Skip Navigation

Biometrika 2002 89(4):841-850; doi:10.1093/biomet/89.4.841
© 2002 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Qu, A.
Right arrow Articles by Song, P. X.-K.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Testing ignorable missingness in estimating equation approaches for longitudinal data

Annie Qu1 and Peter X.-K.Song2

1 Department of Statistics, Oregon State University, Corvallis, Oregon 97331, U.S.Aqu{at}stat.orst.edu 2 Department of Mathematics and Statistics, York University, Toronto, Ontario, Canada M3J 1P3 song{at}mathstat.yorku.ca

We address the matter of determining whether or not missing data in longitudinal studies are ignorable with regard to quasilikelihood or estimating equations approaches.This involves testing for whether or not the zero-mean property of estimating equations holds true. Chen & Little (1999) proposed testing for significant differences among parameter estimators calculated from sample subsets with different patterns of missing data, whereas we propose a more unified generalised score-type test. This avoids exhaustive estimation of parameters for each missing-data pattern, testing instead with a single quadratic score test statistic whether or not there is a common parameter under which the means of all the pattern-specific estimating equations are zero. Comparisons are made for the two approaches with both simulations and real data examples.

Key Words: Generalised estimating equation; Goodness-of-fit test; Ignorable missingness; Quadratic inference function; Schizophrenia trial

Received June 2001. Revised February 2002


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Stat Methods Med ResHome page
R. F Potthoff, G. E Tudor, K. S Pieper, and V. Hasselblad
Can one assess whether missing data are missing at random in medical studies?
Statistical Methods in Medical Research, June 1, 2006; 15(3): 213 - 234.
[Abstract] [PDF]

Home page
 JOURNAL OF EDUCATIONAL AND BEHAVIORAL STATISTICSHome page
P. Ghisletta and D. Spini
An Introduction to Generalized Estimating Equations and an Application to Assess Selectivity Effects in a Longitudinal Study on Very Old Individuals
Journal of Educational and Behavioral Statistics, January 1, 2004; 29(4): 421 - 437.
[Abstract] [PDF]


Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.