An Audit Strategy for Progression-Free Survival
Version of Record online: 6 JAN 2011
© 2011, The International Biometric Society No claim to original US Federal works
Volume 67, Issue 3, pages 1092–1099, September 2011
How to Cite
Dodd, L. E., Korn, E. L., Freidlin, B., Gray, R. and Bhattacharya, S. (2011), An Audit Strategy for Progression-Free Survival. Biometrics, 67: 1092–1099. doi: 10.1111/j.1541-0420.2010.01539.x
- Issue online: 14 SEP 2011
- Version of Record online: 6 JAN 2011
- Received May 2010. Revised October 2010. Accepted October 2010.
- Auxiliary variables;
- Blinded independent central review;
- Event adjudication committee;
- Measurement error;
- Randomized clinical trial;
- Regression estimator
Summary In randomized clinical trials, the use of potentially subjective endpoints has led to frequent use of blinded independent central review (BICR) and event adjudication committees to reduce possible bias in treatment effect estimators based on local evaluations (LE). In oncology trials, progression-free survival (PFS) is one such endpoint. PFS requires image interpretation to determine whether a patient's cancer has progressed, and BICR has been advocated to reduce the potential for endpoints to be biased by knowledge of treatment assignment. There is current debate, however, about the value of such reviews with time-to-event outcomes such as PFS. We propose a BICR audit strategy as an alternative to a complete-case BICR to provide assurance of the presence of a treatment effect. We develop an auxiliary-variable estimator of the log-hazard ratio that is more efficient than simply using the audited (i.e., sampled) BICR data for estimation. Our estimator incorporates information from the LE on all the cases and the audited BICR cases, and is an asymptotically unbiased estimator of the log-hazard ratio from BICR. The estimator offers considerable efficiency gains that improve as the correlation between LE and BICR increases. A two-stage auditing strategy is also proposed and evaluated through simulation studies. The method is applied retrospectively to a large oncology trial that had a complete-case BICR, showing the potential for efficiency improvements.