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Analysis of recurrent event data with incomplete observation gaps
Author(s) -
Kim YangJin,
Jhun Myoungshic
Publication year - 2007
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/sim.2994
Subject(s) - dropout (neural networks) , complete information , event (particle physics) , inference , computer science , conviction , interval (graph theory) , statistics , mathematics , artificial intelligence , machine learning , mathematical economics , combinatorics , political science , law , physics , quantum mechanics
In analysis of recurrent event data, recurrent events are not completely experienced when the terminating event occurs before the end of a study. To make valid inference of recurrent events, several methods have been suggested for accommodating the terminating event ( Statist. Med. 1997; 16 :911–924; Biometrics 2000; 56 :554–562). In this paper, our interest is to consider a particular situation, where intermittent dropouts result in observation gaps during which no recurrent events are observed. In this situation, risk status varies over time and the usual definition of risk variable is not applicable. In particular, we consider the case when information on the observation gap is incomplete, that is, the starting time of intermittent dropout is known but the terminating time is not available. This incomplete information is modeled in terms of an interval‐censored mechanism. Our proposed method is applied to the study of the Young Traffic Offenders Program on conviction rates, wherein a certain proportion of subjects experienced suspensions with intermittent dropouts during the study. Copyright © 2007 John Wiley & Sons, Ltd.