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Evaluation of the Time-Varying Effect of Prognostic Factors on Survival in Ovarian Cancer

  • Gynecologic Oncology
  • Published:
Annals of Surgical Oncology Aims and scope Submit manuscript

Abstract

Purpose

To explore the risk factors in ovarian cancer with respects of time-varying effects on recurrence and survival.

Methods

Two hundred and ninety-eight patients with epithelial ovarian cancer in the Kaohsiung Veterans’ General Hospital from January 1995 to the end of 2011 were included in the study. The assumption of the Cox proportional hazard model, i.e., the hazard ratio is a constant with time, was tested for available prognostic factors. An extended Cox model was then applied, and a statistical package was constructed to perform multivariate analysis in presence of both time-varying and time-independent factors.

Results

Most prognostic factors met the assumption of the Cox proportional hazard model (p > 0.05) except for cancer-associated antigen (CA) 125 nadir concentration during first-line chemotherapy (p = 0.02). Multivariate analysis, where CA125 nadir was allowed to change with time while other factors remained constant, showed that International Federation of Gynecology and Obstetrics (FIGO) stage, residual tumor, CA125 nadir, and age were independent risk factors for recurrence and death.

Conclusions

The effect of CA125 nadir on recurrence and overall survival is not constant over time. It loses predictivity on recurrence and survival after 4.5 years. Awareness of the time-varying effects of the prognostic factors is beneficial to gynecologists in patient consultation and case evaluation.

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References

  1. Gadducci A, Cosio S, Zola P, Landoni F, Maggino T, Sartori E. Surveillance procedures for patients treated for epithelial ovarian cancer: a review of the literature. Int Gynecol Cancer. 2007;17:21–31.

    Article  CAS  Google Scholar 

  2. Su Z, Graybill WS, Zhu Y. Detection and monitoring of ovarian cancer. Clin Chim Acta. 2013;415:341–5.

    Article  CAS  PubMed  Google Scholar 

  3. Gupta D, Lis CG. Role of CA125 in predicting ovarian cancer survival—a review of the epidemiological literature. J Ovarian Res. 2009;2:13.

    Article  PubMed Central  PubMed  Google Scholar 

  4. Makar AP, Baekelandt M, Trope CG, Kristensen GB. The prognostic significance of residual disease, FIGO substage, tumor histology, and grade in patients with FIGO stage III ovarian cancer. Gynecol Oncol. 1995;56:175–80.

    Article  CAS  PubMed  Google Scholar 

  5. Holschneider CH, Berek JS. Ovarian cancer: epidemiology, biology, and prognostic factors. Semin Surg Oncol. 2000;19:3–10.

    Article  CAS  PubMed  Google Scholar 

  6. Cox DR. Regression models and life-tables. J R Stat Soc B. 1972;34:187–220.

    Google Scholar 

  7. Martinussen T, Scheike TH. Dynamic regression models for survival data. New York: Springer; 2006.

    Google Scholar 

  8. R Development Core Team. R: A language and environment of statistical computing. Vienna, Austria: R Foundation for Statistical Computing; 2010.

  9. Perperoglou A, le Cessie S, van Houwelingen HC. A fast routine for fitting Cox models with time varying effects of the covariates. Comput Methods Programs Biomed. 2006;81:154–61.

    Article  PubMed  Google Scholar 

  10. Peters-Engl C, Obermair A, Heinzl H, Buxbaum P, Sevelda P, Medl M. CA 125 regression after two completed cycles of chemotherapy: lack of prediction for long-term survival in patients with advanced ovarian cancer. Br J Cancer. 1999;81:662–6.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  11. Giolo SR, Krieger JE, Mansur AJ, Pereira AC. Survival analysis of patients with heart failure: implications of time-varying regression effects in modeling mortality. PLoS One. 2012;7:e37392.

    Article  PubMed Central  CAS  PubMed  Google Scholar 

  12. Tian L, Zucker D, Wei LJ. On the Cox model with time-varying regression coefficients. J Am Stat Assoc. 2005;100:172–83.

    Article  CAS  Google Scholar 

  13. Liu ML, Lu WB, Shore RE, Zeleniuch-Jacquotte A. Cox regression model with time-varying coefficients in nested case-control studies. Biostatistics. 2010;11:693–706.

    Article  PubMed Central  PubMed  Google Scholar 

  14. van Altena AM, Kolwijck E, Spanjer MJ, Hendriks JC, Massuger LF, de Hullu JA. CA125 nadir concentration is an independent predictor of tumor recurrence in patients with ovarian cancer: a population-based study. Gynecol Oncol. 2010;119:265–9.

