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A test of missing completely at random for longitudinal data with missing observations

✍ Scribed by Taesung Park; Seung-Yeoun Lee

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 115 KB
Volume: 16
Category: Article
ISSN: 0277-6715
DOI: 10.1002/(sici)1097-0258(19970830)16:16<1859::aid-sim593>3.0.co;2-3

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✦ Synopsis

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.

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