XU Ye 1,2,3,4 , JIA Yulong 1,2,3,4 , TAN Jing 1,2,3,4 , SUN Xin 1,2,3,4 , XIONG Yiquan 1,2,3,4 , REN Yan 1,2,3,4
  • 1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
  • 2. Center of Integrative Chinese and Western Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
  • 3. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P. R. China;
  • 4. Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, P. R. China;
REN Yan, Email: ren_yan87@163.com
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Repeated measurement quantitative data is a common data type in clinical studies, and is frequently utilized to assess the therapeutic effects of the intervention measures at a single time point in clinical trials. This study clarifies the concepts and calculation methods for sample size estimation of repeated measurement quantitative data, in order to explore the research question of "comparing group differences at a single time point", from three perspectives: the primary research questions in clinical studies, the main statistical analysis methods and the definitions of the primary outcome indicators. Discrepancies in sample sizes calculated by various methods under different correlation coefficients and varying numbers of repeated measurements were examined. The study revealed that the sample size calculation method based on the mixed-effects model or generalized estimating equations (GEE) accounts for both the correlation coefficient and the number of repeated measurements, resulting in the smallest estimated sample size. Secondly, the sample size calculation method based on covariance analysis considers the correlation coefficient and produces a smaller estimated sample size than the t-test. The t-test based sample size calculation method requires an appropriate approach to be selected according to the definition of the primary outcome measure. The alignment between the sample size calculation method, the statistical analysis method and the definition of the primary outcome measure is essential to avoid the risk of overestimation or underestimation of the required sample size.

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