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A test of missing completely at random for longitudinal data with missing observations
Author(s) -
Park Taesung,
Lee SeungYeoun
Publication year - 1997
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/(sici)1097-0258(19970830)16:16<1859::aid-sim593>3.0.co;2-3
Subject(s) - missing data , statistics , longitudinal data , test (biology) , random effects model , econometrics , computer science , mathematics , data mining , medicine , geology , paleontology , meta analysis
Liang and Zeger proposed a generalized estimating equations approach to the analysis of longitudinal data. Their models assume that missing observations are missing completely at random in the sense of Rubin. However, when this assumption does not hold, their analysis may yield biased results. In this paper, we develop a simple and practical procedure for testing this assumption. The proposed procedure is related to that of Park and Davis. © 1997 John Wiley & Sons, Ltd.