A joint model based on longitudinal CA125 in ovarian cancer to predict recurrence. (January 2016) - Chang, Chung; Chiang, An Jen; Chen, Wei-An; Chang, Hsueh-Wen; Chen, Jiabin
Aims : To develop a new package of joint model to fit longitudinal CA125 in epithelial ovarian cancer relapse.Patients & methods : Included were 305 epithelial ovarian cancer patients who reached complete remission after cytoreductive surgery and first-line chemotherapy. Univariate and multivariate analysis with a joint model was performed to select independent risk factors, which were subsequently combined to predict recurrence.Results : Independent factors were longitudinal CA125, age, stage and residual tumor size (p < 0.05). Prediction of recurrence with these factors had an average of 80.7% accuracy, 5.6–10.7% better than kinetic factors.Conclusion : The new package of joint model fits longitudinal CA125 well. Potential application can be extended to other biomarkers.