    Article  PubMed  Google Scholar 

  15. Markman M, Liu PY, Rothenberg ML, Monk BJ, Brady M, Alberts DS. Pretreatment CA-125 and risk of relapse in advanced ovarian cancer. J Clin Oncol. 2006;24:1454–8.

    Article  CAS  PubMed  Google Scholar 

  16. Juretzka MM, Barakat RR, Chi DS, Iasonos A, Dupont J, Abu-Rustum NR, et al. CA125 level as a predictor of progression-free survival and overall survival in ovarian cancer patients with surgically defined disease status prior to the initiation of intraperitoneal consolidation therapy. Gynecol Oncol. 2007;104:176–80.

    Article  CAS  PubMed  Google Scholar 

  17. Prat A, Parera M, Peralta S, Perez-Benavente MA, Garcia A, Gil-Moreno A, et al. Nadir CA-125 concentration in the normal range as an independent prognostic factor for optimally treated advanced epithelial ovarian cancer. Ann Oncol. 2008;19:327–31.

    Article  CAS  PubMed  Google Scholar 

  18. Kang S, Seo SS, Park SY. Nadir CA-125 level is an independent prognostic factor in advanced epithelial ovarian cancer. J Surg Oncol. 2009;100:244–7.

    Article  CAS  PubMed  Google Scholar 

  19. Riedinger JM, Wafflart J, Ricolleau G, Eche N, Larbre H, Basuyau JP, et al. CA 125 half-life and CA 125 nadir during induction chemotherapy are independent predictors of epithelial ovarian cancer outcome: results of a French multicentric study. Ann Oncol. 2006;17:1234–8.

    Article  CAS  PubMed  Google Scholar 

  20. Aalen OO. Further results on the non-parametric linear regression model in survival analysis. Stat Med. 1993;12:1569–88.

    Article  CAS  PubMed  Google Scholar 

  21. Mckeague IW, Sasieni PD. A partly parametric additive risk model. Biometrika. 1994;81:501–14.

    Article  Google Scholar 

  22. Chiang AJ, Chen J, Chung YC, Huang HJ, Liou WS, Chang C. A longitudinal analysis with CA-125 to predict overall survival in patients with ovarian cancer. J Gynecol Oncol. 2014;25:51–7.

    Article  PubMed Central  PubMed  Google Scholar 

  23. Govindarajulu US, Spiegelman D, Thurston SW, Ganguli B, Eisen EA. Comparing smoothing techniques in Cox models for exposure-response relationships. Stat Med. 2007;26:3735–52.

    Article  PubMed  Google Scholar 

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Acknowledgment

The study was supported in part by a Grant from the National Science Council of Taiwan (NSC 102-2118-M-110-003) and two Grants from KSVGH (VGHKS103-086 and VGHNSU103-006).

Disclosure

The authors declare no conflict of interest.

Author information

Authors and Affiliations

  1. Department of Applied Mathematics, National Sun Yat-sen University, Kaohsiung, Taiwan, Roc

    Chung Chang PhD, Hui-Ching Wang MS & Wei-An Chen MS

  2. Department of Obstetrics and Gynecology, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, Roc

    An Jen Chiang MD, PhD

  3. Department of Biological Sciences, National Sun Yat-sen University, Kaohsiung, Taiwan, Roc

    An Jen Chiang MD, PhD

  4. Department of Pharmacy and Graduate Institute of Pharmaceutical Technology, Tajen University, Pingtung, Taiwan, Roc

    An Jen Chiang MD, PhD

  5. Medical Science and Technology Center, National Sun Yat-sen University, Kaohsiung, Taiwan, Roc

    Jiabin Chen PhD

Authors
  1. Chung Chang PhD
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  2. An Jen Chiang MD, PhD
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  3. Hui-Ching Wang MS
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  4. Wei-An Chen MS
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  5. Jiabin Chen PhD
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Corresponding author

Correspondence to Jiabin Chen PhD.

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Chang, C., Chiang, A.J., Wang, HC. et al. Evaluation of the Time-Varying Effect of Prognostic Factors on Survival in Ovarian Cancer. Ann Surg Oncol 22, 3976–3980 (2015). https://doi.org/10.1245/s10434-015-4493-4

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  • DOI: https://doi.org/10.1245/s10434-015-4493-4

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