We propose 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 effects and attribute risks of various interventions on the risk of coronary heart disease in the Nurses' Health Study. Examples of hypothetical interventions are *a random half of the smokers quit smoking at baseline' and 'inactive individuals increase their activity to at least 30 minutes brisk walking per day1. In contrast to standard statistical methods, the methods we propose allow us to estimate consistent attributable risks, appropriately adjust for time-dependent confounding due to intermediate variables, estimate the effect of joint interventions on multiple risk factors, and estimate the effect of dynamic interventions. 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) one of the largest observational cohorts available, and (ii) the most powerful methods available for causal inference from complex longitudinal data. We will estimate the effect of hypothetical interventions using the parametric g-formula (based on regression and Monte Carlo simulation), marginal structural models (based on inverse-probability weighting), and nested structural models (based on g-estimation). These methods have shown their superiority over standard analytic methods in some areas, but their practical impact in cardiovascular disease epidemiology is largely unknown because they have not been systematically applied to large cohorts with longitudinal data on time-varying risk factors and health outcomes. The main reasons for the lack of widespread used of these methods is their perceived technical complexity, the scarcity of research on how to translate them 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.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL080644-01
Application #
6911805
Study Section
Biostatistical Methods and Research Design Study Section (BMRD)
Program Officer
Wolz, Michael
Project Start
2005-06-01
Project End
2009-05-31
Budget Start
2005-06-01
Budget End
2006-05-31
Support Year
1
Fiscal Year
2005
Total Cost
$455,125
Indirect Cost
Name
Harvard University
Department
Public Health & Prev Medicine
Type
Schools of Public Health
DUNS #
149617367
City
Boston
State
MA
Country
United States
Zip Code
02115
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

Showing the most recent 10 out of 52 publications