Literature DB >> 22416836

On sample size calculation for comparing survival curves under general hypothesis testing.

Sin-Ho Jung1, Shein-Chung Chow.   

Abstract

The log-rank test is commonly used to test the equivalence of two survival distributions under right censoring. Jung et al. (2005) proposed a modified log-rank test for noninferiority trials and its corresponding sample size calculation. In this article, we extend the use of the modified log-rank test for clinical trials with various types of nonconventional study objectives and propose its sample size calculation under general null and alternative hypotheses. The proposed formula is so flexible that we can specify any survival distributions and accrual pattern. The proposed methods are illustrated with designing real clinical trials. Through simulations, the modified log-rank test and the derived formula for sample size calculation are shown to have satisfactory small sample performance.

Entities:  

Mesh:

Year:  2012        PMID: 22416836      PMCID: PMC3340563          DOI: 10.1080/10543406.2010.550701

Source DB:  PubMed          Journal:  J Biopharm Stat        ISSN: 1054-3406            Impact factor:   1.051


  5 in total

1.  Sample sizes for proportional hazards survival studies with arbitrary patient entry and loss to follow-up distributions.

Authors:  N A Yateman; A M Skene
Journal:  Stat Med       Date:  1992-06-15       Impact factor: 2.373

2.  Sample size computation for two-sample noninferiority log-rank test.

Authors:  Sin-Ho Jung; Sun J Kang; Linda M McCall; Brent Blumenstein
Journal:  J Biopharm Stat       Date:  2005       Impact factor: 1.051

3.  Sample sizes based on the log-rank statistic in complex clinical trials.

Authors:  E Lakatos
Journal:  Biometrics       Date:  1988-03       Impact factor: 2.571

4.  Planning the size and duration of a clinical trial studying the time to some critical event.

Authors:  S L George; M M Desu
Journal:  J Chronic Dis       Date:  1974-02

5.  Sample-size formula for the proportional-hazards regression model.

Authors:  D A Schoenfeld
Journal:  Biometrics       Date:  1983-06       Impact factor: 2.571

  5 in total
  5 in total

1.  Phase II cancer clinical trials for biomarker-guided treatments.

Authors:  Sin-Ho Jung
Journal:  J Biopharm Stat       Date:  2017-11-27       Impact factor: 1.051

2.  CALGB 50604: risk-adapted treatment of nonbulky early-stage Hodgkin lymphoma based on interim PET.

Authors:  David J Straus; Sin-Ho Jung; Brandelyn Pitcher; Lale Kostakoglu; John C Grecula; Eric D Hsi; Heiko Schöder; Leslie L Popplewell; Julie E Chang; Craig H Moskowitz; Nina Wagner-Johnston; John P Leonard; Jonathan W Friedberg; Brad S Kahl; Bruce D Cheson; Nancy L Bartlett
Journal:  Blood       Date:  2018-07-26       Impact factor: 22.113

3.  Design of Phase II Non-inferiority Trials.

Authors:  Sin-Ho Jung
Journal:  Contemp Clin Trials Commun       Date:  2017-05-05

Review 4.  Design and Analysis of Cancer Clinical Trials for Personalized Medicine.

Authors:  Sin-Ho Jung
Journal:  J Pers Med       Date:  2021-05-04

5.  Diagnostic Evidence GAuge of Single cells (DEGAS): a flexible deep transfer learning framework for prioritizing cells in relation to disease.

Authors:  Travis S Johnson; Christina Y Yu; Zhi Huang; Siwen Xu; Tongxin Wang; Chuanpeng Dong; Wei Shao; Mohammad Abu Zaid; Xiaoqing Huang; Yijie Wang; Christopher Bartlett; Yan Zhang; Brian A Walker; Yunlong Liu; Kun Huang; Jie Zhang
Journal:  Genome Med       Date:  2022-02-01       Impact factor: 11.117

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.