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• W4316495941 abstract "Randomized controlled trials with a pretest-posttest design frequently yield ordered categorical outcome data. Focusing on the estimation of the win probability that a treated participant would have a better score than (or win over) a control participant, we developed methods for analysis and sample size planning for such trials. We exploited the analysis of covariance framework with the dependent variable being individual participants' win fractions at posttest and the covariate being the win fractions at pretest. The win fractions were obtained using the mid-ranks of the ordinal data. Simulation evaluation based on a recent randomized trial on COVID-19 suggests that the methods perform very well. A sample SAS code for data analysis is presented." @default.
• W4316495941 created "2023-01-16" @default.
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• W4316495941 date "2023-03-01" @default.
• W4316495941 modified "2023-10-16" @default.
• W4316495941 title "A rank-based approach to design and analysis of pretest-posttest randomized trials, with application to COVID-19 ordinal scale data" @default.
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• W4316495941 doi "https://doi.org/10.1016/j.cct.2023.107085" @default.
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