Literature DB >> 27860138

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

Xiaoping Xiong1, Jianrong Wu1.   

Abstract

The treatment of cancer has progressed dramatically in recent decades, such that it is no longer uncommon to see a cure or log-term survival in a significant proportion of patients with various types of cancer. To adequately account for the cure fraction when designing clinical trials, the cure models should be used. In this article, a sample size formula for the weighted log-rank test is derived under the fixed alternative hypothesis for the proportional hazards cure models. Simulation showed that the proposed sample size formula provides an accurate estimation of sample size for designing clinical trials under the proportional hazards cure models.
Copyright © 2016 John Wiley & Sons, Ltd.

Entities:  

Keywords:  clinical trial; cure model; log-rank test; proportional hazards model; sample size calculation; survival analysis

Mesh:

Year:  2016        PMID: 27860138      PMCID: PMC5575817          DOI: 10.1002/pst.1790

Source DB:  PubMed          Journal:  Pharm Stat        ISSN: 1539-1604            Impact factor:   1.894


  10 in total

1.  A nonparametric mixture model for cure rate estimation.

Authors:  Y Peng; K B Dear
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  Estimation in a Cox proportional hazards cure model.

Authors:  J P Sy; J M Taylor
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

3.  A SAS macro for parametric and semiparametric mixture cure models.

Authors:  Fabien Corbière; Pierre Joly
Journal:  Comput Methods Programs Biomed       Date:  2006-12-08       Impact factor: 5.428

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

Authors:  M Ewell; J G Ibrahim
Journal:  Lifetime Data Anal       Date:  1997       Impact factor: 1.588

5.  A generalized F mixture model for cure rate estimation.

Authors:  Y Peng; K B Dear; J W Denham
Journal:  Stat Med       Date:  1998-04-30       Impact factor: 2.373

6.  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

7.  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

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

9.  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

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

  10 in total
  3 in total

1.  A clinical trial design using the concept of proportional time using the generalized gamma ratio distribution.

Authors:  Milind A Phadnis; James B Wetmore; Matthew S Mayo
Journal:  Stat Med       Date:  2017-08-16       Impact factor: 2.373

2.  Cancer immunotherapy trial design with cure rate and delayed treatment effect.

Authors:  Jing Wei; Jianrong Wu
Journal:  Stat Med       Date:  2019-11-26       Impact factor: 2.497

3.  Sample size calculation for two-arm trials with time-to-event endpoint for nonproportional hazards using the concept of Relative Time when inference is built on comparing Weibull distributions.

Authors:  Milind A Phadnis; Matthew S Mayo
Journal:  Biom J       Date:  2021-07-17       Impact factor: 1.715

  3 in total

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