Abstract
This chapter illustrates how two common missing data mechanisms (missing completely at random, and missing at random) influence the Type I familywise error rate of the therapeutic window of a drug using multiple test procedures. A therapeutic window is determined by the minimum dose needed to effectively treat a condition or ailment, and the maximum dose that can be safely administered. The effect of multiple imputation procedures for these missing data mechanisms is also assessed. Simulation results suggest that multiple imputations reduce the familywise error rate of the therapeutic window in the presence of missing data.
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© 2005 Birkhäuser Boston
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Indurkhya, A. (2005). Impact of Missing Data and Imputation Methods on Multiple Test Procedures. In: Balakrishnan, N., Nagaraja, H.N., Kannan, N. (eds) Advances in Ranking and Selection, Multiple Comparisons, and Reliability. Statistics for Industry and Technology. Birkhäuser Boston. https://doi.org/10.1007/0-8176-4422-9_11
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DOI: https://doi.org/10.1007/0-8176-4422-9_11
Publisher Name: Birkhäuser Boston
Print ISBN: 978-0-8176-3232-8
Online ISBN: 978-0-8176-4422-2
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