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On an exponential bound for the Kaplan–Meier estimator
Lifetime Data Analysis, 2007We review limit theory and inequalities for the Kaplan-Meier Kaplan and Meier (J Am Stat Assoc 53:457-481, 1958) product limit estimator of a survival function on the whole line [Formula: see text] . Along the way we provide bounds for the constant in an interesting inequality due to Biotouzé et al.
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The Jackknife Estimate of a Kaplan-Meier Integral
Biometrika, 1994Summary: We derive an explicit formula for the jackknife estimate of a Kaplan- Meier integral. From this the asymptotic analysis of the jackknifed Kaplan-Meier process becomes straightforward. In a small simulation study it is demonstrated that jackknifing may lead to a considerable reduction of the bias.
Stute, Winfried, Wang, Jane-Ling
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Teaching Statistics, 2016
SummarySurvival is difficult to estimate when observation periods of individuals differ in length. Students imagine sailing the Titanic and then recording whether they "live" or "die". A clever algorithm is performed which results in the Kaplan‐Meier estimate of survival.
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SummarySurvival is difficult to estimate when observation periods of individuals differ in length. Students imagine sailing the Titanic and then recording whether they "live" or "die". A clever algorithm is performed which results in the Kaplan‐Meier estimate of survival.
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Expert Kaplan–Meier estimation
Scandinavian Actuarial Journal, 2023The setting of a right-censored random sample subject to contamination is considered. In various fields, expert information is often available and used to overcome the contamination. This paper integrates expert knowledge into the product-limit estimator in two different ways with distinct interpretations.
Martin Bladt, Christian Furrer
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Giornale italiano di nefrologia : organo ufficiale della Societa italiana di nefrologia, 2005
Kaplan-Meier analysis is largely used in nephrology to estimate a population survival curve from a sample. If patients are followed-up until death, the survival curve can be estimated simply by computing the fraction survival at each time point. Kaplan-Meier analysis allows the estimation of survival over time, even when patients drop out or are ...
G, Tripepi, F, Catalano
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Kaplan-Meier analysis is largely used in nephrology to estimate a population survival curve from a sample. If patients are followed-up until death, the survival curve can be estimated simply by computing the fraction survival at each time point. Kaplan-Meier analysis allows the estimation of survival over time, even when patients drop out or are ...
G, Tripepi, F, Catalano
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2003
Publisher Summary The Kaplan–Meier (K–M) integrals are analogs of empirical integrals when the data are subject to right censoring. They may be viewed as basic statistics to which more complicated quantities may be traced back through linearization.
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Publisher Summary The Kaplan–Meier (K–M) integrals are analogs of empirical integrals when the data are subject to right censoring. They may be viewed as basic statistics to which more complicated quantities may be traced back through linearization.
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Extensions of the kaplan-meier estimator
Communications in Statistics - Simulation and Computation, 1995The Kaplan–Meier estimation (KME) (1958) is a popular nonparametric method in analyzing the survival data. Efron (1967) proposes a re-distribution-to-the-right algorithm for right censored data, which can also be re-distributed from right to left by a method of Dinse (1985).
Wei-Ting Kary Chien, Way Kuo
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Bootstrapping the Kaplan-Meier Estimator
Journal of the American Statistical Association, 1986Abstract Randomly censored data consist of iid pairs of observations (Xi, δi), i = 1, …, n; if δ i = 0, Xi denotes a censored observation, and if δ i = 1, Xi denotes an exact “survival” time, which is the variable of interest. For estimating the distribution F of the survival times, the product-limit estimator proposed by Kaplan and Meier (1958) has ...
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Source code survival with the Kaplan Meier
2011 27th IEEE International Conference on Software Maintenance (ICSM), 2011The presence of dead code may affect the comprehensibility, the readability, and the size of source code so increasing the effort and the cost for maintenance. The elimination of dead code needs a huge cost and effort for recognizing and eliminating code that is not effectively used.
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