The overall objective of this research is to develop statistical methods for quantifying the effects of interventions to prevent infectious diseases. The main motivating examples are studies of vaccine effectiveness. Two particularly challenging problems in vaccine studies entail assessing (i) indirect effects of vaccination and (ii) vaccine effects on post-infection endpoints. Evaluating (i) is a non-standard problem because indirect effects measure the effect of vaccinating one individual on another individual's health outcome. Assessing (ii) is challenging because infected vaccinees may not be comparable to infected controls. This specific proposal is to adapt and develop modern causal inference methodology for use in evaluating (i) and (ii). Similar research will be conducted motivated by studies to prevent transmission of HIV from mother to child where issues similar to (ii) arise.
Specific Aim 1 is to develop statistical methods in causal inference with interference for application in evaluating direct, indirect, total, and overall vaccine effects. Areas of particular emphasis will be development of nonparametric tests and confidence intervals, incorporating baseline covariates, and analysis of data from observational studies. A motivating data set is from a trial of cholera vaccines in Bangladesh.
Specific Aim 2 is to develop exact statistical methods in causal inference with principal stratification for application in evaluating vaccine effects on post-infection endpoints. This research will focus on applying the ideas of principal stratification in the small sample setting under minimal assumptions. Comparisons will be conducted between the proposed methods and existing large-sample methods as well as traditional intent-to-treat approaches. The research for this aim is motivated by proof-of-concept clinical trials of vaccines where few events are expected.
Specific Aim 3 is to develop causal inference methodology to assess vaccine effects on infectiousness. This research will combine aspects of Aims 1 and 2 since studies to assess vaccine effects on infectiousness typically entail conditioning on infection of primary cases (Aim 2) and the vaccination status of the primary case can affect the infection outcome in exposed close contacts (Aim 1). Methods developed in this aims will be used to estimate the causal effect of pertussis vaccination on infectiousness using data from a study in Senegal.
Specific Aim 4 is to develop statistical methods for causal inference with principal stratification and competing risks. This research is motivated by studies to prevent postnatal mother-to-child transmission (MTCT) of HIV where HIV-free death and weaning are competing risks. In such studies, investigators are often interested in comparing intervention strategies conditional on survival to a certain time point such that this aim will utilize the principal stratification framework. The methods developed under this aim will be used to estimate the causal effect of antiretroviral therapy on MTCT using data from a recent study in Malawi. 1

Public Health Relevance

This statistical methods developed in this research will lead to improved estimation of the effects of interventions to prevent infectious diseases. Accurate and precise quantification of intervention effects are important in regulatory decisions and public health policy regarding infectious disease control.

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Gezmu, Misrak
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University of North Carolina Chapel Hill
Biostatistics & Other Math Sci
Schools of Public Health
Chapel Hill
United States
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Halloran, M Elizabeth; Hudgens, Michael G (2018) Estimating population effects of vaccination using large, routinely collected data. Stat Med 37:294-301
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
Breskin, Alexander; Cole, Stephen R; Hudgens, Michael G (2018) A Practical Example Demonstrating the Utility of Single-world Intervention Graphs. Epidemiology 29:e20-e21
Richardson, Amy; Hudgens, Michael G; Fine, Jason P et al. (2017) Nonparametric binary instrumental variable analysis of competing risks data. Biostatistics 18:48-61
Saul, Bradley C; Hudgens, Michael G (2017) A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R. J Stat Softw 82:
Rigdon, Joseph; Loh, Wen Wei; Hudgens, Michael G (2017) Response to comment on 'Randomization inference for treatment effects on a binary outcome'. Stat Med 36:876-880
Westreich, Daniel; Hudgens, Michael G (2017) THE AUTHORS REPLY. Am J Epidemiol 185:614-615
Zhou, Jincheng; Chu, Haitao; Hudgens, Michael G et al. (2016) A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes. Stat Med 35:53-64
Lee, Hana; Hudgens, Michael G; Cai, Jianwen et al. (2016) Marginal Structural Cox Models with Case-Cohort Sampling. Stat Sin 26:509-526

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