Collaborative-controlled LASSO for constructing propensity score-based estimators in high-dimensional data

• Published in: Statistical methods in medical research 2019-04, Vol.28 (4), p.1044-1063
• Main Authors: Ju, Cheng, Wyss, Richard, Franklin, Jessica M, Schneeweiss, Sebastian, Häggström, Jenny, van der Laan, Mark J
• Format: Article
• Language: English
• Subjects: Algorithms; average treatment effect; Beneficiaries; Bias; Collaboration; Collaborative learning; collaborative targeted minimum loss-based estimation; Confidence intervals; COX-2 inhibitors; electronic healthcare database; Estimators; Goodness of fit; Health care; Inference; LASSO; Mathematical models; Medicare; model selection; Nonsteroidal anti-inflammatory drugs; Observational studies; Parameter estimation; Propensity; Propensity score; Statistics; statistik
• ISSN: 0962-2802; 1477-0334; 1477-0334
• DOI: 10.1177/0962280217744588

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.