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A Nonparametric Test for Random Dropouts
Author(s) -
Listing Joachim,
Schlittgen Rainer
Publication year - 2003
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.200290010
Subject(s) - dropout (neural networks) , wilcoxon signed rank test , nonparametric statistics , mathematics , statistics , statistical hypothesis testing , missing data , test (biology) , random effects model , computer science , econometrics , machine learning , mann–whitney u test , medicine , paleontology , meta analysis , biology
The problem of dropout is a common one in longitudinal studies. One usually assumes for the analysis that dropout is at random. There are some tests to investigate this assumption. But these tests depend on normally distributed data or lack power, cf. Listing and Schlittgen (1998). We here propose an overall test which combines several Wilcoxon rank sum tests. The alternative hypothesis states that there is a tendency for larger (smaller) values of the target variable the last time the probands show up. The test is applicable with many ties also. It proves to perform well, compared to the test developed for normally distributed data, as well as to a test for completely missing at random which is proposed by Little (1988). An application to real data is given too.