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Likelihood‐based inferences about the mean area under a longitudinal curve in the presence of observations subject to limits of detection
Author(s) -
Chandrasekhar Rameela,
Shi Yi,
Hutson Alan D.,
Wilding Gregory E.
Publication year - 2015
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.1681
Subject(s) - censoring (clinical trials) , missing data , statistics , computer science , econometrics , longitudinal data , mixed model , maximum likelihood , statistical inference , mathematics , data mining
Comparison of groups in longitudinal studies is often conducted using the area under the outcome versus time curve. However, outcomes may be subject to censoring due to a limit of detection and specific methods that take informative missingness into account need to be applied. In this article, we present a unified model‐based method that accounts for both the within‐subject variability in the estimation of the area under the curve as well as the missingness mechanism in the event of censoring. Simulation results demonstrate that our proposed method has a significant advantage over traditionally implemented methods with regards to its inferential properties. A working example from an AIDS study is presented to demonstrate the applicability of our approach. Copyright © 2015 John Wiley & Sons, Ltd.