Performance of Selected Nonparametric Tests for Discrete Longitudinal Data Under Different Patterns of Missing Data

Comparison of changes over time of a continuous response variable between treatment groups is often of main interest in clinical trials. When the distributional properties of the continuous response variable are not regular enough, or when the response is discrete, nonparametric techniques have been...

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Bibliographic Details
Published in:Journal of biopharmaceutical statistics 2009-01, Vol.19 (1), p.190-203
Main Authors: Chirwa, T. F., Bogaerts, J., Chirwa, E. D., Kazembe, L. N.
Format: Article
Language:English
Subjects:
Algorithms
Computer Simulation
Humans
Informative
Longitudinal Studies
MAR
MCAR
Nonparametric tests
Patient Compliance - statistics & numerical data
Patient Dropouts - statistics & numerical data
Quality of Life
Randomized Controlled Trials as Topic - statistics & numerical data
Simulations
Statistics, Nonparametric
Wilcoxon
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Summary:Comparison of changes over time of a continuous response variable between treatment groups is often of main interest in clinical trials. When the distributional properties of the continuous response variable are not regular enough, or when the response is discrete, nonparametric techniques have been used. The relative performances of selected repeated measures nonparametric two-sample tests proposed by Wei and Lachin, Koziol, Wei and Johnson, and the adapted Wilcoxon Rank-Sum test are compared through simulations based on quality of life data. The Wilcoxon Rank-Sum test is the most powerful and is not significantly affected by the different patterns of missing data.
ISSN:1054-3406
1520-5711
DOI:10.1080/10543400802536248