Comparison of Cox’s Regression Model and Weibull’ Parametric Model in Evaluating Factors Affecting in First Recurrence of Epithelial Ovarian Cancer
Ovarian cancer is one of the most deadly women's gynecological malignancies in the world, and despite the low prevalence, it accounts for about 5% of all cancer deaths in women. Survival analysis is a regression relationship between a set of variables with a specific outcome, which is considered disease survival or recurrence in medical studies. The aim of this study is to determine the important factors in the first recurrence of patients with epithelial ovarian cancer with two statistics methods. In this study, we review medical records of patients with epithelial ovarian cancer who referred to the oncology and radiotherapy department of Imam Hossein Hospital of Tehran from the beginning of 2007 to the end of 2018. Univariate and multivariate Cox regression, as well as the parametric Weibull method, were used to investigate the factors affecting patients' first recurrence. We perform all calculations with Stata Ver14. Of the 141 patients, 58 patients (41%) had a first recurrence during our follow-up. The mean time to the first recurrence was 24.88 months. Univariate Cox regression and univariate Weibull analysis showed that metastatic tumor and tumor stage had highly significant effects in the first recurrence of epithelial ovarian cancer. In multivariate Cox and multivariate Weibull analysis, the metastatic tumor had a significant effect in the first recurrence of epithelial ovarian cancer. One of the causes of ovarian cancer recurrence may be diagnosis happened at late stages. Therefore, screening programs are needed to reduce illness and death from ovarian cancer.
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|Issue||Vol 58, No 9 (2020)|
|Ovarian cancer Recurrence Predictor Cox regression Weibull parametric method|
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