Estimating the effects of multiple time-varying exposures using joint marginal structural models: alcohol consumption, injection drug use, and HIV acquisition

By C. Howe, Stephen R. Cole, S. Mehta, G. Kirk. 2012

41 citations recorded by the source corpus.

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.

Subfields: Epidemiology, Statistics and Probability

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Citations in the corpus

Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders (2019, methodology): “The implementation of this outcome model is similar to the one in (Hernán et al., 2001; Howe et al., 2012).”
Estimating Counterfactual Treatment Outcomes over Time Through Adversarially Balanced Representations (2020, methodology): “We adopt the implementation in (Hernán et al., 2001; Howe et al., 2012; Lim et al., 2018) for MSMs and use logistic regression for estimating the propensity weights as follows: where ω (cid:63) , φ (cid:63) and w (cid:63) are regression coefﬁcients, k ∈ { c, d indicates the chemotherapy or…”
A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures (2019, unknown): “While several methods from the causal inference literature are capable of estimating the e ﬀ ect of a whole mixture (Howe et al. 2012; Kang and Schafer 2007; Keil et al. 2018a; Robins et al. 2004), WQS regression has the advantage over these methods of a simple implementation.”