5 citations · 11 across the 8 of their papers we have counts for
6 papers · 1 filter
A Bayesian semi-parametric approach for inference on the population partly conditional mean from longitudinal data with dropout
Maria Josefsson, Michael J. Daniels, Sara Pudas
Studies of memory trajectories using longitudinal data often result in highly non-representative samples due to selective study enrollment and attrition. An additional bias comes f…
Informed Pooled Testing with Quantitative Assays
Tao Liu, Joseph W Hogan, Wanning Su +3
Pooled testing is widely used for screening for viral or bacterial infections with low prevalence when individual testing is not cost-efficient. Pooled testing with qualitative ass…
Bayesian Methods for Multiple Mediators: Relating Principal Stratification and Causal Mediation in the Analysis of Power Plant Emission Controls
Chanmin Kim, Michael Daniels, Joseph Hogan +2
Emission control technologies installed on power plants are a key feature of many air pollution regulations in the US. While such regulations are predicated on the presumed relatio…
Bayesian Longitudinal Causal Inference in the Analysis of the Public Health Impact of Pollutant Emissions
Chanmin Kim, Corwin M Zigler, Michael J Daniels +2
Pollutant emissions from coal-burning power plants have been deemed to adversely impact ambient air quality and public health conditions. Despite the noticeable reduction in emissi…
Bayesian nonparametric generative models for causal inference with missing at random covariates
Jason Roy, Kirsten J Lum, Michael J. Daniels +3
We propose a general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting. The joint distribution of the observed data (outcome, treatment, and…
Comparing Biomarkers as Trial Level General Surrogates
Erin E. Gabriel, Michael J. Daniels, M. Elizabeth Halloran
An intermediate response measure that accurately predicts efficacy in a new setting can reduce trial cost and time to product licensure. In this paper, we define a trial level gene…