title: Estimating inverse probability weights using super learner when weight-model specification is unknown in a marginal structural Cox model context.
abstract: Correct specification of the inverse probability weighting (IPW) model is necessary for consistent inference from a marginal structural Cox model (MSCM). In practical applications, researchers are typically unaware of the true specification of the weight model. Nonetheless, IPWs are commonly estimated using parametric models, such as the main-effects logistic regression model. In practice, assumptions underlying such models may not hold and data-adaptive statistical learning methods may provide an alternative. Many candidate statistical learning approaches are available in the literature. However, the optimal approach for a given dataset is impossible to predict. Super learner (SL) has been proposed as a tool for selecting an optimal learner from a set of candidates using cross-validation. In this study, we evaluate the usefulness of a SL in estimating IPW in four different MSCM simulation scenarios, in which we varied the specification of the true weight model specification (linear and/or additive). Our simulations show that, in the presence of weight model misspecification, with a rich and diverse set of candidate algorithms, SL can generally offer a better alternative to the commonly used statistical learning approaches in terms of MSE as well as the coverage probabilities of the estimated effect in an MSCM. The findings from the simulation studies guided the application of the MSCM in a multiple sclerosis cohort from British Columbia, Canada (1995-2008), to estimate the impact of beta-interferon treatment in delaying disability progression. Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Centre for Health Evaluation and Outcome Sciences (CHÉOS), St. Pauls Hospital, Vancouver, BC, Canada.
authors.forename: Mohammad Ehsanul
authors.identifier: 0000-0002-0346-2871
authors.initials: ME
authors.lastname: Karim
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.|Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada.|Department of Pediatrics, McGill University, Montréal, QC, Canada.|Research Institute, McGill University Health Centre, Montréal, QC, Canada.
authors.forename: Robert W
authors.initials: RW
authors.lastname: Platt
authors.affiliation: 'The BeAMS Study, Long-term Benefits and Adverse Effects of Beta-interferon for Multiple Sclerosis': Shirani, A.; Zhao Y.; Evans C.; Kingwell E.; van der Kop M.L.; Oger J.; Gustafson, P; Petkau, J; Tremlett, H.
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D018450:Disease Progression; D006801:Humans; D016899:Interferon-beta / Q000627:therapeutic use; D016014:Linear Models; D016015:Logistic Models; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000188:drug therapy; D016016:Proportional Hazards Models; D016896:Treatment Outcome
chemical_list: D016899:Interferon-beta
keywords: inverse-probability weighting; marginal structural models; multiple sclerosis; super learner; time-dependent confounding