Abstract
In the framework of clinical trials, legal and ethical restrictions make a population model unrealistic for sampling. Randomization tests are a viable alternative to classical inference. Their theoretical properties depend heavily on the random rule used to allocate patients to treatments, so that Ad-Hoc theoretical studies are necessary for each allocation design. In this paper, we obtain theoretical results for randomization tests when the drop-the-loser rule is used.
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Acknowledgements
The authors gratefully acknowledge the comments made by the editors and anonymous referees. This research was partially supported by the project MTM2010-15972.
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Flournoy, N., Galbete, A., Moler, J.A., Plo, F. (2013). Randomization Based Inference for the Drop-The-Loser Rule. In: Ucinski, D., Atkinson, A., Patan, M. (eds) mODa 10 – Advances in Model-Oriented Design and Analysis. Contributions to Statistics. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00218-7_10
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DOI: https://doi.org/10.1007/978-3-319-00218-7_10
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