Results 1 to 10 of about 32,632 (220)
Hidden Imputations and the Kaplan-Meier Estimator. [PDF]
Am J Epidemiol, 2020 AbstractThe Kaplan-Meier (KM) estimator of the survival function imputes event times for right-censored and left-truncated observations, but these imputations are hidden and therefore sometimes unrecognized by applied health scientists. Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM ...
Cole SR, Edwards JK, Naimi AI, Muñoz A.
europepmc +4 more sources
Interval-specific censoring set adjusted Kaplan-Meier estimator. [PDF]
J Appl Stat, 2023 We propose a non-parametric approach to reduce the overestimation of the Kaplan-Meier (KM) estimator when the event and censoring times are independent. We adjust the KM estimator based on the interval-specific censoring set, a collection of intervals where censored data are observed between two adjacent event times.
Wu Y, Kolassa J.
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JMIR Medical Informatics, 2021 BackgroundPatient data is considered particularly sensitive personal data. Privacy regulations strictly govern the use of patient data and restrict their exchange. However, medical research can benefit from multicentric studies in which patient data from
von Maltitz, Marcel +6 more
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Kaplan–Meier Estimator under Association
Journal of Multivariate Analysis, 1998 This work studies the Kaplan-Meier estimator and the estimation of the hazard function in a model with censored failure times. The true survival times \(T_1,\dots T_n\), with common marginal \(F\), are not assumed mutually independent. They satisfy two different notions of weak dependence: a) They are positively associated, i.e.
Zongwu Cai
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A Generalized Kaplan-Meier Estimator
Annals of Statistics, 1984 A class of nonparametric maximum likelihood estimators of the survival function based on incomplete or right-censored data is derived in the general situation that the time to death and the time to loss (censoring) are not necessarily independent. This is the competing risks problem with two causes of failure.
Robertson, James B., Uppuluri, V. R. R.
exaly +3 more sources
A Combinatoric Approach to the Kaplan-Meier Estimator
Annals of Statistics, 1985 The paper considers the Kaplan-Meier estimator \(F_ n^{KM}\) from a combinatoric viewpoint. Under the assumption that the estimated distribution F and the censoring distribution G are continuous, the combinatoric results are used to show that \(\int | \theta (z)| dF_ n^{KM}(z)\) has expectation not larger than \(\int | \theta (z)| dF(z)\) for any ...
exaly +4 more sources
Conditional Kaplan–Meier Estimator with Functional Covariates for Time-to-Event Data
Stats, 2022 Due to the wide availability of functional data from multiple disciplines, the studies of functional data analysis have become popular in the recent literature. However, the related development in censored survival data has been relatively sparse.
Sudaraka Tholkage +2 more
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Expert Kaplan--Meier estimation [PDF]
, 2022 The 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.
Bladt, Martin, Furrer, Christian
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Using the Kaplan–Meier Estimator to Assess the Reliability of Agricultural Machinery
Agronomy, 2022 Kaplan–Meier analyses can be used in many disciplines, e.g., agricultural engineering. Agricultural machinery and vehicles can be regarded as objects that ‘die’ because, like living creatures, they failed, although after repair they can be used until ...
Karol Durczak +5 more
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BMC Medical Research Methodology, 2020 Background In the presence of dependent censoring even after stratification of baseline covariates, the Kaplan–Meier estimator provides an inconsistent estimate of risk.
Takuya Kawahara +2 more
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