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Frailty models for survival data
Lifetime Data Analysis, 1995A frailty model is a random effects model for time variables, where the random effect (the frailty) has a multiplicative effect on the hazard. It can be used for univariate (independent) failure times, i.e. to describe the influence of unobserved covariates in a proportional hazards model.
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Rank Tests for Clustered Survival Data
Lifetime Data Analysis, 2003In a clinical trial, we may randomize subjects (called clusters) to different treatments (called groups), and make observations from multiple sites (called units) of each subject. In this case, the observations within each subject could be dependent, whereas those from different subjects are independent.
Jung, Sin-Ho, Jeong, Jong-Hyeon
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Tests of Independence for Bivariate Survival Data
Biometrics, 1996We propose two test statistics based on the covariance process of the martingale residuals for testing independence of bivariate survival data. The first test statistic takes the supremum over time of the absolute value of the covariance process, and the second test statistic is a time-weighted summary of the process. We derive asymptotic properties of
Shih, Joanna H., Louis, Thomas A.
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Comparison of models for survival data
Statistics in Medicine, 1983AbstractMany mathematical representations are possible both for the frequency distribution of survival time and for the effect on that distribution of explanatory variables. A short review is given of the main types of model, the techniques available for model choice, the consequences of assuming a particular form and methods for assessing goodness of ...
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Adaptive Designs with Survival Data
2016The 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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Estimating Haplotype Effects for Survival Data
Biometrics, 2009SummaryGenetic association studies often investigate the effect of haplotypes on an outcome of interest. Haplotypes are not observed directly, and this complicates the inclusion of such effects in survival models. We describe a new estimating equations approach for Cox's regression model to assess haplotype effects for survival data.
Scheike, Thomas +2 more
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Some Permutation Tests for Survival Data
Biometrics, 1996We 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 of Renal Transplantations in Patients
New England Journal of Medicine, 1965THE tabulations compiled by the Registry in Human Kidney Transplantation sponsored by the National Academy of Sciences and National Research Council and under the careful direction of Murray and his associates1 2 3 permit survival data of patients with renal grafts to be calculated by means of conventional technics.4 The published tables are based on ...
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Flexible Bayesian Modelling for Survival Data
Lifetime Data Analysis, 1998The 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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