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

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 th...

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Veröffentlicht in:	Statistics in medicine 1997-08, Vol.16 (16), p.1859-1871
Hauptverfasser:	Park, Taesung, Lee, Seung-Yeoun
Format:	Artikel
Sprache:	eng
Schlagworte:	Aged Biological and medical sciences Child Computerized, statistical medical data processing and models in biomedicine Coronary Disease - prevention & control Data Interpretation, Statistical Effect Modifier, Epidemiologic Female Humans Longitudinal Studies Male Medical sciences Medical statistics Models, Statistical Obesity - epidemiology Regression Analysis Risk Factors Sample Size Urinary Incontinence - epidemiology
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Zusammenfassung:	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.
ISSN:	0277-6715 1097-0258
DOI:	10.1002/(SICI)1097-0258(19970830)16:16<1859::AID-SIM593>3.0.CO;2-3