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Dynamic Prediction of Outcome for Patients With Ovarian Cancer: Application of a Joint Model for Longitudinal Cancer Antigen 125 Values
  1. Yuting Cao*,
  2. Yi Jiang,
  3. Xiao Lin*,
  4. Jinsong Liu,
  5. Tao Lu*,
  6. Wenjun Cheng and
  7. Fangrong Yan, PhD*
  1. * Research Center of Biostatistics and Computational Pharmacy, China pharmaceutical University, Nanjing, PR China;
  2. The First Affiliated Hospital of Nanjing Medical University, Nanjing, PR China; and
  3. Department of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX.
  1. Address correspondence and reprint requests to W. Cheng, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210019, PR China. E-mail: chengwenjundoc{at}163.com; F. Yan, Research Center of Biostatistics and Computational Pharmacy, China Pharmaceutical University, Nanjing 210009, PR China. E-mail: f.r.yan{at}163.com.

Abstract

Objective In clinical practice, gynecologic oncologists are interested in predicting the prognosis of patients through information from different sources. Recently, the overall survival (OS) of ovarian cancer patients has been widely and intensively researched, and a large number of risk factors have been determined, including the biomarker of cancer antigen 125 (CA-125). For newly diagnosed patients, it is critical to construct effective prognostic models to predict prognosis dynamically by combining their CA-125 values with adjusted clinical factors.

Methods/Materials A total of 227 ovarian cancer participants entered this study. A 4-step method was used to construct a joint model to examine the association between longitudinal CA-125 measurements and OS time, to explore time-independent predictive factors influencing OS, and to obtain an accurate and credible dynamic prediction of OS for specific subjects.

Results We found that CA-125 values were greatly affected by observation time, menarche, Federation International of Gynecology and Obstetrics stage, and ascites at baseline. Similarly, CA-125 values, menopause, Federation International of Gynecology and Obstetrics stage, and surgery state at baseline were selected from the best Cox proportion hazard model and showed a strong correlation with OS. In addition, the analyses presented by the joint model depict that, as time goes by, increasing CA-125 was deemed to be a significant predictor of death.

Conclusions Together, our results show that a joint model could be highly efficient in clinical consultation and diagnosis for patients newly diagnosed as having ovarian cancer. Longitudinal CA-125 values, which are measured over time, can be used to credibly predict OS after taking all adjusted covariates into account.

  • CA-125
  • Joint model
  • Ovarian cancer
  • Overall survival

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Footnotes

  • The authors declare no conflicts of interest.

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