Multiple testing procedures based on weighted Kaplan-Meier statistics for right-censored survival data

In clinical trials or drug development studies, researchers are often interested in identifying which treatments or dosages are more effective than the standard one. Recently, several multiple testing procedures based on weighted logrank tests have been proposed to compare several treatments with a...

Full description

Saved in:
Bibliographic Details
Published inStatistics in medicine Vol. 24; no. 1; pp. 23 - 35
Main Author Chi, Yunchan
Format Journal Article
LanguageEnglish
Published Chichester, UK John Wiley & Sons, Ltd 15.01.2005
Wiley Subscription Services, Inc
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary:In clinical trials or drug development studies, researchers are often interested in identifying which treatments or dosages are more effective than the standard one. Recently, several multiple testing procedures based on weighted logrank tests have been proposed to compare several treatments with a control in a one‐way layout where survival data are subject to random right‐censorship. However, weighted logrank tests are based on ranks, and these tests might not be sensitive to the magnitude of the difference in survival times against a specific alternative. Therefore, it is desirable to develop a more robust and powerful multiple testing procedure. This paper proposes multiple testing procedures based on two‐sample weighted Kaplan–Meier statistics, each comparing an individual treatment with the control, to determine which treatments are more effective than the control. The comparative results from a simulation study are presented and the implementation of these methods to the prostate cancer clinical trial and the renal carcinoma tumour study are presented. Copyright © 2004 John Wiley & Sons, Ltd.
Bibliography:National Science Council of Taiwan - No. NSC89-2115-M-006-021
ark:/67375/WNG-S1GP03LN-N
istex:E36BDE57B687AE21169C65A28683B5536D461284
ArticleID:SIM1733
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ObjectType-Article-2
content type line 23
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1733