Related Experiment Video
Updated: Jun 16, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Joint Models for the Association of Longitudinal Binary and Continuous Processes With Application to a Smoking
Xuefeng Liu1, Michael J Daniels, Bess Marcus
1Department of Biostatistics and Epidemiology, East Tennessee State University, Johnson City, TN 37614.
None:
Joint models for the association of a longitudinal binary and a longitudinal continuous process are proposed for situations in which their association is of direct interest. The models are parameterized such that the dependence between the two processes is characterized by unconstrained regression coefficients. Bayesian variable selection techniques are used to parsimoniously model these coefficients. A Markov chain Monte Carlo (MCMC) sampling algorithm is developed for sampling from the posterior distribution, using data augmentation steps to handle missing data. Several technical issues are addressed to implement the MCMC algorithm efficiently. The models are motivated by, and are used for, the analysis of a smoking cessation clinical trial in which an important question of interest was the effect of the (exercise) treatment on the relationship between smoking cessation and weight gain.
Related Concept Videos
Longitudinal Research
Longitudinal Studies
Statistical Methods for Analyzing Epidemiological Data
Assumptions of Survival Analysis
Truncation in Survival Analysis
Left truncation occurs when individuals who experienced the event of interest before a certain time are not included in the study. This is often due to a "delayed entry" into the study where only those who survive until a certain entry point are observed.
Mechanistic Models: Compartment Models in Individual and Population Analysis