The Effect of Error-in-Confounders on the Estimation of the Causal Parameter When Using Marginal Structural Models and Inverse Probability-of-Treatment Weights: A Simulation Study

Regier Michael D. (), Moodie Erica E. M. () and Platt Robert W. ()
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Regier Michael D.: West Virginia University, Morgantown, WV, USA
Moodie Erica E. M.: McGill University, Montréal, QC, Canada
Platt Robert W.: McGill University, Montréal, QC, Canada

The International Journal of Biostatistics, 2014, vol. 10, issue 1, 1-15

Abstract: We performed an empirical study to evaluate the effect of mismeasured continuous confounders on the estimation of the causal parameter when using marginal structural models and inverse probability-of-treatment weighting. By executing an extensive simulation using 500 randomly generated parameter value combinations within a defined space, we observed the well-understood effects of attenuation and augmentation, and two unanticipated effects: null effects and sign reversals. We implemented a secondary empirical study to further investigate the sign reversal effect. We use the results of our study to identify conceptual similarities between the analytic and empirical results for multivariable linear and logistic regression, and our empirical results. Through this synthesis, we have been able to suggest feasible directions of research as well as outline the form of expected results.

Keywords: causal inference; censored data; inverse probability weighting; marginal structural model; measurement error (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (1)

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DOI: 10.1515/ijb-2012-0039

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