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Data-adaptive longitudinal model selection in causal inference with collaborative targeted minimum loss-based estimation.
- Source :
-
Biometrics [Biometrics] 2020 Mar; Vol. 76 (1), pp. 145-157. Date of Electronic Publication: 2019 Nov 06. - Publication Year :
- 2020
-
Abstract
- Causal inference methods have been developed for longitudinal observational study designs where confounding is thought to occur over time. In particular, one may estimate and contrast the population mean counterfactual outcome under specific exposure patterns. In such contexts, confounders of the longitudinal treatment-outcome association are generally identified using domain-specific knowledge. However, this may leave an analyst with a large set of potential confounders that may hinder estimation. Previous approaches to data-adaptive model selection for this type of causal parameter were limited to the single time-point setting. We develop a longitudinal extension of a collaborative targeted minimum loss-based estimation (C-TMLE) algorithm that can be applied to perform variable selection in the models for the probability of treatment with the goal of improving the estimation of the population mean counterfactual outcome under a fixed exposure pattern. We investigate the properties of this method through a simulation study, comparing it to G-Computation and inverse probability of treatment weighting. We then apply the method in a real-data example to evaluate the safety of trimester-specific exposure to inhaled corticosteroids during pregnancy in women with mild asthma. The data for this study were obtained from the linkage of electronic health databases in the province of Quebec, Canada. The C-TMLE covariate selection approach allowed for a reduction of the set of potential confounders, which included baseline and longitudinal variables.<br /> (© 2019 The International Biometric Society.)
- Subjects :
- Adrenal Cortex Hormones administration & dosage
Asthma complications
Asthma drug therapy
Causality
Cohort Studies
Computer Simulation
Data Interpretation, Statistical
Databases, Factual statistics & numerical data
Female
Humans
Longitudinal Studies
Pregnancy
Pregnancy Complications drug therapy
Treatment Outcome
Algorithms
Biometry methods
Models, Statistical
Subjects
Details
- Language :
- English
- ISSN :
- 1541-0420
- Volume :
- 76
- Issue :
- 1
- Database :
- MEDLINE
- Journal :
- Biometrics
- Publication Type :
- Academic Journal
- Accession number :
- 31397506
- Full Text :
- https://doi.org/10.1111/biom.13135