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 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.
ObjectiveTo evaluate the effectiveness of hormone replacement therapy (HRT) combined with Kuntai capsule in the treatment of diminished ovarian reserve (DOR) in the clinical practice based on real world data (RWD). MethodsEmploying a target trial emulation framework, this study utilized electronic medical record data from the Tianjin Regional Healthcare Database. The outcome measures were the changes from baseline in follicle-stimulating hormone (FSH), luteinizing hormone (LH), and estradiol (E2) levels within 3 months of treatment. Inverse probability weighting was used to balance baseline confounders, and the average treatment effect (ATE) was estimated based on weighted linear models. The primary analysis included patients with complete FSH data, and multiple sensitivity analyses were conducted to verify the robustness of the results. ResultsA total of 239 patients with DOR who had at least one sex hormone outcome were included. Among them, 198, 142, and 223 patients had complete data for FSH, LH, and E2, respectively. In the primary analysis population (n=198), 131 patients were in the Western medicine group (HRT alone) and 67 were in the combination group (HRT plus Kuntai capsule). Compared to the Western medicine group, the combination group showed a significantly greater reduction in FSH levels (ATE=?5.92IU/L, 95%CI ?10.73 to ?1.12). In the population with complete E2 data, the combination group demonstrated a significantly greater increase in E2 levels (ATE=61.03pg/mL, 95%CI 33.16 to 88.91). For LH, the difference was not statistically significant between two groups. ConclusionIn real world clinical practice, HRT combined with Kuntai capsule is superior to HRT alone in improving FSH and E2 levels in patients with DOR. These findings are consistent with previous clinical evidence and suggest that the target trial emulation framework holds significant methodological value for evaluating the efficacy of integrated Chinese and Western medicine therapies.
The use of repeated measurement data from patients to improve the classification ability of prediction models is a key methodological issue in the current development of clinical prediction models. This study aims to investigate the statistical modeling approach of the two-stage model in developing prediction models for non-time-varying outcomes using repeated measurement data. Using the prediction of the risk of severe postpartum hemorrhage as a case study, this study presents the implementation process of the two-stage model from various perspectives, including data structure, basic principles, software utilization, and model evaluation, to provide methodological support for clinical investigators.
ObjectiveBased on the requirements of the era of big medical data and discipline development, this study aimed to enhance the clinical research capabilities of medical postgraduates by exploring and evaluating some teaching innovations. MethodsA research-oriented clinical research design course was developed for postgraduate students, focusing on enhancing their clinical research abilities. Innovative teaching content and methods were implemented, and a questionnaire survey was conducted to assess the effectiveness of the teaching innovations among clinical medical master's students. ResultsA total of 699 clinical medical master's students completed the survey questionnaire. 94% of students expressed satisfaction with the course, 96% believed that the relevant knowledge covered in the course met the requirements of clinical research, 94% felt that their research capabilities had improved after completing the course, and 99% believed that the course helped them publish academic papers and complete their master's theses. ConclusionStudents recognized the teaching innovations in the course, which stimulated their initiative and enthusiasm for learning, improved the teaching quality of the course, and enhanced the research capabilities of the students.