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Pediatrics In Review, 2012
Aims and objectives. This paper describes when and why survival analysis is used and describes the use and interpretation of the techniques most commonly encountered in medical literature. This is performed using examples taken from core medical journals.Background.
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Aims and objectives. This paper describes when and why survival analysis is used and describes the use and interpretation of the techniques most commonly encountered in medical literature. This is performed using examples taken from core medical journals.Background.
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2007
This chapter introduces some fundamental results in survival analysis. We first describe what is censored failure time data and how to interpret the failure time distribution. Two nonparametric methods for estimating the survival curve, the life table estimator and the Kaplan-Meier estimator, are demonstrated. We then discuss the two-sample problem and
Hongyu, Jiang, Jason P, Fine
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This chapter introduces some fundamental results in survival analysis. We first describe what is censored failure time data and how to interpret the failure time distribution. Two nonparametric methods for estimating the survival curve, the life table estimator and the Kaplan-Meier estimator, are demonstrated. We then discuss the two-sample problem and
Hongyu, Jiang, Jason P, Fine
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Journal of Nephrology, 2004
Abstract Survival analysis is the statistical method for studying the time between entry to a study and a subsequent event. Survival is often the most important outcome in both observational and intervention studies. The analysis of survival, however, is not simple because of a number of factors.
Izhar H, Khan, Gordon J, Prescott
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Abstract Survival analysis is the statistical method for studying the time between entry to a study and a subsequent event. Survival is often the most important outcome in both observational and intervention studies. The analysis of survival, however, is not simple because of a number of factors.
Izhar H, Khan, Gordon J, Prescott
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Diseases of the Colon & Rectum, 1989
Survival analysis is a statistical method used to calculate the probability of an event such as death or relapse of disease occurring in a patient over time. Survival analysis is important in the interpretation of clinical research, and is frequently encountered in the colorectal literature. In this article, the terminology used in survival analysis is
R K, Reznick, C B, Guest
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Survival analysis is a statistical method used to calculate the probability of an event such as death or relapse of disease occurring in a patient over time. Survival analysis is important in the interpretation of clinical research, and is frequently encountered in the colorectal literature. In this article, the terminology used in survival analysis is
R K, Reznick, C B, Guest
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The American Statistician, 2008
In this article, I will discuss life tables and Kaplan-Meier estimators, which are similar to life tables. Then I turn to proportional-hazards models, aka "Cox models." Along the way, I will look at the efficacy of screening for lung cancer, the impact of negative religious feelings on survival, and the efficacy of hormone replacement therapy.
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In this article, I will discuss life tables and Kaplan-Meier estimators, which are similar to life tables. Then I turn to proportional-hazards models, aka "Cox models." Along the way, I will look at the efficacy of screening for lung cancer, the impact of negative religious feelings on survival, and the efficacy of hormone replacement therapy.
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Applicaitons in survival analysis
Journal of Animal Science, 1999Survival or failure time traits such as herd life and days open are both important economically and pose a number of challenges to an analysis based on linear mixed models. The main features of a survival trait are that it is the time until some event occurs, and some of the observations are censored.
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Survival analysis and neural networks
Proceedings of the International Joint Conference on Neural Networks, 2003., 2004A feedforward neural network architecture for survival analysis is presented which generalizes the standard, usually linear, models described in literature. The time variable is embedded in the model and the network is able to extract its interactions with other system features.
ELEUTERI A. +6 more
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Phylogenetic Survival Analysis
Going back in time through a phylogenetic tree makes it possible to evaluate ancestral genomes and assess their potential to acquire key polymorphisms of interest over evolutionary time. Knowledge of this kind may allow for the emergence of key traits to be predicted and pre-empted from currently circulating strains in the future.Arturo, Torres Ortiz, Louis, Grandjean
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