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Estimating the Effects of Multiple Time-varying Exposures Using Joint Marginal Structural Models: Alcohol Consumption, Injection Drug Use, and HIV Acquisition

Howe, Chanelle J.a; Cole, Stephen R.b; Mehta, Shruti H.c; Kirk, Gregory D.c,d

Epidemiology:
doi: 10.1097/EDE.0b013e31824d1ccb
Methods
Abstract

The joint effects of multiple exposures on an outcome are frequently of interest in epidemiologic research. In 2001, Hernán et al (J Am Stat Assoc. 2001;96:440–448) presented methods for estimating the joint effects of multiple time-varying exposures subject to time-varying confounding affected by prior exposure using joint marginal structural models. Nonetheless, the use of these joint models is rare in the applied literature. Minimal uptake of these joint models, in contrast to the now widely used standard marginal structural model, is due in part to a lack of examples demonstrating the method. In this paper, we review the assumptions necessary for unbiased estimation of joint effects as well as the distinction between interaction and effect measure modification. We demonstrate the use of marginal structural models for estimating the joint effects of alcohol consumption and injection drug use on HIV acquisition, using data from 1525 injection drug users in the AIDS Link to Intravenous Experience cohort study. In the joint model, the hazard ratio (HR) for heavy drinking in the absence of any drug injections was 1.58 (95% confidence interval = 0.67–3.73). The HR for any drug injections in the absence of heavy drinking was 1.78 (1.10–2.89). The HR for heavy drinking and any drug injections was 2.45 (1.45–4.12). The P values for multiplicative and additive interaction were 0.7620 and 0.9200, respectively, indicating a lack of departure from effects that multiply or add. We could not rule out interaction on either scale due to imprecision.

Author Information

From the a Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University Program in Public Health, Providence, RI; b Department of Epidemiology, University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC; c Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and d Division of Infectious Diseases, Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD.

Submitted 19 June 2011; accepted 13 December 2011; posted 10 April 2012.

Supported by National Institutes of Health grants R01-AA-01759, R01-DA-04334, R01-DA-12568.

The authors reported no other financial interests related to this research.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Chanelle J. Howe, Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University Program in Public Health, 121 South Main St, Providence, RI 02912. E-mail: chanelle_howe@brown.edu.

© 2012 Lippincott Williams & Wilkins, Inc.