Abstract
AbstractWe 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.
Highlights
Inverse probability weighting (IPW) has become very popular as a method to adjust statistical analyses for bias due to confounding or selection in cross-sectional and longitudinal observational studies
We demonstrate that, under a wide variety of settings, our proposed methodology performs well in terms of several statistical measures compared with the traditional maximum likelihood propensity score estimator (MLE), the covariate balancing propensity score (CBPS) estimator (Imai & Ratkovic, 2014), the stable balancing weights (SBW) estimator (Zubizarreta, 2015) and the adaptation of the calibration approach proposed by Han (2016)
We observe that for most of the considered settings, the confidence interval coverage obtained using the proposed SEAVE approach is closer to the nominal level than that calculated using MLE or CBPS methods
Summary
Inverse probability weighting (IPW) has become very popular as a method to adjust statistical analyses for bias due to confounding or selection in cross-sectional and longitudinal observational studies. We demonstrate that, under a wide variety of settings, our proposed methodology performs well in terms of several statistical measures compared with the traditional maximum likelihood propensity score estimator (MLE), the covariate balancing propensity score (CBPS) estimator (Imai & Ratkovic, 2014), the stable balancing weights (SBW) estimator (only for the fixed time period setting) (Zubizarreta, 2015) and the adaptation of the calibration approach proposed by Han (2016).
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