Morbidity measures in the presence of recurrent composite endpoints
Author(s) -
Scott Martin,
Möcks Joachim,
Givens Sam,
Köhler Walter,
Maurer Jörg,
Budde Michael
Publication year - 2003
Publication title -
pharmaceutical statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.421
H-Index - 38
eISSN - 1539-1612
pISSN - 1539-1604
DOI - 10.1002/pst.20
Subject(s) - statistician , event (particle physics) , clinical trial , computer science , dropout (neural networks) , dependency (uml) , event data , monte carlo method , statistics , data mining , econometrics , machine learning , artificial intelligence , medicine , mathematics , covariate , physics , pathology , quantum mechanics
Abstract The analysis of recurrent event data in clinical trials presents a number of difficulties. The statistician is faced with issues of event dependency, composite endpoints, unbalanced follow‐up times and informative dropout. It is not unusual, therefore, for statisticians charged with responsibility for providing reliable and valid analyses to need to derive new methods specific to the clinical indication under investigation. One method is proposed that appears to have possible advantages over those that are often used in the analysis of recurrent event data in clinical trials. Based on an approach that counts periods of time with events instead of single event counts, the proposed method makes an adjustment for patient time on study and incorporates heterogeneity by estimating an individual per‐patient risk of experiencing a morbid event. Monte Carlo simulations demonstrate that, with use of a real clinical study data, the proposed method consistently outperforms other measures of morbidity. Copyright © 2003 John Wiley & Sons, Ltd.