28 citations · 40 across the 8 of their papers we have counts for
8 papers
Bayesian Inference of Mixing and Transmission Heterogeneity in Stratified Disease Surveillance Models
Miles Moran, Rob Trangucci, Lisa Madsen
When surveillance data of infectious disease incidence (e.g. weekly case counts) are disaggregated by demographic indicators, disparities in long-run health outcomes between these…
Bayesian Methods for Modeling Cumulative Exposure to Extensive Environmental Health Hazards
Rob Trangucci, Jesse Contreras, Jon Zelner +2
Measuring the impact of an environmental point source exposure on the risk of disease, like cancer or childhood asthma, is well-developed. Modeling how an environmental health haza…
Vaccine efficacy for binary post-infection outcomes under misclassification without monotonicity
Rob Trangucci, Yang Chen, Jon Zelner
In order to meet regulatory approval, pharmaceutical companies often must demonstrate that new vaccines reduce the total risk of a post-infection outcome like transmission, symptom…
Modeling racial/ethnic differences in COVID-19 incidence with covariates subject to non-random missingness
Rob Trangucci, Yang Chen, Jon Zelner
Characterizing the cumulative burden of COVID-19 by race/ethnicity is of the utmost importance for public health researchers and policy makers in order to design effective mitigati…
Quantifying Observed Prior Impact
David E Jones, Robert N Trangucci, Yang Chen
We distinguish two questions (i) how much information does the prior contain? and (ii) what is the effect of the prior? Several measures have been proposed for quantifying effectiv…
Voting patterns in 2016: Exploration using multilevel regression and poststratification (MRP) on pre-election polls
Rob Trangucci, Imad Ali, Andrew Gelman +1
We analyzed 2012 and 2016 YouGov pre-election polls in order to understand how different population groups voted in the 2012 and 2016 elections. We broke the data down by demograph…
Bayesian hierarchical weighting adjustment and survey inference
Yajuan Si, Rob Trangucci, Jonah Sol Gabry +1
We combine Bayesian prediction and weighted inference as a unified approach to survey inference. The general principles of Bayesian analysis imply that models for survey outcomes s…
A Hierarchical Bayes Approach to Adjust for Selection Bias in Before-After Analyses of Vision Zero Policies
Jonathan Auerbach, Christopher Eshleman, Rob Trangucci
American cities devote significant resources to the implementation of traffic safety countermeasures that prevent pedestrian fatalities. However, the before-after comparisons typic…