Assessing of case–cohort design: a case study for breast cancer patients in Xinjiang, China

Abstract

ObjectiveTo assess the effectiveness and clinical value of case–cohort design and determine prognostic factors of breast cancer patients in Xinjiang on the basis of case–cohort design.MethodsThe survival data with different sample characteristics were simulated by using Cox proportional risk models. To evaluate the effectiveness for the case–cohort, entire cohort, and simple random sampling design by comparing the mean, coefficient of variation, etc., of covariate parameters. Furthermore, the prognostic factors of breast cancer patients in Xinjiang were determined based on case–cohort sampling designs. The models were comprehensively evaluated by likelihood ratio test, the area under the receiver operating characteristic curve (AUC), and Akaike Information Criterion (AIC).ResultsIn a simulations study, the case–cohort design shows better stability and improves the estimation efficiency when the censored rate is high. In the breast cancer data, molecular subtypes, T-stage, N-stage, M-stage, types of surgery, and postoperative chemotherapy were identified as the prognostic factors of patients in Xinjiang. These models based on the different sampling designs both passed the likelihood ratio test (p<0.05). Moreover, the model constructed under the case–cohort design had better fitting effect (AIC=3,999.96) and better discrimination (AUC=0.807).ConclusionSimulations study confirmed the effectiveness of case–cohort design and further determined the prognostic factors of breast cancer patients in Xinjiang based on this design, which presented the practicality of case–cohort design in actual data.