The overall objective of this research is to develop statistical methods for quantifying the effects of interventions to prevent infectious diseases. Th primary motivating examples for this research are studies of vaccines, although the developed methods will be general and have immediate application in other settings. One particularly significant and challenging problem in vaccine studies entails assessing indirect effects of vaccination. For vaccines that are costly or do not afford complete protection from disease when an individual is vaccinated, evaluating the indirect effects (or herd immunity) is important in policy considerations about vaccine introduction and utilization. Failure to account for herd immunity can lead to incorrect conclusions regarding the public health benefit of a vaccine. Drawing inference about herd immunity is non-standard because indirect effects measure the effect of vaccinating one individual on another individual's health outcome. In the nomenclature of causal inference, this is known as interference. That is, interference is said to be present i the treatment (e.g., vaccination) of one individual affects the outcome of another individual. In this grant innovative statistical methods will be developed for drawing inference about the effects of a treatment or exposure when there is possibly interference between individuals.
In Aim 1 randomization-based (i.e., exact) statistical methods will be developed.
In Aim 2 inverse probability weighted, doubly robust, and stratified propensity score treatment effect estimators will be developed for observational studies.
Aim 3 will focus on inference about treatment effects on time-to-event outcomes subject to right censoring.
In Aim 4 treatment effect bounds and sensitivity analysis methods will be developed under various sets of assumptions which do not fully identify the causal effects.
For Aims 1 - 4 it will be assumed that individuals can be partitioned into groups such that there is no interference between individuals in different groups; this assumption will be reasonable if the groups are sufficiently separated spatially, temporally, and/or socially.
In Aim 5 methods will be developed for arbitrary forms of interference that do not assume the population can be partitioned into separate interference groups. For all of the proposed research, the theoretical properties of the proposed methods will be rigorously established. Extensive simulation studies will be conducted to evaluate the performance of the proposed methods in realistic settings. The developed methods will be used to analyze data from several large infectious disease prevention studies, providing new insights into the different effects of cholera, influenza, pneumococcal, rotavirus, and typhoid vaccines, and malaria bed nets. The resulting inferences will have straightforward interpretations in terms of the expected number of infections or cases of disease averted due to the intervention. The statistical methods developed will be applicable to many other settings where interference may be present, including econometrics, education, network analysis, political science, and spatial analyses.
The 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.
| 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 |
| 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 |
| 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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