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Analysis of incomplete longitudinal binary data using multiple imputation
Author(s) -
Li Xiaoming,
Mehrotra Devan V.,
Barnard John
Publication year - 2005
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/sim.2343
Subject(s) - gee , missing data , generalized estimating equation , imputation (statistics) , statistics , binary data , mathematics , estimating equations , binary number , propensity score matching , random effects model , computer science , econometrics , maximum likelihood , medicine , meta analysis , arithmetic
We propose a propensity score‐based multiple imputation (MI) method to tackle incomplete missing data resulting from drop‐outs and/or intermittent skipped visits in longitudinal clinical trials with binary responses. The estimation and inferential properties of the proposed method are contrasted via simulation with those of the commonly used complete‐case (CC) and generalized estimating equations (GEE) methods. Three key results are noted. First, if data are missing completely at random, MI can be notably more efficient than the CC and GEE methods. Second, with small samples, GEE often fails due to ‘convergence problems’, but MI is free of that problem. Finally, if the data are missing at random, while the CC and GEE methods yield results with moderate to large bias, MI generally yields results with negligible bias. A numerical example with real data is provided for illustration. Copyright © 2005 John Wiley & Sons, Ltd.