Prediction of Individual Long-term Outcomes in Smoking Cessation Trials Using Frailty Models
Article first published online: 14 MAR 2011
© 2011, The International Biometric Society
Volume 67, Issue 4, pages 1321–1329, December 2011
How to Cite
Li, Y., Wileyto, E. P. and Heitjan, D. F. (2011), Prediction of Individual Long-term Outcomes in Smoking Cessation Trials Using Frailty Models. Biometrics, 67: 1321–1329. doi: 10.1111/j.1541-0420.2011.01578.x
- Issue published online: 14 DEC 2011
- Article first published online: 14 MAR 2011
- Received May 2010. Revised November 2010. Accepted December 2010.
- Bayesian inference;
- Frailty model;
- Importance sampling;
- ROC curves;
- Smoking cessation
Summary In smoking cessation clinical trials, subjects commonly receive treatment and report daily cigarette consumption over a period of several weeks. Although the outcome at the end of this period is an important indicator of treatment success, substantial uncertainty remains on how an individual's smoking behavior will evolve over time. Therefore it is of interest to predict long-term smoking cessation success based on short-term clinical observations. We develop a Bayesian method for prediction, based on a cure-mixture frailty model we proposed earlier, that describes the process of transition between abstinence and smoking. Specifically we propose a two-stage prediction algorithm that first uses importance sampling to generate subject-specific frailties from their posterior distributions conditional on the observed data, then samples predicted future smoking behavior trajectories from the estimated model parameters and sampled frailties. We apply the method to data from two randomized smoking cessation trials comparing bupropion to placebo. Comparisons of actual smoking status at one year with predictions from our model and from a variety of empirical methods suggest that our method gives excellent predictions.