Literature DB >> 24338956

Pseudo-value approach for comparing survival medians for dependent data.

Kwang Woo Ahn1, Franco Mendolia.   

Abstract

Survival median is commonly used to compare treatment groups in cancer-related research. The current literature focuses on developing tests for independent survival data. However, researchers often encounter dependent survival data such as matched pair data or clustered data. We propose a pseudo-value approach to test the equality of survival medians for both independent and dependent survival data. We investigate the type I error and power of the proposed method by a simulation study, in which we examine independent and dependent data. The simulation study shows that the proposed method performs equivalently to the existing methods for independent survival data and performs better for dependent survival data. A study comparing survival median times for bone marrow transplants illustrates the proposed method.
Copyright © 2013 John Wiley & Sons, Ltd.

Entities:  

Keywords:  censored data; clustered data; pseudo-value approach; survival median

Mesh:

Year:  2013        PMID: 24338956      PMCID: PMC3976739          DOI: 10.1002/sim.6072

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  8 in total

1.  Akaike's information criterion in generalized estimating equations.

Authors:  W Pan
Journal:  Biometrics       Date:  2001-03       Impact factor: 2.571

2.  A nonparametric test for equality of survival medians.

Authors:  Mohammad H Rahbar; Zhongxue Chen; Sangchoon Jeon; Joseph C Gardiner; Jing Ning
Journal:  Stat Med       Date:  2012-02-03       Impact factor: 2.373

3.  Regression modeling of competing risks data based on pseudovalues of the cumulative incidence function.

Authors:  John P Klein; Per Kragh Andersen
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

4.  On pseudo-values for regression analysis in competing risks models.

Authors:  Frederik Graw; Thomas A Gerds; Martin Schumacher
Journal:  Lifetime Data Anal       Date:  2008-12-03       Impact factor: 1.588

5.  Working-correlation-structure identification in generalized estimating equations.

Authors:  Lin-Yee Hin; You-Gan Wang
Journal:  Stat Med       Date:  2009-02-15       Impact factor: 2.373

6.  Score test of homogeneity for survival data.

Authors:  D Commenges; P K Andersen
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

7.  Outcomes of pediatric bone marrow transplantation for leukemia and myelodysplasia using matched sibling, mismatched related, or matched unrelated donors.

Authors:  Peter J Shaw; Fangyu Kan; Kwang Woo Ahn; Stephen R Spellman; Mahmoud Aljurf; Mouhab Ayas; Michael Burke; Mitchell S Cairo; Allen R Chen; Stella M Davies; Haydar Frangoul; James Gajewski; Robert Peter Gale; Kamar Godder; Gregory A Hale; Martin B A Heemskerk; John Horan; Naynesh Kamani; Kimberly A Kasow; Ka Wah Chan; Stephanie J Lee; Wing H Leung; Victor A Lewis; David Miklos; Machteld Oudshoorn; Effie W Petersdorf; Olle Ringdén; Jean Sanders; Kirk R Schultz; Adriana Seber; Michelle Setterholm; Donna A Wall; Lolie Yu; Michael A Pulsipher
Journal:  Blood       Date:  2010-07-29       Impact factor: 22.113

8.  Marginal models for clustered time-to-event data with competing risks using pseudovalues.

Authors:  Brent R Logan; Mei-Jie Zhang; John P Klein
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

  8 in total
  1 in total

1.  PSEUDO-VALUE APPROACH FOR CONDITIONAL QUANTILE RESIDUAL LIFETIME ANALYSIS FOR CLUSTERED SURVIVAL AND COMPETING RISKS DATA WITH APPLICATIONS TO BONE MARROW TRANSPLANT DATA.

Authors:  Kwang Woo Ahn; Brent R Logan
Journal:  Ann Appl Stat       Date:  2016-07-22       Impact factor: 2.083

  1 in total

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