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

ABSTRACT: 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

SUBMITTER: Gruber S 

PROVIDER: S-EPMC4262745 | biostudies-literature | 2015 Jan

REPOSITORIES: biostudies-literature