Sample size calculation is an important factor to evaluate the reliability of the diagnostic test. In this paper, a case study of the clinical diagnostic test of artificial intelligence for identification of liver contrast-enhanced ultrasound was performed to conduct two-category and multi-categories studies. Based on sensitivity and specificity, the sample size was then estimated in combination with the statistical characteristics of disease incidence, test level and one/two-sided test. Eventually, the sample size was corrected by integrating the factors of the proportion of training/test dataset and the dropout rate of cases in the medical image recognition system. Moreover, the application of Sample Size Calculator, MedCalc, PASS, and other software can accelerate sample size calculation and reduce the amount of labor.
The calculation of sample size is a critical component in the design phase of clinical trials incorporating health economic evaluations. A reasonable sample size is essential to ensure the scientific validity and accuracy of trial results. This paper summarizes the sample size calculation methods in the frequentist framework based on two health economic evaluation indicators: incremental cost-effectiveness ratio (ICER) and net benefit and examines these methods in terms of their applicable conditions, advantages, and limitations. The ICER method derives the sample size calculation formula by computing the ratio of incremental cost to incremental effect, while the net benefit method determines the economic viability of interventions by calculating incremental net benefit, subsequently leading to the formulation of the sample size calculation. Furthermore, this paper briefly discusses other sample size calculation methods, such as the classical Bayesian approach and the value of information analysis, providing a reference for calculating sample size in clinical trials with integrated health economic evaluations.
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.
With increasing amount of attention being paid to single case randomized controlled trial (N-of-1 trials), sample size estimation has become an important issue for clinical researchers. This paper mainly introduces the model and hypothesis of N-of-1 trials. Based on the hypothetical model, sample size estimation methods of fixed model and random model are proposed. The premises of the model application, formulas and examples are then given. It is expected in case of conduction N-of-1 trials, the correct methods are used to estimate sample size and improve the research quality of N-of-1 trials.
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.
Network meta-analysis (NMA) is a new statistical approach which comes from head to head meta-analysis. Hence, NMA inherits all methodology challenges of head to head meta-analysis and with increased complexity results due to more intervention treatments involved. The issue of sample size and statistical power in individual trial and head to head meta-analysis is widely emphasized currently; however, they are not been paid due attention in NMA. This article aims to introduce the theory, computational principles and software implementation using examples with step by step approach.
Stepped wedge cluster randomized trials (SW-CRT) is a kind of cluster randomized controlled trial mainly applied in the field of public health policy that has emerged in recent years, which has gradually attracted the attention of workers in the field of health and wellness. At present, this trial method is not widely used at home and abroad, and there are various ways of sample size calculation and statistical analysis. This paper describes the principles, categories, and differences between SW-CRT and traditional randomized controlled trials, and outlines sample size calculation and statistical analysis methods. In general, SW-CRT is characterized by clustering, cross-design, and measurement of results at multiple time points. In terms of sample size calculation, it is necessary to distinguish between clusters with the same and different sizes, and commonly used sample size calculation procedures can be implemented in Stata, R, and SAS software, as well as in fixed online websites, including the "Steppedwedge" program, the "swCRTdesign" program, the "Swdpwr" program, the "CRTpowerdist" program, and the "Shiny CRT Calculator" tool and so on. Based on the design characteristics of SW-CRT, the researcher should also consider the confounding factors of time effects and repeated measurements of result. Therefore, the statistical analysis methods are often based on generalized linear mixed model (GLMM) and generalized estimating equations (GEE). However, most of the above models have been proposed based on cross-sectional studies, there is a lack of statistical methods for queue design and SW-CRT with transitional period now, and more comprehensive methodological exploration is still needed in the future.
The robustness of results of statistical analysis would be altered on the condition of repeated update of traditional meta-analysis and cumulative meta-analysis. In addition, the cumulative meta-analysis lacks estimation of the sample size. While trail sequential analysis (TSA), which introduces group sequential analysis in meta-analysis, can adjust the random error and ultimately estimate the required sample size of the systematic review or meta-analysis. TSA is performed in TSA software. In the present study, we aimed to introduce how to use the TSA software for performing meta-analysis.
