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Some Permutation Tests for Survival Data

Biometrics, 1996
We introduce two new classes of tests for censored data. The first tests for association between survival and a covariate and the second tests for equality of survival distributions between K groups. Both tests are permutation tests based on nonparametric test statistics and, unlike the Wald test in the proportional hazards model or the log rank test ...
Sun, Yanqing, Sherman, Michael
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Survival Data Mining

2009
Survival analysis (SA) consists of a variety of methods for analyzing the timing of events and/or the times of transition among several states or conditions. The event of interest can only happen at most once to any individual or subject. Alternate terms to identify this process include Failure Analysis (FA), Reliability Analysis (RA), Lifetime Data ...
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Adaptive Designs with Survival Data

2016
The designs with adaptive sample size modifications have been extended to survival data by several authors including application of the inverse normal method, the Fisher’s combination test approach, and some extensions of the conditional error rate principle.
Gernot Wassmer, Werner Brannath
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Univariate Survival Data

2000
This chapter gives a description of univariate survival data methods. The topic is also described in many other books. Thus, it is not absolutely necessary for persons experienced in survival analysis to read it, but it does contain notation and key results that will be needed later.
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Silencing survival data

Trends in Neurosciences, 2000
M P, Mattson, S W, Barger, R, Dantzer
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Flexible Bayesian Modelling for Survival Data

Lifetime Data Analysis, 1998
The analysis of failure time data often involves two strong assumptions. The proportional hazards assumption postulates that hazard rates corresponding to different levels of explanatory variables are proportional. The additive effects assumption specifies that the effect associated with a particular explanatory variable does not depend on the levels ...
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Examples of Survival Data

1997
The problem of analyzing time to event data arises in a number of applied fields, such as medicine, biology, public health, epidemiology, engineering, economics, and demography. Although the statistical tools we shall present are applicable to all these disciplines, our focus is on applying the techniques to biology and medicine.
John P. Klein, Melvin L. Moeschberger
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Cancer Statistics, 2021

Ca-A Cancer Journal for Clinicians, 2021
Rebecca L Siegel, Kimberly D Miller
exaly  

Cancer statistics, 2022

Ca-A Cancer Journal for Clinicians, 2022
Rebecca L Siegel   +2 more
exaly  

Cancer statistics, 2023

Ca-A Cancer Journal for Clinicians, 2023
Rebecca L Siegel   +2 more
exaly  

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