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Inference methods for saturated models in longitudinal clinical trials with incomplete binary data
Author(s) -
Song James X.
Publication year - 2006
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.233
Subject(s) - missing data , statistics , binary data , point estimation , random effects model , binary number , inference , expectation–maximization algorithm , mathematics , longitudinal data , time point , econometrics , maximum likelihood , computer science , data mining , artificial intelligence , medicine , meta analysis , philosophy , arithmetic , aesthetics
In the longitudinal studies with binary response, it is often of interest to estimate the percentage of positive responses at each time point and the percentage of having at least one positive response by each time point. When missing data exist, the conventional method based on observed percentages could result in erroneous estimates. This study demonstrates two methods of using expectation‐maximization (EM) and data augmentation (DA) algorithms in the estimation of the marginal and cumulative probabilities for incomplete longitudinal binary response data. Both methods provide unbiased estimates when the missingness mechanism is missing at random (MAR) assumption. Sensitivity analyses have been performed for cases when the MAR assumption is in question. Copyright © 2006 John Wiley & Sons, Ltd.