The overall objective of this research is to develop statistical methods for quantifying the effects of interventions to prevent infectious diseases. The 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 (spillover) effects of vaccination. For vaccines that are costly or do not afford complete protection from disease when an individual is vaccinated, evaluating 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 if 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. For each of the project's aims, the theoretical properties of the proposed statistical methods will be established. Simulation studies will be conducted to evaluate the performance of the proposed methods over a wide range of 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 vaccines for cholera, influenza, and other pathogens, 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. User-friendly software implementing the proposed methods will be developed and made freely available. The statistical methods and software 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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