Robust Prediction of t-Year Survival with Data from Multiple Studies
Article first published online: 28 JUL 2010
© 2010, The International Biometric Society
Volume 67, Issue 2, pages 436–444, June 2011
How to Cite
Cai, T., Gerds, T. A., Zheng, Y. and Chen, J. (2011), Robust Prediction of t-Year Survival with Data from Multiple Studies. Biometrics, 67: 436–444. doi: 10.1111/j.1541-0420.2010.01462.x
- Issue published online: 20 JUN 2011
- Article first published online: 28 JUL 2010
- Received April 2009. Revised April 2010. Accepted April 2010.
- Conditional Kaplan–Meier;
- Nonparametric maximum likelihood;
- Predictive values;
- Survival analysis
Summary Recently meta-analysis has been widely utilized to combine information across multiple studies to evaluate a common effect. Integrating data from similar studies is particularly useful in genomic studies where the individual study sample sizes are not large relative to the number of parameters of interest. In this article, we are interested in developing robust prognostic rules for the prediction of t-year survival based on multiple studies. We propose to construct a composite score for prediction by fitting a stratified semiparametric transformation model that allows the studies to have related but not identical outcomes. To evaluate the accuracy of the resulting score, we provide point and interval estimators for the commonly used accuracy measures including the time-specific receiver operating characteristic curves, and positive and negative predictive values. We apply the proposed procedures to develop prognostic rules for the 5-year survival of breast cancer patients based on five breast cancer genomic studies.