Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...

Expressions are found for the cumulants needed to analyze and improve the accuracy of Cox regression up to order O(n−3/2), and consistent estimators for these are given. In addition, the Bartlett identities are shown to hold for the Cox partial likelihood (and, in a broad sense, for partial likelihood in general), so that the cumulant estimators found can be used to adjust statistics in accorda...

Typically in cohort studies, the time-scale used in Cox regression models is time-on-study, adjusting for age as a covariate. However, age can also be used as the time-scale, where subjects enter the analysis at their baseline age (left-truncation) and exit at their event/censoring age. Using SAS PROC PHREG, we compared five methods using time-on-study and age as the time-scales. We used a subs...

We present an asymptotic exponential bound for the deviation of the survival function estimator of the Cox model. We show that the bound holds even when the proportional hazards assumption does not hold.

Evidence of an association between survival time and date of birth would suggest an etiologic role for a seasonally variable environmental exposure occurring within a narrow perinatal time period. Risk factors that may exhibit seasonal epidemicity include diet, infectious agents, allergens, and antihistamine use. Typically data has been analyzed by simply categorizing births into months or seas...

The Cox (1972) regression model is extended to include discrete and mixed continuous/discrete failure time data by retaining the multiplicative hazard rate form of the absolutely continuous model. Application of martingale arguments to the regression parameter estimating function show the Breslow(1974) estimator to be consistent and asymptotically Gaussian under this model. A computationally co...