Smoking hot joint models for attrition bias—XMAR-ks the spot
Abstract
It's been said that smoking is a leading cause of statistics. Striving to understand relationships between smoking, cognition, dementia and death has long served as a prototypical example of selection bias issues when studying longitudinal change in the presence of likely informative censoring events. The article by Stuckwisch et al. (Am J Epidemiol. 2025: https://doi.org/10.1093/aje/kwaf107) compares results between a standard linear mixed model and an increasingly used Joint Model approach to investigate this long-vexing problem. We describe the nature of the "extended missing at random" (XMAR) assumption of the Joint Model, position the XMAR assumption in an expanded four-level missingness taxonomy, and illustrate a sequential sensitivity analysis using example data on smoking and cognitive decline with attrition by dementia and death events to illuminate approaches.
Fields of Study & Subjects
Statistical Methods and Bayesian Inference
Smoking Behavior and Cessation
Advanced Causal Inference Techniques
Publication details
- Journal / source
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Volume
- 195
- Issue
- 6
- Pages
- 1651–1658
- Publication date
- 20 March 2026
- Language
- EN