Literature DB >> 17501936

Nonparametric inference on median residual life function.

Jong-Hyeon Jeong1, Sin-Ho Jung, Joseph P Costantino.   

Abstract

A simple approach to the estimation of the median residual lifetime is proposed for a single group by inverting a function of the Kaplan-Meier estimators. A test statistic is proposed to compare two median residual lifetimes at any fixed time point. The test statistic does not involve estimation of the underlying probability density function of failure times under censoring. Extensive simulation studies are performed to validate the proposed test statistic in terms of type I error probabilities and powers at various time points. One of the oldest data sets from the National Surgical Adjuvant Breast and Bowel Project (NSABP), which has more than a quarter century of follow-up, is used to illustrate the method. The analysis results indicate that, without systematic post-operative therapy, a significant difference in median residual lifetimes between node-negative and node-positive breast cancer patients persists for about 10 years after surgery. The new estimates of the median residual lifetime could serve as a baseline for physicians to explain any incremental effects of post-operative treatments in terms of delaying breast cancer recurrence or prolonging remaining lifetimes of breast cancer patients.

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Year:  2007        PMID: 17501936     DOI: 10.1111/j.1541-0420.2007.00826.x

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  14 in total

1.  Comparing quantile residual life functions by confidence bands.

Authors:  Alba M Franco-Pereira; Rosa E Lillo; Juan Romo
Journal:  Lifetime Data Anal       Date:  2011-11-16       Impact factor: 1.588

2.  Empirical likelihood ratio test for median and mean residual lifetime.

Authors:  Mai Zhou; Jong-Hyeon Jeong
Journal:  Stat Med       Date:  2010-11-05       Impact factor: 2.373

3.  A new flexible dependence measure for semi-competing risks.

Authors:  Jing Yang; Limin Peng
Journal:  Biometrics       Date:  2016-02-24       Impact factor: 2.571

Review 4.  Statistical Methods for Conditional Survival Analysis.

Authors:  Sin-Ho Jung; Ho Yun Lee; Shein-Chung Chow
Journal:  J Biopharm Stat       Date:  2017-11-29       Impact factor: 1.051

5.  Conditional quantile residual lifetime models for right censored data.

Authors:  Cunjie Lin; Li Zhang; Yong Zhou
Journal:  Lifetime Data Anal       Date:  2014-01-17       Impact factor: 1.588

6.  Estimating a Treatment Effect in Residual Time Quantiles under the Additive Hazards Model.

Authors:  Luis Alexander Crouch; Cheng Zheng; Ying Qing Chen
Journal:  Stat Biosci       Date:  2016-10-28

7.  Censored quantile regression for residual lifetimes.

Authors:  Mi-Ok Kim; Mai Zhou; Jong-Hyeon Jeong
Journal:  Lifetime Data Anal       Date:  2011-12-20       Impact factor: 1.588

8.  Inference for the median residual life function in sequential multiple assignment randomized trials.

Authors:  Kelley M Kidwell; Jin H Ko; Abdus S Wahed
Journal:  Stat Med       Date:  2013-11-20       Impact factor: 2.373

9.  On estimation of covariate-specific residual time quantiles under the proportional hazards model.

Authors:  Luis Alexander Crouch; Susanne May; Ying Qing Chen
Journal:  Lifetime Data Anal       Date:  2015-06-10       Impact factor: 1.588

10.  Tumor relapse after pancreatic cancer resection is detected earlier by 18-FDG PET than by CT.

Authors:  Cosimo Sperti; Claudio Pasquali; Sergio Bissoli; Franca Chierichetti; Guido Liessi; Sergio Pedrazzoli
Journal:  J Gastrointest Surg       Date:  2009-09-24       Impact factor: 3.452

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