Hospital and Harvard Medical School, USA.
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2019 (English)In: Statistical Methods in Medical Research, ISSN 0962-2802, E-ISSN 1477-0334, Vol. 28, no 4, p. 1044-1063Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
Sage Publications, 2019. Vol. 28, no 4, p. 1044-1063
Keywords [en]
Propensity score, average treatment effect, LASSO, model selection, electronic healthcare database, collaborative targeted minimum loss-based estimation
National Category
Probability Theory and Statistics
Research subject
Statistics
Identifiers
URN: urn:nbn:se:umu:diva-142764DOI: 10.1177/0962280217744588ISI: 000463234000005PubMedID: 29226777OAI: oai:DiVA.org:umu-142764DiVA, id: diva2:1164605
Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2019-05-23Bibliographically approved

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Häggström, Jenny

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