The overall goal of this proposed research is to develop, apply and disseminate advanced, yet practical quantitative methods to enable accurate inference from complex longitudinal data on individuals infected with HIV. Marginal structural models are an accurate and flexible potential outcomes-based approach for estimating etiologic effects using observational data. This innovative approach for longitudinal data analysis has a rapidly increasing appearance in the scholarly literature.
The aims of this proposal are to: (1) extend marginal structural models to generalize estimates from a study sample to a target population;(2) extend marginal structural models to studies nested in cohorts;(3) extend marginal structural models to account for differential exposure measurement error;(4) extend marginal structural models to account for prior information using Bayesian methods;(5) develop estimates for a marginal structural model that combine estimation from inverse probability weighting and the parametric G-formula;and (6) develop widely- applicable statistical software to implement methods for extensions in aims 1-5. The innovations defined in aims 1-5 are essential for marginal structural models to become more useful to applied scientists studying HIV and public health. To accomplish these aims, we will use important and timely empirical data from the Centers for AIDS Research (CFAR) Network of Integrated Clinical Systems (CNICS). The CNICS includes over 20,000 HIV infected adults seen quarterly in clinical care at 8 CFAR sites since 2006. The fruits of this research will be described in a series of peer-reviewed papers for each aim. These papers will define each problem, describe an innovative solution, provide support for the solution in terms of Monte Carlo simulation experiments, illustrate the method using analysis of CNICS data, and provide guidance on implementation of the solution using developed statistical software. In conclusion, the present proposal describes a significant and innovative program of research that will result in groundbreaking methodological tools for making accurate inferences from complex observational studies of HIV and other diseases.

Public Health Relevance

The goal of this work is to make new data analysis methods. These new methods will help scientists to get correct results from medical data. Results will allow scientists to better study the AIDS virus and the public's health.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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AIDS Clinical Studies and Epidemiology Study Section (ACE)
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Mckaig, Rosemary G
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University of North Carolina Chapel Hill
Public Health & Prev Medicine
Schools of Public Health
Chapel Hill
United States
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Lee, Jennifer S; Cole, Stephen R; Achenbach, Chad J et al. (2018) Cancer risk in HIV patients with incomplete viral suppression after initiation of antiretroviral therapy. PLoS One 13:e0197665
Rudolph, Jacqueline E; Cole, Stephen R; Edwards, Jessie K (2018) Parametric assumptions equate to hidden observations: comparing the efficiency of nonparametric and parametric models for estimating time to AIDS or death in a cohort of HIV-positive women. BMC Med Res Methodol 18:142
Keil, Alexander P; Mooney, Stephen J; Jonsson Funk, Michele et al. (2018) RESOLVING AN APPARENT PARADOX IN DOUBLY ROBUST ESTIMATORS. Am J Epidemiol 187:891-892
Breskin, Alexander; Cole, Stephen R; Hudgens, Michael G (2018) A Practical Example Demonstrating the Utility of Single-world Intervention Graphs. Epidemiology 29:e20-e21
Edwards, Jessie K; Cole, Stephen R; Hall, H Irene et al. (2018) Virologic suppression and CD4+ cell count recovery after initiation of raltegravir or efavirenz-containing HIV treatment regimens. AIDS 32:261-266
Cole, Stephen R; Edwards, Jessie K; Westreich, Daniel et al. (2018) Estimating multiple time-fixed treatment effects using a semi-Bayes semiparametric marginal structural Cox proportional hazards regression model. Biom J 60:100-114
Buchanan, Ashley L; Hudgens, Michael G; Cole, Stephen R et al. (2018) Generalizing Evidence from Randomized Trials using Inverse Probability of Sampling Weights. J R Stat Soc Ser A Stat Soc 181:1193-1209
Breskin, Alexander; Cole, Stephen R; Hudgens, Michael G (2018) The Authors Respond. Epidemiology 29:e51
Lesko, Catherine R; Buchanan, Ashley L; Westreich, Daniel et al. (2018) The Authors Respond. Epidemiology 29:e14-e15
Edwards, Jessie K; Cole, Stephen R; Moore, Richard D et al. (2018) Sensitivity Analyses for Misclassification of Cause of Death in the Parametric G-Formula. Am J Epidemiol :

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