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A two‐stage analysis of repeated measurements with dropouts and/or intermittent missing data
Author(s) -
Overall John E.,
Tonidandel Scott
Publication year - 2006
Publication title -
journal of clinical psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.124
H-Index - 119
eISSN - 1097-4679
pISSN - 0021-9762
DOI - 10.1002/jclp.20217
Subject(s) - missing data , statistics , analysis of covariance , dropout (neural networks) , psychology , repeated measures design , stage (stratigraphy) , covariance , maximum likelihood , econometrics , mathematics , computer science , paleontology , machine learning , biology
This article is about a simple two‐stage analysis that utilizes slope coefficients as the dependent variable for testing the significance of difference in mean rates of change in repeated measurement designs with missing data. The ANCOVA test on the doubly weighted slope coefficients provides power comparable to that of more complex maximum likelihood procedures when data are missing completely at random, requires fewer assumptions and is more generally applicable under realistic nonrandom dropout conditions, and most importantly can be readily understood and explained by those who actually do most controlled clinical research. © 2005 Wiley Periodicals, Inc. J Clin Psychol 62: 285–291, 2006.