CL4 Marginal Structural Models Used in Estimating Cost-Effectiveness of Time-Varying Drug Therapy Using Adimistrative Databases. The Case of Statin in Secondary Prevention

Summary Robins (1998) introduced marginal structural models, a general class of counterfactual models for the joint effects of time-varying treatments in complex longitudinal studies subject to time-varying confounding. Robins (1998) established the identification of marginal structural model parameters under a sequential randomization assumption, which rules out unmeasured confounding of treatment assignment over time. The marginal structural Cox model is one of the most popular marginal structural models to evaluate the causal effect of time-varying treatments on a censored failure time outcome. In this paper, we establish sufficient conditions for identification of marginal structural Cox model parameters with the aid of a time-varying instrumental variable, when sequential randomization fails to hold due to unmeasured confounding. Our instrumental variable identification condition rules out any interaction between an unmeasured confounder and the instrumental variable in its additive effects on the treatment process, the longitudinal generalization of the identifying condition of Wang & Tchetgen Tchetgen (2018). We describe a large class of weighted estimating equations that give rise to consistent and asymptotically normal estimators of the marginal structural Cox model, thereby extending the standard inverse probability of treatment weighted estimation of marginal structural models to the instrumental variable setting. Our approach is illustrated via extensive simulation studies and an application to estimate the effect of community antiretroviral therapy coverage on HIV incidence.

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A graphical perspective of marginal structural models: An application for the estimation of the effect of physical activity on blood pressure

Statistical Methods in Medical Research ◽

10.1177/0962280216680834 ◽

2016 ◽

Vol 27 (8) ◽

pp. 2428-2436

Author(s):

Denis Talbot ◽

Amanda M Rossi ◽

Simon L Bacon ◽

Juli Atherton ◽

Geneviève Lefebvre

Keyword(s):

Physical Activity ◽

Blood Pressure ◽

Repeated Measures ◽

Structural Equation ◽

Structural Equation Models ◽

Causal Effect ◽

Structural Models ◽

Marginal Structural Models ◽

Time Varying ◽

Causal Graph

Estimating causal effects requires important prior subject-matter knowledge and, sometimes, sophisticated statistical tools. The latter is especially true when targeting the causal effect of a time-varying exposure in a longitudinal study. Marginal structural models are a relatively new class of causal models that effectively deal with the estimation of the effects of time-varying exposures. Marginal structural models have traditionally been embedded in the counterfactual framework to causal inference. In this paper, we use the causal graph framework to enhance the implementation of marginal structural models. We illustrate our approach using data from a prospective cohort study, the Honolulu Heart Program. These data consist of 8006 men at baseline. To illustrate our approach, we focused on the estimation of the causal effect of physical activity on blood pressure, which were measured at three time points. First, a causal graph is built to encompass prior knowledge. This graph is then validated and improved utilizing structural equation models. We estimated the aforementioned causal effect using marginal structural models for repeated measures and guided the implementation of the models with the causal graph. By employing the causal graph framework, we also show the validity of fitting conditional marginal structural models for repeated measures in the context implied by our data.

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Estimating controlled direct effects through marginal structural models

Political Science Research and Methods ◽

10.1017/psrm.2020.3 ◽

2020 ◽

Vol 8 (3) ◽

pp. 391-408

Author(s):

Michelle Torres

Keyword(s):

Regression Models ◽

Structural Models ◽

Marginal Structural Models ◽

Time Varying ◽

Direct Effects ◽

Treatment Control ◽

Regression Methods ◽

Confounding Bias ◽

Unbiased Estimates ◽

Future Outcomes

AbstractWhen working with panel data, many researchers wish to estimate the direct effects of time-varying factors on future outcomes. However, when a baseline treatment affects both the confounders of further stages of the treatment and the outcome, the estimation of controlled direct effects (CDEs) using traditional regression methods faces a bias trade-off between confounding bias and post-treatment control. Drawing on research from the field of epidemiology, in this article I present a marginal structural modeling (MSM) approach that allows scholars to generate unbiased estimates of CDEs. Further, I detail the characteristics and implementation of MSMs, compare the performance of this approach under different conditions, and discuss and assess practical challenges when conducting them. After presenting the method, I apply MSMs to estimate the effect of wealth in childhood on political participation, highlighting the improvement in terms of bias relative to traditional regression models. The analysis shows that MSMs improve our understanding of causal mechanisms especially when dealing with multi-categorical time-varying treatments and non-continuous outcomes.

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