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Biometrika 1998 85(1):139-149; doi:10.1093/biomet/85.1.139
© 1998 by Biometrika Trust
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Analysis of incomplete repeated measurements with dependent censoring times

Q. YAO, L. J. WEI and J. W. HOGAN

Department of Biostatistics, University of Washington Seattle, Washington 98195, U.S.A. yao{at}biostat.washington.edu
Department of Biostatistics, Harvard University Boston, Massachusetts 021 15, U.S.A. wei{at}biostat.harvard.edu
Center for Statistical Sciences, Brown University Providence, Rhode Island 02912, U.S.A. jhogan{at}at.brown.edu

A frequently encountered complication in repeated-measurements data analysis is that a substantial number of subjects drop out of the study. In this paper, several conceptually simple methods are proposed for analysing incomplete repeated measures when the measurement times vary substantially from subject to subject and the subject's dropout time may depend on the observed and unobserved outcome variables. Numerical studies are conducted to examine the adequacy of our procedures under practical settings. A detailed illustration with an AIDS clinical trial is also provided.

Key Words: Artificial censoring • Log-rank statistic • Missing data • Scale-change model • U-statistic


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