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AN OVERVIEW OF STATISTICAL METHODS FOR MULTIPLE FAILURE TIME DATA IN CLINICAL TRIALS
Author(s) -
WEI L. J.,
GLIDDEN DAVID V.
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970430)16:8<833::aid-sim538>3.0.co;2-2
Subject(s) - computer science , inference , multivariate statistics , event (particle physics) , clinical trial , event data , data mining , term (time) , statistics , machine learning , artificial intelligence , medicine , mathematics , physics , pathology , quantum mechanics , analytics
In a long term clinical trial to evaluate a new treatment, quite often each study subject may experience a number of ‘failures’ that correspond to repeated occurrences of the same type of event or events of entirely different natures during his/her follow‐up period. To obtain efficient inference procedures for the therapeutic effect over time, it is desirable to utilize those multiple event times in the analysis. In this article, we review some useful procedures for analysing different kinds of multivariate failure time data. Specifically, we discuss the two‐sample problems and the general regression problems with various survival models. We also give some recommendations of appropriate procedures for each type of multiple event data structure for practical usage. © 1997 by John Wiley & Sons, Ltd. Stat. Med., Vol. 16, 833–839 (1997).