7 papers
Causal mediation analysis for longitudinal and survival data in continuous time using Bayesian non-parametric joint models
Saurabh Bhandari, Michael J. Daniels, Juned Siddique
Observational cohort data is an important source of information for understanding the causal effects of treatments on survival and the degree to which these effects are mediated th…
Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan
Zeynab Aghabazaz, Michael J Daniels, Hongyan Ning +2
We introduce a statistical framework for combining data from multiple large longitudinal cardiovascular cohorts to enable the study of long-term cardiovascular health starting in e…
A Bayesian Nonparametric Approach for Semi-Competing Risks with Application to Cardiovascular Health
Karina Gelis-Cadena, Michael Daniels, Juned Siddique
We address causal estimation in semi-competing risks settings, where a non-terminal event may be precluded by one or more terminal events. We define a principal-stratification caus…
Personalized feature threshold estimation in joint modelling of longitudinal and time-to-event data
Mirajul Islam, Michael J. Daniels, Juned Siddique
Cardiovascular disease (CVD) cohort studies collect longitudinal data on numerous CVD risk factors including body mass index (BMI), systolic blood pressure (SBP), diastolic blood p…
A Joint Model of Longitudinal CVD Risk Factors, Medication Use, and Time-to-Terminal Events
Zeynab Aghabazaz, Michael J Daniels, Donald M Lloyd-Jones +1
We introduce a novel Bayesian approach for jointly modeling longitudinal cardiovascular disease (CVD) risk factor trajectories, medication use, and time-to-events. Our methodology…
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…