Background & Aims: Renal transplantation is the best therapeutic option for End Stage Renal Disease (ESRD). The aim of this study was to determine the graft survival rate of renal transplantation in patients who have been transplanted from live donor in Shiraz Transplant Research Center, Shiraz, Iran. Methods: In a survival analysis study, organ survival rate after kidney transplantation from...

We show that the nonparametric maximum likelihood estimate (NPMLE) of the regression coefficient from the joint likelihood (of the regression coefficient and the baseline survival) works well for the Cox proportional hazards model with left-truncated and interval-censored data, but the NPMLE may underestimate the baseline survival. Two alternatives are also considered: first, the marginal likel...

GOFCOX is a user-friendly FORTRAN program for assessing the adequacy of the Cox proportional hazards model. The underlying methodology is based on the comparison of the maximum partial likelihood estimator and a weighted parameter estimator. The latter is the root to an estimation equation that assigns varying weights to the individual contributions to the partial likelihood score function. The...

The regular maximum partial likelihood estimator is biased when the covariates in the Cox proportional hazards model are measured with error, unless the measurement errors tend to zero. Although several alternative estimators have been proposed, theoretical justifications for them are lacking. We try to fill this gap by showing that the corrected maximum partial likelihood estimator proposed by...

Survival analysis techniques are often used in clinical and epidemiologic research to model time until event data. Using SAS® system's PROC PHREG, Cox regression can be employed to model time until event while simultaneously adjusting for influential covariates and accounting for problems such as attrition, delayed entry, and temporal biases. Furthermore, by extending the techniques for single ...

Survival analysis endures as an old, yet active research field with applications that spread across many domains. Continuing improvements in data acquisition techniques pose constant challenges in applying existing survival analysis methods to these emerging data sets. In this paper, we present tools for fitting regularized Cox survival analysis models on high-dimensional, massive sample-size (...

We introduce the R package CPHshape, which computes the effect parameters and the nonparametric maximum likelihood estimator of a shape constrained baseline hazard in the Cox proportional hazards model. The functionality of the package is illustrated using reproducible examples which are based on simulated data.