As a novel research model that can address multiple research questions within an overall trial structure, master protocol design shares similarities with the clinical research on syndrome-based traditional Chinese medicine in terms of study design. The sample size estimation in master protocol design is characterized by analyzing the subtrials separately and re-estimation at interim analyses. Specific methods include the combination of Simon’s two-stage design and Bayesian hierarchical design that facilitates information borrowing. By drawing on these methods to estimate dynamically and adjust the sample size for each subtrial in a targeted manner, it is expected to provide a feasible approach for the methodological development of sample size estimation in the field of clinical research on syndrome-based traditional Chinese medicine.
ObjectiveTo explore the parameter selection of different sample size estimation methods and the differences in estimation results in single-group target value clinical trials with rate as the outcome evaluation index. MethodsWe conducted a literature review to assess the method of target value selection for single-group target value clinical trials. Then, different values of target value (P0), clinical expected value (P1), and class II error level (β) were set through numerical simulation. Sample size results estimated using different sample size estimation methods were obtained using PASS software. The coefficient of variation, range/mean, analysis of variance and other methods were used to compare the differences between different methods. ResultsAnalysis of the data simulation results showed: when the expected value P1 was fixed, the sample size first decreased rapidly and then decreased slowly along with the increase or decrease of the targeted value P0 on both sides of the sample size limit value. When the difference between P0 and P1 was within 0.15, the ratio before and after correction could be controlled within 0.9. When the difference between P0 and P1 was more than 0.6, the ratio before and after correction approached 0.5. When P0+P1≈1, the ratio of different standard error choices (Sp0 or Sp1) to the estimated sample size was close to 1. When 0.65<P0+P1<1.35, the ratio of different standard error choices (Sp0 or Sp1) to the estimated sample size was about 3:1. When the confidence was 0.8, P0 and P1 were between 0.25 and 0.75 and between 0.20 and 0.80, respectively. We found little difference among the sample sizes estimated using these five methods (CV<0.10, range/mean<0.2). ConclusionThere are some differences among different sample size estimation methods, however, when P0 and P1 values are around 0.5, the differences between different methods are small, suggesting that appropriate methods should be selected for sample size estimation.
Objective To review the current application of sample size estimation in real-world studies (RWS), analyse parameter settings and commonly used methods, and provide methodological guidance for researchers conducting RWS. Methods First, ClinicalTrials.gov was searched to identify RWS with documented sample size calculations. Key information was extracted for descriptive analysis. Secondly, critical parameters and common estimation methods for RWS sample size calculations were systematically reviewed, and strategies were proposed for addressing common challenges. Finally, relevant international reporting standards were interpreted. Results The literature review included 44 clinical trials with a wide range of sample sizes (30 to 30 400 cases). While most studies detailed the sample size estimation process, the parameter settings were often incomplete and many failed to adequately consider the characteristics of real-world data. Therefore, we proposed key parameters for RWS sample size estimation, including effect size, significance level and statistical power. Researchers should also consider issues such as heterogeneity, confounding factors and data quality. This study clarified the essential elements of reporting sample size estimation. Conclusion Methodological guidance for real-world evidence sample size estimation is lacking. We advise researchers to standardise reporting procedures for sample size estimation in future studies and to set parameters reasonably based on research objectives, study design types and data characteristics. This will enhance the transparency and scientific rigour of real-world evidence.
ObjectiveTo explore two methods of sample size estimation in multi-reader multi-case study of radiological diagnostic test and realize them by software. MethodsDemonstration programs were conducted in R software using the Van Dyke dataset, calculating combinations of readers and cases using the OR and DBM methods. These serve as pilot test results for multi-reader multi-case studies, providing a reference for parameter settings in subsequent formal experiments. ResultsWhen the effect size was 0.044, 6 readers and 247 cases could yield 0.80 power, while with an effect size of 0.088, only 6 readers and 44 cases were needed to reach 80.5% power. The sample sizes calculated using the OR method and the DBM method were consistent, and the same sample size calculation results could be obtained through conversion between the two methods. ConclusionFor the estimation of sample size in multi-reader multi-case studies, R software provides a convenient and mature software package for sample size estimation using multi-reader multi-case designs in radiological diagnostic tests, thereby offering a reference for selecting appropriate sample size estimation and statistical analysis methods in radiological diagnostic tests.
