This paper integrates expert knowledge into the product-limit estimator in two different ways with distinct interpretations. Strong uniform consistency is proved for both cases under certain assumptions on the kind of contamination and the quality of expert information, which sheds light on the ...
Kaplan-Meier估计器与约束的右 цensor数据--递归计算算法 V0.4-2 说明书 Package‘kmc’November22,2022 Type Package Title Kaplan-Meier Estimator with Constraints for Right Censored Data --a Recursive Computational Algorithm Version0.4-2 Date2022-11-21 Maintainer Yifan Yang<***> Description Given c...
2.4.1 Kaplan–Meier Estimator as the Nonparametric Maximum Likelihood Estimator for the Survival In this section we show that the Kaplan–Meier estimator is the nonparametric maximum likelihood estimator for the survival function. We consider event times t1<t2<⋯<tD. Under the assumption of indepen...
Menu location: Analysis_Survival_Kaplan-Meier.This function estimates survival rates and hazard from data that may be incomplete.The survival rate is expressed as the survivor function (S):- where t is a time period known as the survival time, time to failure or time to event (such as ...
By multiplying the weight to each patient, Kaplan-Meier curves can be created for the SCE to outcomes with censoring. The HR is then calculated using a weighted proportional hazard model. For this method, two assumptions need to be introduced to achieve unbiasedness.Results The proposed method ...
Product-limit estimatorKaplan-Meier estimatorrandom censorshipsurvival dataconfidence bandsmean life-timecounting processesmartingalesstochastic integralsweak convergence... Gill,Richard - 《Annals of Statistics》 被引量: 601发表: 1983年 Consistent Estimation Under Random Censorship When Covariables Are Present...
Using a simple example data set and the redistribution algorithm, we illustrate how imputations are made by the KM estimator. We also discuss the assumptions necessary for valid analyses of survival data. Illustrating imputations hidden by the KM estimator helps to clarify these assumptions and ...
We derive process limit distribution results for the Nelson-Aalen estimator\nof a hasard function and for the Kaplan-Meier estimator of a distribution\nfunction, under different dependence assumptions. The data are assumed to be\nright censored observations of a stationary time series. We treat ...
FunctionalcentrallimittheoremLimitdistributionWe derive process limit distribution results for the Nelson–Aalen estimator of a hazard function and for the Kaplan–Meier estimator of a distribution function, under different dependence assumptions. The data are assumed to be right censored observations of a...
Kaplan–Meier (KM) estimator proposed by [21] has been a popular method in time-to-event data (see, e.g., [22,23,24]), as it is a nonparametric approach without stringent model assumptions and describes the survival probabilities directly. KM estimator has also been used in functional da...