Nonparametric inference on cause‐specific quantile residual life
Identifieur interne : 002751 ( Istex/Corpus ); précédent : 002750; suivant : 002752Nonparametric inference on cause‐specific quantile residual life
Auteurs : Jong-Hyeon Jeong ; Jason P. FineSource :
- Biometrical Journal [ 0323-3847 ] ; 2013-01.
English descriptors
- KwdEn :
- Axillary dissection, Biometrical, Biometrical journal, Breast cancer, Breast cancer patients, Breast deaths, Censoring, Censoring proportion, Error probabilities, Global test, Global test statistic, Gmbh, Jeong, Kgaa, Life expectancy, Life function, Life functions, Mastectomy, Median, Nodal group, Nominal level, Nonparametric, Nsabp, Nsabp data, Nsabp study, Operations research, Percentile, Probability density function, Quantile, Radical mastectomy, Residual, Residual life, Residual life distributions, Residual life subdistributions, Residual lifetimes, Scenario, Statistic, Subdistribution, Subdistributions, Survival data, Survival function, Test statistic, Time point, Total mastectomy, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.
- Teeft :
- Axillary dissection, Biometrical, Biometrical journal, Breast cancer, Breast cancer patients, Breast deaths, Censoring, Censoring proportion, Error probabilities, Global test, Global test statistic, Gmbh, Jeong, Kgaa, Life expectancy, Life function, Life functions, Mastectomy, Median, Nodal group, Nominal level, Nonparametric, Nsabp, Nsabp data, Nsabp study, Operations research, Percentile, Probability density function, Quantile, Radical mastectomy, Residual, Residual life, Residual life distributions, Residual life subdistributions, Residual lifetimes, Scenario, Statistic, Subdistribution, Subdistributions, Survival data, Survival function, Test statistic, Time point, Total mastectomy, Verlag, Verlag gmbh, Weinheim, Weinheim biometrical journal.
Abstract
In medical research, investigators are often interested in inferring time‐to‐event distributions under competing risks. It is well known, however, that the naive approach based on the Kaplan–Meier method to estimate the proportion of cause‐specific events overestimates the true quantity. In this paper, we show that the quantile residual life function, a natural and popular summary measure of survival data, could be also seriously affected by the competing events. An existing two‐sample test statistic for inference on median residual life is modified for competing risks data, which does not involve estimation of the improper probability density function of the subdistribution of cause‐specific events under censoring. Simulation results demonstrate that the test statistic controls the type 1 error probabilities reasonably well. The proposed method is applied to a real data example from a large‐scale phase III breast cancer study.
Url:
DOI: 10.1002/bimj.201100190
Links to Exploration step
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