The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables

Azarang, Leyla; Uña‐Álvarez, Jacobo de; Stute, Winfried

doi:10.1080/02331888.2014.960871

Cited by 3 publications

(1 citation statement)

References 14 publications

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“…Alternatively, one could either correct null distribution as Lawrence et al [29] proposed or employ computer intensive methods. Jackknife estimate, that is behind numerous important theoretical results about RMST [21,31,32] could be a natural option or bootstrapping that has a straightforward application in survival analysis [33,34]. Nevertheless, bootstrapping RMST requires extra care.…”

Section: Discussionmentioning

confidence: 99%

A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances

Nemes

Bülow

Gustavsson

2020

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Restricted Mean Survival Time ( R M S T ) experiences a renaissance and is advocated as a model-free, easy to interpret alternative to proportional hazards regression and hazard rates with implication in causal inference. Estimation of R M S T and associated variance is mainly done by numerical integration of Kaplan–Meier curves. In this paper we briefly review the two main alternatives to the Kaplan–Meier method; analysis based on pseudo-observations, and the flexible parametric survival method. Using computer simulations, we assess the efficacy of the three methods compared to a fully parametric approach where the distribution of survival times is known. Thereafter, the three methods are directly compared without any distributional assumption for the survival data. Generally, flexible parametric survival methods outperform both competitors, however the differences are small.

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Section: Discussionmentioning

confidence: 99%

A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances

Nemes

Bülow

Gustavsson

2020

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Resampling based inference for a distribution function using censored ranked set samples

Mahdizadeh

Strzałkowska-Kominiak

2017

Comput Stat

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Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance

Guibert

Planchet

2018

Insurance: Mathematics and Economics

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The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables

Abstract: In this paper the Jackknife estimate of covariance of two Kaplan-Meier integrals with covariates is introduced. Its strong consistency is established under mild conditions. Several applications of the estimator are discussed.

Cited by 3 publications

References 14 publications

A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances

A Brief Overview of Restricted Mean Survival Time Estimators and Associated Variances

Resampling based inference for a distribution function using censored ranked set samples

Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance

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