Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations.
Janie CoulombeShu YangPublished in: Biometrics (2024)
Electronic health records and other sources of observational data are increasingly used for drawing causal inferences. The estimation of a causal effect using these data not meant for research purposes is subject to confounding and irregularly-spaced covariate-driven observation times affecting the inference. A doubly-weighted estimator accounting for these features has previously been proposed that relies on the correct specification of two nuisance models used for the weights. In this work, we propose a novel consistent multiply robust estimator and demonstrate analytically and in comprehensive simulation studies that it is more flexible and more efficient than the only alternative estimator proposed for the same setting. It is further applied to data from the Add Health study in the United States to estimate the causal effect of therapy counseling on alcohol consumption in American adolescents.
Keyphrases
- electronic health record
- alcohol consumption
- clinical decision support
- big data
- healthcare
- adverse drug
- young adults
- magnetic resonance
- stem cells
- drinking water
- magnetic resonance imaging
- cross sectional
- data analysis
- bone marrow
- machine learning
- health information
- finite element
- human immunodeficiency virus
- artificial intelligence
- climate change
- smoking cessation