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A rank-based approach to design and analysis of pretest-posttest randomized trials, with application to COVID-19 ordinal scale data.
Zou, Guangyong; Smith, Emma J; Zou, Lily; Qiu, Shi-Fang; Shu, Di.
  • Zou G; Department of Epidemiology and Biostatistics, Western University, London, Canada; Robarts Research Institute, Western University, London, Canada. Electronic address: gy.zou@robartinc.com.
  • Smith EJ; Department of Epidemiology and Biostatistics, Western University, London, Canada.
  • Zou L; Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, Canada.
  • Qiu SF; Department of Statistics and Data Science, Chongqing University of Technology, Chongqing, China.
  • Shu D; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Department of Pediatrics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA; Clinical Futures, Children's Hospital of Philadelphia, Ph
Contemp Clin Trials ; 126: 107085, 2023 03.
Article in English | MEDLINE | ID: covidwho-2177074
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.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2023 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Experimental Studies / Prognostic study / Randomized controlled trials Limits: Humans Language: English Journal: Contemp Clin Trials Journal subject: Medicine / Therapeutics Year: 2023 Document Type: Article