Multiple Imputation to Account for Measurement Error in Marginal Structural Models

Jessie K. Edwards, Stephen R. Cole, Daniel Westreich, Heidi Crane, Joseph J. Eron, W. Christopher Mathews, Richard Moore, Stephen L. Boswell, Catherine R. Lesko, Michael J. Mugavero

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Background: Marginal structural models are an important tool for observational studies. These models typically assume that variables are measured without error. We describe a method to account for differential and nondifferential measurement error in a marginal structural model. Methods: We illustrate the method estimating the joint effects of antiretroviral therapy initiation and current smoking on all-cause mortality in a United States cohort of 12,290 patients with HIV followed for up to 5 years between 1998 and 2011. Smoking status was likely measured with error, but a subset of 3,686 patients who reported smoking status on separate questionnaires composed an internal validation subgroup. We compared a standard joint marginal structural model fit using inverse probability weights to a model that also accounted for misclassification of smoking status using multiple imputation. Results: In the standard analysis, current smoking was not associated with increased risk of mortality. After accounting for misclassification, current smoking without therapy was associated with increased mortality (hazard ratio [HR]: 1.2 [95% confidence interval [CI] = 0.6, 2.3]). The HR for current smoking and therapy [0.4 (95% CI = 0.2, 0.7)] was similar to the HR for no smoking and therapy (0.4; 95% CI = 0.2, 0.6). Conclusions: Multiple imputation can be used to account for measurement error in concert with methods for causal inference to strengthen results from observational studies.

Original languageEnglish (US)
Pages (from-to)645-652
Number of pages8
Issue number5
StatePublished - Sep 25 2015

ASJC Scopus subject areas

  • Epidemiology


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