Asymptotic Properties of Adaptive Likelihood Weights by Cross-Validation |
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Authors: | Xiaogang Wang |
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Institution: | 1. Department of Mathematics and Statistics , York University , Toronto , Canada stevenw@mathstat.yorku.ca |
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Abstract: | For clinical trials on neurodegenerative diseases such as Parkinson's or Alzheimer's, the distributions of psychometric measures for both placebo and treatment groups are generally skewed because of the characteristics of the diseases. Through an analytical, but computationally intensive, algorithm, we specifically compare power curves between 3- and 7-category ordinal logistic regression models in terms of the probability of detecting the treatment effect, assuming a symmetric distribution or skewed distributions for the placebo group. The proportional odds assumption under the ordinal logistic regression model plays an important role in these comparisons. The results indicate that there is no significant difference in the power curves between 3-category and 7-category response models where a symmetric distribution is assumed for the placebo group. However, when the skewness becomes more extreme for the placebo group, the loss of power can be substantial. |
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Keywords: | Asymptotic normality Consistency Cross-validation Weighted likelihood |
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