7 papers · 1 filter
Bayesian feature selection in joint models with application to a cardiovascular disease cohort study
Mirajul Islam, Michael J. Daniels, Zeynab Aghabazaz +1
Cardiovascular disease (CVD) cohorts collect data longitudinally to study the association between CVD risk factors and event times. An important area of scientific research is to b…
A Bayesian semi-parametric approach to causal mediation for longitudinal mediators and time-to-event outcomes with application to a cardiovascular disease cohort study
Saurabh Bhandari, Michael J. Daniels, Maria Josefsson +2
Causal mediation analysis of observational data is an important tool for investigating the potential causal effects of medications on disease-related risk factors, and on time-to-d…
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…
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.…