Differentiating between borderline and invasive malignancies in ovarian tumors using a multivariate logistic regression model

A clinicopathologic multivariate analysis affecting recurrence of borderline ovarian tumors.

OBJECTIVE To evaluate the risk factors associated with recurrence of borderline ovarian tumors that may be used as evidence of the efficacy of select preventive procedures. METHODS Various clinicopathologic factors of 234 patients with borderline ovarian tumors admitted to our hospital between January 2001 and June 2007 were reviewed. Univariate and multivariate logistic regression models wer...

Bayesian multivariate logistic regression.

Bayesian analyses of multivariate binary or categorical outcomes typically rely on probit or mixed effects logistic regression models that do not have a marginal logistic structure for the individual outcomes. In addition, difficulties arise when simple noninformative priors are chosen for the covariance parameters. Motivated by these problems, we propose a new type of multivariate logistic dis...

Outcomes of High-Risk Pregnancies in Northern Iran: Multivariate Logistic Regression Model

Abstract Background and purpose: High-risk pregnancy is referred to a situation in which mother, fetus or neonate are in higher risk of morbidity or mortality. Because of adverse outcomes of high-risk pregnancies, this study aims to determine these outcomes in the North of Iran.  Materials and Methods: We recruited 803 urban and rural pregnant women in this crosssectional ...

Controversies in borderline ovarian tumors

Borderline ovarian tumors (BOTs) represent about 15% to 20% of all ovarian malignancies and differ from invasive ovarian cancers (IOCs) by many characters. Historically, standard management of BOT is peritoneal washing cytology, hysterectomy, bilateral salpingo-oophorectomy, omentectomy, complete peritoneal resection of macroscopic lesions; in case of mucinous BOTs, appendectomy should be perfo...

Tutorial on Multivariate Logistic Regression