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.
Objective To investigate the methodological characteristics of observational studies on the correlation between drug exposure during pregnancy and birth defects. Methods The PubMed database was searched from January 1, 2020 to December 31, 2020 to identify observational studies investigating the correlation between drug use during pregnancy and birth defects. Literature screening and data extraction were conducted by two researchers and statistical analysis was performed using R 3.6.1 software. Results A total of 40 relevant articles were identified, of which 8 (20.0%) were published in the four major medical journals and their sub-journals, 21 (42.5%) were conducted in Europe and the United States, and 4 were conducted (10.0%) in China. Cohort studies (30, 75.0%) and case-control studies (10, 25%) were the most commonly used study designs. Sixteen studies (40.0%) did not specify how the databases were linked. Sixteen studies (40.0%) did not report a clear definition of exposure, while 17 studies (42.5%) defined exposure as prescribing a drug that could not be guaranteed to have been taken by the pregnant women, possibly resulting in misclassification bias. Six studies (15.0%) did not report the diagnostic criteria for birth defects and 18 studies (45.0%) did not report the types of birth defects. In addition, 33 studies (82.5%) did not control for confounding factors in the study design, while only 19 studies (47.5%) considered live birth bias. Conclusion Improvements are imperative in reporting and conducting observational studies on the correlation between drug use during pregnancy and birth defects. This includes the methods for linking data sources, definition of exposure and outcomes, and control of confounding factors. Methodological criteria are needed to improve the quality of these studies to provide higher quality evidence for policymakers and researchers.
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.
Evidence synthesis is the process of systematically gathering, analyzing, and integrating available research evidence. The quality of evidence synthesis depends on the quality of the original studies included. Validity assessment, also known as risk of bias assessment, is an essential method for assessing the quality of these original studies. Currently, there are numerous validity assessment tools available, but some of them lack a rigorous development process and evaluation. The application of inappropriate validity assessment tools to assessing the quality of the original studies during the evidence synthesis process may compromise the accuracy of study conclusions and mislead the clinical practice. To address this dilemma, the LATITUDES Network, a one-stop resource website for validity assessment tools, was established in September 2023, led by academics at the University of Bristol, U.K. This Network is dedicated to collecting, sorting and promoting validity assessment tools to improve the accuracy of original study validity assessments and increase the robustness and reliability of the results of evidence synthesis. This study introduces the background of the establishment of the LATITUDES Network, the included validity assessment tools, and the training resources for the use of validity assessment tools, in order to provide a reference for domestic scholars to learn more about the LATITUDES Network, to better use the appropriate validity assessment tools to conduct study quality assessments, and to provide references for the development of validity assessment tools.
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.
In recent years, the rapid advancement of artificial intelligence has profoundly affected societal structures and a broad range of sectors, including healthcare, and is reshaping modes of production and everyday life. Across the evolution of AI technologies, high-quality datasets provide the essential foundation for model training and iterative improvement. Nevertheless, the construction of datasets for AI training remains challenged by ambiguous objective definition and insufficient core technical support, which together hinder the development of high-quality AI datasets. To address these challenges, the China Academy of Information and Communications Technology and Tsinghua University, together with other institutions, jointly released the Guidelines for the Construction of High-Quality Datasets for Artificial Intelligence (hereinafter, the Guidelines), proposing an engineering-oriented, full-lifecycle framework that encompasses data collection, governance, annotation, quality inspection, and operational management. Centered on the Guidelines, this article reviews and interprets their release context, key definitions, and recommended implementation pathways, with the aim of providing a structured reference for researchers seeking to systematically advance high-quality AI dataset development.
In recent years, the concept of population medicine has emerged as a research field that has important implications for healthcare practice and policy decision-making. It specifically aims to improve overall health of patient populations and safety, quality and efficiency of healthcare system. This paper descried the background, definition and characteristics of population medicine, discussed relationship between population medicine and population health and evidence-based medicine. It also introduced Department of Population Medicine at Harvard Medical School as a world-class model in the field of population medicine, discussed the needs and potential strategies for developing population medicine research in China, and briefly outlined the current development of population medicine in China.
Randomised controlled trials are often difficult to conduct for rare diseases, the treatment of life-threatening and serious diseases with no effective treatment options, and single-arm clinical trials are one of the most common choices. However, single-arm clinical trials lack a control group, making it difficult to determine whether the observed treatment effects stem from the drug itself or are influenced by natural disease progression, placebo effects, or selection bias. In recent years, with the continuous accumulation of real-world data, it is possible to use real-world data to construct an external control for single-arm clinical trials. However, it is necessary to carefully consider how to select appropriate external control data to enhance the credibility of the results. Based on the existing studies and relevant laws and regulations in China and abroad, this study expounds the key considerations of using real-world data to construct the external control of single-arm clinical trials on the source data from the perspective of external control setting model, reference intervention selection, data source, and so on. This study could provide references for relevant researchers to conduct similar studies.
With the acceleration of global innovative drug development, selecting safe, effective, and cost-effective products from numerous drugs has posed new challenges for the decision-making process of medical insurance drug access and dynamic updating of insurance directory. Real-world data (RWD) provides a new perspective for evaluation of clinical and economic value of drugs, but there are still uncertainties regarding the scope, quality standards, and evidence categories of RWD that can be used. Based on the current status of domestic and international RWD supporting the assessment of the clinical and economic value of drugs, this paper, in collaboration with national RWD and healthcare experts, has developed the key considerations for using real-world data to evaluate the clinical and economic value of drugs. This paper first clarifies the scope of RWD that can be used to evaluate the clinical and economic value of drugs evaluate; secondly, provides specific requirements and guidance on data attribution, data governance, and quality standards for RWD; finally, summarizes the evidence categories of RWD supporting evaluate the clinical and economic value of drugs evaluate.
Objective To systematically investigate the implementation and reporting quality of statistical analysis methods in observational studies for the clinical evaluation of heart failure treatment and management devices, and to provide references for the standardized design and reporting of statistical analyses in future studies within this field. Methods A comprehensive search was conducted in the PubMed database for observational studies published between October 2014 and September 2024 that aimed to evaluate the effectiveness and/or safety of heart failure treatment devices with a control group. Two researchers independently screened the literature and extracted data. The basic characteristics of the included studies and the implementation and reporting features of their statistical analysis methods were analyzed. Results A total of 65 studies were included, comprising 63 (96.92%) cohort studies and 2 (3.08%) case-control studies. Among these, only 39 (60.00%) studies performed multivariable analyses. The median number of confounders included was 9 (IQR 5 to 16), and only 22 (56.41%) studies reported specific methods for identifying confounders. None of the studies considered procedure-related confounders such as operator experience or institutional procedure volume. The most frequently used multivariable method was Cox regression (20, 51.28%), followed by propensity score methods (13, 33.33%). Only 15 (23.08%) studies conducted subgroup analyses and 11 (16.92%) performed sensitivity analyses. Compared with studies published in non-Q1 journals according to the journal citation reports (JCR), studies published in Q1 journals had larger sample sizes and higher proportions of using multivariable analysis. Conclusion Observational studies on the clinical evaluation of heart failure treatment devices exhibit notable deficiencies in the implementation of statistical analysis methods, including inadequate identification and control of confounding factors and low proportions of subgroup and sensitivity analyses. Addressing these methodological limitations in future research will be essential for generating robust, high-quality evidence to inform clinical decision-making.