In the process of clinical diagnosis and treatment, restricted mean survival time (RMST), which reflects life expectancy patients up to a specified time, can be used as an appropriate outcome measure. However, RMST only calculates within period after start follow-up may not accurately portray change in patient's over time. The adjusted for patient has already survived defined conditional (cRMST...

The seminal article by D.R. Cox proposing his namesake regression was published in 1972, making survival analysis a youngster in the family of statistics. It is a testimony to the field's success that the need for handbooks reviewing the current state of literature has developed. Survival Analysis is such a book, introducing the field in a manner that is both accessible and practical. The book ...

Survival analysis has been a topic of active statistical research in the past few decades with applications spread across several areas. Traditional applications usually consider data with only a small numbers of predictors with a few hundreds or thousands of observations. Recent advances in data acquisition techniques and computation power have led to considerable interest in analyzing very-hi...

We introduce a semi-parametric Bayesian model for survival analysis. The model is centred on a parametric baseline hazard, and uses a Gaussian process to model variations away from it nonparametrically, as well as dependence on covariates. As opposed to many other methods in survival analysis, our framework does not impose unnecessary constraints in the hazard rate or in the survival function. ...

MOTIVATION The development of methods for linking gene expressions to various clinical and phenotypic characteristics is an active area of genomic research. Scientists hope that such analysis may, for example, describe relationships between gene function and clinical events such as death or recovery. Methods are available for relating gene expression to measurements that are categorized or cont...

Since the pioneering work of Thomas and Grunkemeier (1975) and Owen (1988), the empirical likelihood has been developed as a powerful nonparametric inference approach and become popular in statistical literature. There are many applications of empirical likelihood in survival analysis. In this paper, we present an overview of some recent developments of the empirical likelihood for survival dat...

Survival analysis, often called time-to-event analysis, is a set of methods focused on failure time prediction. Such methods are developed in a variety of research domains like medicine, sociology, economics or engineering, and are used to analyze different types of failures. For example, in business applications the failure may denote bankruptcy or loan default. One of the most important chara...

AIM This paper focuses on the use of censored data in survival analysis. Survival analysis is used most frequently in the case of cancer patients when the study is finished and a number of individuals are still alive. The original article cited was declared recently to be the most cited statistical study in the biomedical area. The goal of this paper is to explain the basic principles and metho...

Background: Lung cancer is one of the most common cancers and the leading cause of death due to cancer in the world. It has the highest mortality rate compared to breast, prostate, and other cancers. Different factors can be effective in the survival of lung cancer patients. The present study has evaluated survival and its related factors. Materials and Methods: The present study was performed...

Introduction: The aim of this study was to predict cancer survival according to the role of demographic determinants, psychological factors and tumor grade. Methods: In this cross-sectional retrospective study, 350 patients with blood, breast, prostate, lung, stomach and colon cancers (173 patients with cancer survival for more than 2 years and 177 patients with cancer survival for 2 years o...