Randomized response model versus mixture model in the crossover design for clinical trials

The clinical trials are usually designed with the implicit assumption that data analysis will occur only after the trial is completed. It is a challenging problem if the sponsor wishes to evaluate the drug efficacy in the middle of the study without breaking the randomization codes. In this article,...

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Bibliographic Details
Published inCommunications in statistics. Theory and methods Vol. 45; no. 15; pp. 4611 - 4627
Main Author Wu, Chien-Hua
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 02.08.2016
Taylor & Francis Ltd
Subjects
Baseline categorical logit model
Clinical trials
Crossover design
Crossovers
Data processing
Design analysis
Drugs
Effectiveness
Medical research
Mixture model
Randomization
Randomized response model
Statistics
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Summary:The clinical trials are usually designed with the implicit assumption that data analysis will occur only after the trial is completed. It is a challenging problem if the sponsor wishes to evaluate the drug efficacy in the middle of the study without breaking the randomization codes. In this article, the randomized response model and mixture model are introduced to analyze the data, masking the randomization codes of the crossover design. Given the probability of treatment sequence, the test of mixture model provides higher power than the test of randomized response model, which is inadequate in the example. The paired t-test has higher powers than both models if the randomization codes are broken. The sponsor may stop the trial early to claim the effectiveness of the study drug if the mixture model concludes a positive result.
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ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2014.927490