Reclassification calibration test for censored survival data: performance and comparison to goodness-of-fit criteria

BACKGROUND: The risk reclassification table assesses clinical performance of a biomarker in terms of movements across relevant risk categories. The Reclassification- Calibration (RC) statistic has been developed for binary outcomes, but its performance for survival data with moderate to high censoring rates has not been evaluated. METHODS: We develop an RC statistic for survival data with higher censoring rates using the Greenwood-Nam-D’Agostino approach (RC-GND). We examine its performance characteristics and compare its performance and utility to the Hosmer-Lemeshow goodness-of-fit test under various assumptions about the censoring rate and the shape of the baseline hazard. RESULTS: The RC-GND test was robust to high (up to 50%) censoring rates and did not exceed the targeted 5% Type I error in a variety of simulated scenarios. It achieved 80% power to detect better calibration with respect to clinical categories when an important predictor with a hazard ratio of at least 1.7 to 2.2 was added to the model, while the Hosmer-Lemeshow goodness-of-fit (gof) test had power of 5% in this scenario. CONCLUSIONS: The RC-GND test should be used to test the improvement in calibration with respect to clinically relevant risk strata. When an important predictor is omitted, the Hosmer-Lemeshow goodness-of-fit test is usually not significant, while the RC-GND test is sensitive to such an omission. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s41512-018-0034-5) contains supplementary material, which is available to authorized users.