Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets

• Published in: Statistics in medicine Vol. 34; no. 1; pp. 106 - 117
• Main Authors: Gruber, Susan, Logan, Roger W., Jarrín, Inmaculada, Monge, Susana, Hernán, Miguel A.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 15.01.2015; Wiley Subscription Services, Inc
• Subjects: Algorithms; Antiretroviral Therapy, Highly Active - statistics & numerical data; Artificial intelligence; Bias; Computer Simulation; Confidence Intervals; Confounding Factors (Epidemiology); Data Interpretation, Statistical; data-adaptive; ensemble learning; HIV; HIV Infections - drug therapy; HIV Infections - mortality; HIV Infections - prevention & control; Human immunodeficiency virus; Humans; inverse probability weighting; Logistic Models; longitudinal data; Machine Learning; marginal structural model; Medical statistics; Models, Statistical; Mortality; Mortality - trends; Probability; Regression analysis; Spain; super learning; Spain; ensemble learning; data-adaptive; inverse probability weighting; marginal structural model; super learning; longitudinal data
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6322

Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data‐adaptive procedure may be able to better exploit information available in measured covariates. By combining predictions from multiple algorithms, ensemble learning offers an alternative to logistic regression modeling to further reduce bias in estimated marginal structural model parameters. We describe the application of two ensemble learning approaches to estimating stabilized weights: super learning (SL), an ensemble machine learning approach that relies on V‐fold cross validation, and an ensemble learner (EL) that creates a single partition of the data into training and validation sets. Longitudinal data from two multicenter cohort studies in Spain (CoRIS and CoRIS‐MD) were analyzed to estimate the mortality hazard ratio for initiation versus no initiation of combined antiretroviral therapy among HIV positive subjects. Both ensemble approaches produced hazard ratio estimates further away from the null, and with tighter confidence intervals, than logistic regression modeling. Computation time for EL was less than half that of SL. We conclude that ensemble learning using a library of diverse candidate algorithms offers an alternative to parametric modeling of inverse probability weights when fitting marginal structural models. With large datasets, EL provides a rich search over the solution space in less time than SL with comparable results. Copyright © 2014 John Wiley & Sons, Ltd.