Calculation of C statistics for the Cox proportional hazards regression models and its implementation in SAS

YAN Ruo-hua; LI Wei; GU Hong-qiu; WANG Yang

doi:10.16462/j.cnki.zhjbkz.2016.09.023

Volume 20 Issue 9

Sep. 2016

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Article Navigation > CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION > 2016 > 20(9): 953-956,961

YAN Ruo-hua, LI Wei, GU Hong-qiu, WANG Yang. Calculation of C statistics for the Cox proportional hazards regression models and its implementation in SAS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(9): 953-956,961. doi: 10.16462/j.cnki.zhjbkz.2016.09.023

Citation:

YAN Ruo-hua, LI Wei, GU Hong-qiu, WANG Yang. Calculation of C statistics for the Cox proportional hazards regression models and its implementation in SAS[J]. CHINESE JOURNAL OF DISEASE CONTROL & PREVENTION, 2016, 20(9): 953-956,961. doi: 10.16462/j.cnki.zhjbkz.2016.09.023

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Calculation of C statistics for the Cox proportional hazards regression models and its implementation in SAS

doi: 10.16462/j.cnki.zhjbkz.2016.09.023

State Key Laboratory of Cardiovascular Disease, Fu Wai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China

Received Date: 2016-04-30
Rev Recd Date: 2016-07-26

Abstract

Abstract

Objective C statistics is one of the most widely-used indexes in accessing the discrimination of the Cox proportional hazards regression models. However, the calculation methods for C statistics have been controversial. Our study aims to investigate the calculation of C statistics and its implementation in SAS. Methods To calculate C statistics and its 95% confidence interval (CI), we used PROC PHREG to predict the survival function, and decided whether the predicted survival probabilities was consistent with the actual survival times. Taking a registry study as an example, we evaluated the discrimination of a Cox regression model which predicted the 30-day mortality after discharge in patients with acute heart failure. Results A total of 2 836 patients were included in the final analysis. Older age (Unit: years; hazard ratio (HR): 1.029; 95% CI: 1.022-1.037), lower systolic blood pressure (Unit: mmHg; HR: 0.992; 95% CI: 0.989-0.995) and increased pulse rate (Unit: beats/min; HR: 1.011; 95% CI: 1.007-1.014) were all statistically significant predictors for 30-day post-discharge death. The C statistics of the model was 0.638 (95% CI: 0.570-0.704), indicating a certain degree of discrimination. Conclusions C statistics is a good index for accessing the discrimination of Cox regression models, and it can be calculated by SAS programs.
- Statistics, the parameters,
- Models, statistical,
- Epidemiologic methods

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