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Open Access Research article

Using imputed pre-treatment cholesterol in a propensity score model to reduce confounding by indication: results from the multi-ethnic study of atherosclerosis

Neal W Jorgensen1*, Christopher T Sibley2 and Robyn L McClelland1

Author Affiliations

1 Department of Biostatistics, University of Washington, Seattle, WA, USA

2 National Institutes of Health Clinical Center, Bethesda, MD, USA

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BMC Medical Research Methodology 2013, 13:81  doi:10.1186/1471-2288-13-81

Published: 21 June 2013

Abstract

Background

Studying the effects of medications on endpoints in an observational setting is an important yet challenging problem due to confounding by indication. The purpose of this study is to describe methodology for estimating such effects while including prevalent medication users. These techniques are illustrated in models relating statin use to cardiovascular disease (CVD) in a large multi-ethnic cohort study.

Methods

The Multi-Ethnic Study of Atherosclerosis (MESA) includes 6814 participants aged 45-84 years free of CVD. Confounding by indication was mitigated using a two step approach: First, the untreated values of cholesterol were treated as missing data and the values imputed as a function of the observed treated value, dose and type of medication, and participant characteristics. Second, we construct a propensity-score modeling the probability of medication initiation as a function of measured covariates and estimated pre-treatment cholesterol value. The effect of statins on CVD endpoints were assessed using weighted Cox proportional hazard models using inverse probability weights based on the propensity score.

Results

Based on a meta-analysis of randomized controlled trials (RCT) statins are associated with a reduced risk of CVD (relative risk ratio = 0.73, 95% CI: 0.70, 0.77). In an unweighted Cox model adjusting for traditional risk factors we observed little association of statins with CVD (hazard ratio (HR) = 0.97, 95% CI: 0.60, 1.59). Using weights based on a propensity model for statins that did not include the estimated pre-treatment cholesterol we observed a slight protective association (HR = 0.92, 95% CI: 0.54-1.57). Results were similar using a new-user design where prevalent users of statins are excluded (HR = 0.91, 95% CI: 0.45-1.80). Using weights based on a propensity model with estimated pre-treatment cholesterol the effects of statins (HR = 0.74, 95% CI: 0.38, 1.42) were consistent with the RCT literature.

Conclusions

The imputation of pre-treated cholesterol levels for participants on medication at baseline in conjunction with a propensity score yielded estimates that were consistent with the RCT literature. These techniques could be useful in any example where inclusion of participants exposed at baseline in the analysis is desirable, and reasonable estimates of pre-exposure biomarker values can be estimated.

Keywords:
Multiple imputation; Confounding by indication; Propensity score; Inverse probability of treatment weights; Statins