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Titlebook: Survival Analysis; A Self-Learning Text David G. Kleinbaum,Mitchel Klein Textbook 2012Latest edition Springer Science+Business Media, LLC,

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發(fā)表于 2025-3-23 10:24:14 | 只看該作者
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發(fā)表于 2025-3-23 16:03:52 | 只看該作者
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發(fā)表于 2025-3-23 21:35:46 | 只看該作者
Parametric Survival Models, parametric likelihood is constructed and described in relation to left, right, and interval-censored data. Binary regression is presented as an alternative approach for modeling interval-censored outcomes. The chapter concludes with a discussion of frailty models.
14#
發(fā)表于 2025-3-23 23:19:49 | 只看該作者
1431-8776 eatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data. This text is suitable for researchers and statisticians working in the medical and other life sciences as well as sta
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發(fā)表于 2025-3-24 04:19:16 | 只看該作者
Introduction to Survival Analysis, type of problem addressed by survival analysis, the outcome variable considered, the need to take into account “censored data,” what a survival function and a hazard function represent, basic data layouts for a survival analysis, the goals of survival analysis, and some examples of survival analysi
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發(fā)表于 2025-3-24 07:39:22 | 只看該作者
Parametric Survival Models,s of survival models, called parametric models, in which the distribution of the outcome (i.e., the time to event) is specified in terms of unknown parameters. Many parametric models are acceleration failure time models in which survival time is modeled as a function of predictor variables. We exami
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發(fā)表于 2025-3-24 12:21:00 | 只看該作者
Recurrent Event Survival Analysis,nts.” Modeling this type of data can be carried out using a Cox PH model with the data layout constructed so that each subject has a line of data corresponding to each recurrent event. A variation of this approach uses a stratified Cox PH model, which stratifies on the order in which recurrent event
18#
發(fā)表于 2025-3-24 16:08:21 | 只看該作者
Competing Risks Survival Analysis, contrasts with the topic of the preceding chapter in which subjects could experience more than one event of a given type. When only one of several different types of event can occur, we refer to the probabilities of these events as “competing risks,” which explains the title of this chapter.
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發(fā)表于 2025-3-24 21:46:34 | 只看該作者
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發(fā)表于 2025-3-25 01:04:22 | 只看該作者
Design Issues for Randomized Trials,accrue the study subjects, the time period over which enrolled subjects will be followed, and how to adjust sample size requirements to allow for subjects who might be lost to follow-up and/or who might switch therapies from the one they were originally allocated during the study period.
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