Results 241 to 250 of about 4,691,817 (292)
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2005
Abstract Over the last 30 years there has been a rapid development of probability models and statistical analysis for technological and medical survival data. Many studies have been made of the length of life or of periods of remission of animal or human subjects being treated for serious diseases.
Murray Aitkin, Brain Francis, John Hinde
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Abstract Over the last 30 years there has been a rapid development of probability models and statistical analysis for technological and medical survival data. Many studies have been made of the length of life or of periods of remission of animal or human subjects being treated for serious diseases.
Murray Aitkin, Brain Francis, John Hinde
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2009
Abstract Over the last 30 years there has been a rapid development of probability models and statistical analysis for technological and medical survival data. Many studies have been made of the length of life or of periods of remission of animal or human subjects being treated for serious diseases.
Murray Aitkin +3 more
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Abstract Over the last 30 years there has been a rapid development of probability models and statistical analysis for technological and medical survival data. Many studies have been made of the length of life or of periods of remission of animal or human subjects being treated for serious diseases.
Murray Aitkin +3 more
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Super Learner for Survival Data Prediction
The International Journal of Biostatistics, 2020Abstract Survival analysis is a widely used method to establish a connection between a time to event outcome and a set of potential covariates. Accurately predicting the time of an event of interest is of primary importance in survival analysis. Many different algorithms have been proposed for survival prediction.
Golmakani, Marzieh K., Polley, Eric C.
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Analysis of lognormal survival data
Mathematical Biosciences, 1997The failure rate and the mean residual life function (MRLF) of a lognormal distribution are known to be nonmonotonic. It is of interest to study the point at which the monotonicity changes (the change point). In this article we study the change points of the failure rate and the MRLF for the lognormal distribution.
Gupta, Ramesh C. +2 more
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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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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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Multivariate Survival Data With Censoring
2008We define a new class of models for multivariate survival data, in continuous time, based on a number of cumulative hazard functions, along the lines of our family of models for correlated survival data in discrete time [Gross and Huber-Carol (2000, 2002)]. This family is an alternative to frailty and copula models.
Huber, Catherine, Gross, Shulamith
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Canadian Medical Association journal, 1980
This brief review presents a nonmathematical description of the methods used to describe the outcome of patient groups when the time to an event such as death or disease recurrence is of interest. Calculation of the product-limit, actuarial and relative survival curves is described and the underlying principles are explained.
A J, Coldman, J M, Elwood
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This brief review presents a nonmathematical description of the methods used to describe the outcome of patient groups when the time to an event such as death or disease recurrence is of interest. Calculation of the product-limit, actuarial and relative survival curves is described and the underlying principles are explained.
A J, Coldman, J M, Elwood
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Prediction intervals for survival data
Statistics in Medicine, 1983AbstractThis paper concerns large sample prediction intervals for the survival times of a future sample based on an initial sample of censored survival data. Simple procedures are developed for obtaining non‐parametric and exponential prediction intervals for the future sample quantiles; the non‐parametric interval results from inversion of an ...
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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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