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Stable inverse probability weighting estimation for longitudinal studies
Author(s) -
Avagyan Vahe,
Vansteelandt Stijn
Publication year - 2021
Publication title -
scandinavian journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.359
H-Index - 65
eISSN - 1467-9469
pISSN - 0303-6898
DOI - 10.1111/sjos.12542
Subject(s) - inverse probability weighting , propensity score matching , covariate , mathematics , estimator , statistics , average treatment effect , marginal structural model , weighting , econometrics , inverse probability , counterfactual thinking , outcome (game theory) , estimating equations , point estimation , confounding , bayesian probability , medicine , posterior probability , mathematical economics , radiology , philosophy , epistemology
We consider estimation of the average effect of time‐varying dichotomous exposure on outcome using inverse probability weighting (IPW) under the assumption that there is no unmeasured confounding of the exposure–outcome association at each time point. Despite the popularity of IPW, its performance is often poor due to instability of the estimated weights. We develop an estimating equation‐based strategy for the nuisance parameters indexing the weights at each time point, aimed at preventing highly volatile weights and ensuring the stability of IPW estimation. Our proposed approach targets the estimation of the counterfactual mean under a chosen treatment regime and requires fitting a separate propensity score model at each time point. We discuss and examine extensions to enable the fitting of marginal structural models using one propensity score model across all time points. Extensive simulation studies demonstrate adequate performance of our approach compared with the maximum likelihood propensity score estimator and the covariate balancing propensity score estimator.