Multiple Imputation to Account for Measurement Error in Marginal Structural Models. Issue 5 (September 2015)

• Record Type: Journal Article
• Authors: Edwards, Jessie K.; Cole, Stephen R.; Westreich, Daniel; Crane, Heidi; Eron, Joseph J.; Mathews, W. Christopher; Moore, Richard; Boswell, Stephen L.; Lesko, Catherine R.; Mugavero, Michael J.
• Is Part Of: Epidemiology. Volume 26:Issue 5(2015:Sep.)
• Publication Date: 2015-09
• Subjects: Epidemiology -- Periodicals; Epidemiology -- Environmental aspects -- Periodicals; Epidemiology -- Periodicals; 614.405
• DOI: 10.1097/EDE.0000000000000330
• Languages: English
• ISSNs: 1044-3983

Abstract

Abstract : 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. Abstract : Supplemental Digital Content is available in the text.