We propose to continue our efforts to apply and further develop analytic methods for estimating the effect of public health interventions using complex longitudinal data from observational studies. Our focus will be the estimation of the causal effects and attributable risks of various interventions on the risk of coronary heart disease and related outcomes in prospective studies. Examples of hypothetical interventions are """"""""inactive individuals increase their activity to at least 30 minutes brisk walking per day,"""""""" """"""""everybody drinks 2 cups of coffee daily,"""""""" and """"""""statin therapy is initiated after a diagnosis of diabetes is made."""""""" We will estimate the effect of these hypothetical interventions using methods that overcome some key limitations of standard statistical methods. That is, the methods we propose allow us to estimate attributable risks of exposure, appropriately adjust for time-dependent confounding due to intermediate variables or variables affected by exposure, estimate the effect of joint interventions on multiple risk factors, and estimate the effect of dynamic interventions. Specifically, we will implement recently developed methods based on inverse probability of dynamic marginal structural models, and on g-estimation of nested structural models, to estimate the effect of hypothetical interventions under the assumption of no unmeasured confounding, and to assess the sensitivity of the estimates to violations of the assumption that can arise because of subclinical disease (""""""""reverse causation"""""""" bias) and exposure measurement error. The main reasons for the lack of widespread use of these methods is their perceived technical complexity, the scarcity of research on how to translate their theoretical development into practical applications, and the lack of standard software. We will adapt and further develop these methods to address realistic questions in cardiovascular epidemiology, and produce user-friendly and computationally-efficient software to apply them. Our research will produce state-of-the-art estimates of the effects of public health interventions on risk factors for coronary heart disease that use (i) some of the best observational cohorts available, and (ii) the most powerful methods available for causal inference from complex longitudinal data.

Public Health Relevance

Public health policy requires estimating the effects of hypothetical interventions on risk factors for cardiovascular diseases and other health outcomes. Unfortunately, standard regression methods may result in biased effect estimates when applied to complex longitudinal data. We propose to develop and implement methods that, under assumptions less restrictive than those of standard methods, can be used to estimate the effect of hypothetical interventions. We will also develop software for these methods, and will make it publicly available.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Wolz, Michael
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Harvard University
Public Health & Prev Medicine
Schools of Public Health
United States
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Danaei, Goodarz; García Rodríguez, Luis Alberto; Cantero, Oscar Fernández et al. (2018) Electronic medical records can be used to emulate target trials of sustained treatment strategies. J Clin Epidemiol 96:12-22
Danaei, Goodarz; Robins, James M; Young, Jessica G et al. (2016) Weight Loss and Coronary Heart Disease: Sensitivity Analysis for Unmeasured Confounding by Undiagnosed Disease. Epidemiology 27:302-10
Murray, Eleanor J; Hernán, Miguel A (2016) Adherence adjustment in the Coronary Drug Project: A call for better per-protocol effect estimates in randomized trials. Clin Trials 13:372-8
Lajous, Martín; Banack, Hailey R; Kaufman, Jay S et al. (2015) Should patients with chronic disease be told to gain weight? The obesity paradox and selection bias. Am J Med 128:334-6
Young, Jessica G; Tchetgen Tchetgen, Eric J (2014) Simulation from a known Cox MSM using standard parametric models for the g-formula. Stat Med 33:1001-14
Young, Jessica G; Her?an, Miguel A; Robins, James M (2014) Identification, estimation and approximation of risk under interventions that depend on the natural value of treatment using observational data. Epidemiol Methods 3:1-19
Wirth, Kathleen E; Tchetgen Tchetgen, Eric J (2014) Accounting for selection bias in association studies with complex survey data. Epidemiology 25:444-53
Lajous, Martin; Bijon, Anne; Fagherazzi, Guy et al. (2014) Body mass index, diabetes, and mortality in French women: explaining away a ""paradox"". Epidemiology 25:10-4
Hernán, Miguel A; Schisterman, Enrique F; Hernández-Díaz, Sonia (2014) Invited commentary: composite outcomes as an attempt to escape from selection bias and related paradoxes. Am J Epidemiol 179:368-70
Garcia-Aymerich, Judith; Varraso, Raphaëlle; Danaei, Goodarz et al. (2014) Incidence of adult-onset asthma after hypothetical interventions on body mass index and physical activity: an application of the parametric g-formula. Am J Epidemiol 179:20-6

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