Comparison of methods for incomplete repeated measures data analysis in small samples |
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| Institution: | 1. The Science of Stress and Health Promotion, School of Medicine, National and Kapodistrian University of Athens and Biomedical Research Foundation, Academy of Athens, Soranou Ephessiou Str. 4, 11527 Athens, Greece;2. Department of Pediatric Neurology, “Aghia Sophia” Children''s Hospital, 11527 Athens, Greece;3. Unit of Developmental and Behavioral Pediatrics, First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, “Aghia Sophia” Children''s Hospital, 11527, Athens, Greece;4. Technological Educational Institute of Western Greece, 30200 Messolonghi, Greece;5. Unit on Clinical and Translational Research in Endocrinology, First Department of Pediatrics, School of Medicine, University of Athens, “Aghia Sophia” Children''s Hospital, 11527 Athens, Greece |
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| Abstract: | This paper presents missing data methods for repeated measures data in small samples. Most methods currently available are for large samples. In particular, no studies have compared the performance of multiple imputation methods to that of non-imputation incomplete analysis methods. We first develop a strategy for multiple imputations for repeated measures data under a cell-means model that is applicable for any multivariate data with small samples. Multiple imputation inference procedures are applied to the resulting multiply imputed complete data sets. Comparisons to other available non-imputation incomplete data methods is made via simulation studies to conclude that there is not much gain in using the computer intensive multiple imputation methods for small sample repeated measures data analysis in terms of the power of testing hypotheses of parameters of interest. |
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