Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes
Article first published online: 18 MAY 2009
© 2009, The International Biometric Society
Volume 66, Issue 1, pages 20–29, March 2010
How to Cite
Rizopoulos, D., Verbeke, G. and Molenberghs, G. (2010), Multiple-Imputation-Based Residuals and Diagnostic Plots for Joint Models of Longitudinal and Survival Outcomes. Biometrics, 66: 20–29. doi: 10.1111/j.1541-0420.2009.01273.x
- Issue published online: 17 MAR 2010
- Article first published online: 18 MAY 2009
- Received July 2008. Revised December 2008. Accepted February 2009.
- Joint modeling;
- Longitudinal data;
- Model diagnostics;
- Survival data
Summary The majority of the statistical literature for the joint modeling of longitudinal and time-to-event data has focused on the development of models that aim at capturing specific aspects of the motivating case studies. However, little attention has been given to the development of diagnostic and model-assessment tools. The main difficulty in using standard model diagnostics in joint models is the nonrandom dropout in the longitudinal outcome caused by the occurrence of events. In particular, the reference distribution of statistics, such as the residuals, in missing data settings is not directly available and complex calculations are required to derive it. In this article, we propose a multiple-imputation-based approach for creating multiple versions of the completed data set under the assumed joint model. Residuals and diagnostic plots for the complete data model can then be calculated based on these imputed data sets. Our proposals are exemplified using two real data sets.