Literature DB >> 9384622

The large sample distribution of the weighted log rank statistic under general local alternatives.

M Ewell1, J G Ibrahim.   

Abstract

We derive the large sample distribution of the weighted log rank statistic under a general class of local alternatives in which both the cure rates and the conditional distribution of time to failure among those who fail are assumed to vary in the two treatment arms. The analytic result presented here is important to data analysts who are designing clinical trials for diseases such as non-Hodgkins lymphoma, leukemia and melanoma, where a significant proportion of patients are cured. We present a numerical illustration comparing powers obtained from the analytic result to those obtained from simulations.

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Year:  1997        PMID: 9384622     DOI: 10.1023/a:1009690200504

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  8 in total

1.  A comparison of tests of the difference in the proportion of patients who are cured.

Authors:  R Sposto; H N Sather; S A Baker
Journal:  Biometrics       Date:  1992-03       Impact factor: 2.571

2.  Nonparametric estimation and testing in a cure model.

Authors:  E M Laska; M J Meisner
Journal:  Biometrics       Date:  1992-12       Impact factor: 2.571

3.  Semi-parametric estimation in failure time mixture models.

Authors:  J M Taylor
Journal:  Biometrics       Date:  1995-09       Impact factor: 2.571

4.  A linear rank test for use when the main interest is in differences in cure rates.

Authors:  R J Gray; A A Tsiatis
Journal:  Biometrics       Date:  1989-09       Impact factor: 2.571

5.  Designing clinical trials with arbitrary specification of survival functions and for the log rank or generalized Wilcoxon test.

Authors:  J Halpern; B W Brown
Journal:  Control Clin Trials       Date:  1987-09

6.  Cure rate models: power of the logrank and generalized Wilcoxon tests.

Authors:  J Halpern; B W Brown
Journal:  Stat Med       Date:  1987-06       Impact factor: 2.373

7.  Survivorship analysis when cure is a possibility: a Monte Carlo study.

Authors:  A I Goldman
Journal:  Stat Med       Date:  1984 Apr-Jun       Impact factor: 2.373

8.  The use of mixture models for the analysis of survival data with long-term survivors.

Authors:  V T Farewell
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

  8 in total
  11 in total

1.  Flexible Cure Rate Modeling Under Latent Activation Schemes.

Authors:  Freda Cooner; Sudipto Banerjee; Bradley P Carlin; Debajyoti Sinha
Journal:  J Am Stat Assoc       Date:  2007-06-01       Impact factor: 5.033

2.  Modelling geographically referenced survival data with a cure fraction.

Authors:  Freda Cooner; Sudipto Banerjee; A Marshall McBean
Journal:  Stat Methods Med Res       Date:  2006-08       Impact factor: 3.021

3.  The Gini concentration test for survival data.

Authors:  Marco Bonetti; Chiara Gigliarano; Pietro Muliere
Journal:  Lifetime Data Anal       Date:  2009-09-02       Impact factor: 1.588

4.  Single-arm phase II trial design under parametric cure models.

Authors:  Jianrong Wu
Journal:  Pharm Stat       Date:  2015-04-01       Impact factor: 1.894

5.  Analysis of cure rate survival data under proportional odds model.

Authors:  Yu Gu; Debajyoti Sinha; Sudipto Banerjee
Journal:  Lifetime Data Anal       Date:  2010-06-03       Impact factor: 1.588

6.  Sample size determination in shared frailty models for multivariate time-to-event data.

Authors:  Liddy M Chen; Joseph G Ibrahim; Haitao Chu
Journal:  J Biopharm Stat       Date:  2014       Impact factor: 1.051

7.  A novel sample size formula for the weighted log-rank test under the proportional hazards cure model.

Authors:  Xiaoping Xiong; Jianrong Wu
Journal:  Pharm Stat       Date:  2016-11-08       Impact factor: 1.894

8.  Sample size calculation for the proportional hazards cure model.

Authors:  Songfeng Wang; Jiajia Zhang; Wenbin Lu
Journal:  Stat Med       Date:  2012-07-11       Impact factor: 2.373

9.  Power to detect the effects of HIV vaccination in repeated low-dose challenge experiments.

Authors:  Michael G Hudgens; Peter B Gilbert; John R Mascola; Chih-Da Wu; Dan H Barouch; Steven G Self
Journal:  J Infect Dis       Date:  2009-08-15       Impact factor: 5.226

10.  Sample size calculation for testing differences between cure rates with the optimal log-rank test.

Authors:  Jianrong Wu
Journal:  J Biopharm Stat       Date:  2016-02-16       Impact factor: 1.051

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