The Kaplan-Meier Estimate for Dependent Failure Time Observations
نویسندگان
چکیده
منابع مشابه
The Kaplan Meier Estimate in Survival Analysis
The need for analyzing time to event data arises in a number of applied fields, such as epidemiology, public health and medicine [1]. ‘Time to event’ simply means the time from entry into a study until a subject has a particular outcome. The study that involves time to event can be a cohort study on a specific number of patients or participants which should be followed for a particular time per...
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The Kaplan–Meier estimator is a nonparametric estimator which may be used to estimate the survival distribution function from censored data. The estimator may be obtained as the limiting case of the classical actuarial (life table) estimator, and it seems to have been first proposed by Böhmer [2]. It was, however, lost sight of by later researchers and not investigated further until the importa...
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In some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function, and inferences about it are of our interest. The main result of this paper is that of providing, under certain regularity conditions, the Kaplan-Meier estimator can be expressed as the mean of random variables, with a ...
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The Kaplan-Meier (KM) method is used to analyze 'time-to-event' data. The outcome in KM analysis often includes all-cause mortality, but could also include other outcomes such as the occurrence of a cardiovascular event. The purpose of this article is to explain the basic concepts of the KM method, to provide some guidance regarding the presentation of the KM results and to discuss some importa...
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ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 1994
ISSN: 0047-259X
DOI: 10.1006/jmva.1994.1031