Randomized controlled trial has been the "gold standard" for clinical trials, in which randomization serves as a fundamental principle of clinical trials and plays an important role in balancing covariates. The allocation probability in traditional design is fixed, while that in adaptive randomization can alter during the experiment according to the specified plan to achieve the purposes of balancing the sample size, maximizing the benefit of patient, or balancing covariates etc. In this study, the adaptive randomization methods applied in clinical trials are discussed to explore their advantages and disadvantages for providing reference for the randomization of clinical trials.
ObjectiveTo examine statistical performance of different rare-event meta-analyses methods.MethodsUsing Monte-Carlo simulation, we set a variety of scenarios to evaluate the performance of various rare-event meta-analysis methods. The performance measures included absolute percentage error, root mean square error and interval coverage.ResultsAcross different scenarios, the absolute percentage error and root mean square error were similar for Bayesian logistic regression model, generalized mixed linear effects model and continuity correction, but the interval coverage was higher with Bayesian logistic regression model. The statistical performances with Mantel-Haenszel method and Peto method were consistently suboptimal across different scenarios.ConclusionsBayesian logistic regression model may be recommended as a preferred approach for rare-event meta-analysis.
With the increasing improvement of real-world evidence as a research system and guideline specification for pre-market registration and post-market regulatory decision support of clinically urgent drug and mechanical products, identifying an approach to ensure the high quality and standards of real-world data and establishing a basis for the generation of real-world evidence is receiving increasing attention and concern from regulatory authorities. Based on the experience of Boao hope city real-world data research pattern and ophthalmic data platform construction, this paper discussed the "source data-database-evidence chain" generation process, data management, and data governance in real-world study from the special features and necessity of multiple sources and heterogeneity of data, multiple research designs, and standardized regulatory requirements, and provided references for further construction of comprehensive research data platforms in the future.
Systematic reviews can provide important evidence support for clinical practice and health decision-making. In this process, literature screening and data extraction are extensively time-consuming procedures. Natural language processing (NLP), as one of the research directions of computer science and artificial intelligence, can accelerate the process of literature screening and data extraction in systematic reviews. This paper introduced the requirements of systematic reviews for rapid literature screening and data extraction, the development of NLP and types of machine learning; and systematically collated the NLP tools for the title and abstract screening, full-text screening and data extraction in systematic reviews; and discussed the problems in the application of NLP tools in the field of systematic reviews and proposed a prospect for its future development.
With the gradual standardization and improvement of the real-world study system, real-world evidence, as a supplement to evidence from classical randomized controlled trials, is increasingly used to evaluate the effectiveness and safety of pharmaceuticals and medical devices. High-quality real-world evidence is not only related to the quality of real-world data, but also depends on the type of study design. Therefore, as one of the important designs for pragmatic clinical trials, the Zelen design has received much attention from investigators in recent years. This paper discussed the implementation processes, subtypes of design, advantages, limitations, statistical concerns, and appropriate application scenarios of the Zelen design, on the basis of published papers, in order to clarify its application value, and to provide references for future research.
With the real-world study (RWS) becoming a hotspot for clinical research, health data collected from routine clinical practice have gained increasing attention worldwide, particularly the data related to the off-label use of drugs, which have been at the forefront of clinical research in recent years. The guidance from the National Medical Products Administration has proposed that real-world evidence (RWE) can be an important consideration in supporting label expansions where randomized controlled trials are unfeasible. Nevertheless, how to use the RWE to support the approval of new or expanded indications remains unclear. This study aims to explore the structured process for the use of RWE in supporting label expansions of approved drugs, and to discuss the key considerations in such process by reviewing the documents from relevant regulatory agencies and publications from public databases, which can inform future directions for studies in this area.
Interrupted time series (ITS) analysis is a quasi-experimental design for evaluating the effectiveness of health interventions. By controlling the time trend before the intervention, ITS is often used to estimate the level change and slope change after the intervention. However, the traditional ITS modeling strategy might indicate aggregation bias when the data was collected from different clusters. This study introduced two advanced ITS methods of handling hierarchical data to provide the methodology framework for population-level health intervention evaluation.
In 2019, the national government issued the document "Implementation Plan for Supporting the Construction of the Boao Lecheng International Medical Tourism Pilot Area", which allowed the use of innovative drugs and medical devices in medical institution of Boao Lecheng. These medical products had been designed to meet urgent clinical requirements and had been approved by regulatory authorities overseas. Through the use of these medical products, real-world data were generated in the routine clinical practice, based on which real-world evidence might be produced for regulatory decision-making by using scientific and rigorous methods. In March 2020, the first medical device product using domestic real-world data was approved, suggesting that the real-world data initiative in Boao Lecheng achieved initial success. This work also provided important experience for promoting the practice of medical device regulatory decision-making based on real-world evidence in China. Here, we shared the preliminary experiences from the study on the first approved medical device product and discussed the issues on developing a real-world data research framework in Boao Lecheng in attempt to offer insights for future studies.
Real-world data studies have experienced rapid development in recent years, however, misunderstandings persist. This paper aims to improve practice and promote standardization by updating the categorization of real-world data, proposing two conceptual frameworks for conducting real-world data studies, developing the concepts of research data infrastructure and clarifying the misconceptions on registry database, and discussing future development.