SEMINARS
|
|
Spring 2006
STATISTICS
COLLOQUIUM
Wednesday, February 1, 2006
3:30-4:00—Refreshments
4:00-5:00—Talk
Yost Hall, Room 101
John Barnard, PhD
Cleveland Clinic Foundation, Department of Quantitative Health Sciences
Analysis of Incomplete Longitudinal Binary Data Using Multiple Imputation
We propose a propensity score-based multiple imputation (MI) method to tackle incomplete missing data resulting from dropouts 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 problems¹, 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.>
|
