5 citations · 14 across the 18 of their papers we have counts for
18 papers
Dirichlet process mixture models for the Analysis of Repeated Attempt Designs
Michael J. Daniels, Minji Lee, Wei Feng
In longitudinal studies, it is not uncommon to make multiple attempts to collect a measurement after baseline. Recording whether these attempts are successful provides useful infor…
A Bayesian Non-parametric Approach for Causal Mediation with a Post-treatment Confounder
Woojung Bae, Michael J. Daniels, Michael G. Perri
We propose a new Bayesian non-parametric (BNP) method for estimating the causal effects of mediation in the presence of a post-treatment confounder. We specify an enriched Dirichle…
Truncation Approximation for Enriched Dirichlet Process Mixture Models
Natalie Burns, Michael J. Daniels
Enriched Dirichlet process mixture (EDPM) models are Bayesian nonparametric models which can be used for nonparametric regression and conditional density estimation and which overc…
A Bayesian nonparametric approach for causal inference with multiple mediators
Samrat Roy, Michael J. Daniels, Brendan J. Kelly +1
Mediation analysis with contemporaneously observed multiple mediators is an important area of causal inference. Recent approaches for multiple mediators are often based on parametr…
Flexible evaluation of surrogacy in Bayesian adaptive platform studies
Michael C Sachs, Erin E Gabriel, Alessio Crippa +1
Trial level surrogates are useful tools for improving the speed and cost effectiveness of trials, but surrogates that have not been properly evaluated can cause misleading results.…
Information Borrowing in Regression Models
Amy Zhang, Le Bao, Michael J. Daniels
Model development often takes data structure, subject matter considerations, model assumptions, and goodness of fit into consideration. To diagnose issues with any of these factors…