Evidence-based medicine is the methodology of modern clinical research and plays an important role in guiding clinical practice. It has become an integral part of medical education. In the digital age, evidence-based medicine has evolved to incorporate innovative research models that utilize multimodal clinical big data and artificial intelligence methods. These advancements aim to address the challenges posed by diverse research questions, data methods, and evidence sources. However, the current teaching content in medical schools often fails to keep pace with the rapidly evolving disciplines, impeding students' comprehensive understanding of the discipline's knowledge system, cutting-edge theories, and development directions. In this regard, this article takes the opportunity of graduate curriculum reform to incorporate real-world data research, artificial intelligence, and bioinformatics into the existing evidence-based medicine curriculum, and explores the reform of evidence-based medicine teaching in the information age. The aim is to enable students to truly understand the role and value of evidence-based medicine in the development of medicine, while possessing a solid theoretical foundation, a broad international perspective, and a keen research sense, in order to cultivate talents for the development of the evidence-based medicine discipline.
Due to the competition of new drug research and clinical requirement, speeding up drug development and marketing requires faster and more flexible clinical trial design that meets the ethical requirements. Different adaptive designs have emerged in clinical trials of different stages and purposes, for trial efficiency improvement. Adaptive design is more widely used in the field of oncology. Compared with traditional design, adaptive design is more complicated and requires higher level of methodology from researchers. Therefore, implementing adaptive design requires careful consideration and adequate preparation. This paper aims to summarize the design of adaptive methods used in different trial stages so as to provide reference for clinical research designers and implementers.
Evidence synthesis serves as a bridge between clinical practice and the best available evidence. Evidence synthesis based on high-quality randomized controlled trials is generally considered the highest level of evidence, but its external validity is limited. In some scenarios, the inclusion of non-randomized intervention studies (NRSI) in evidence synthesis may further supplement or even replace randomized controlled trial evidence, such as assessing intervention effectiveness and rare events in a broader population to provide more information for health care decision-making. With the rapid development of real-world data and the improvement of statistical analysis methods, real-world evidence, as an important source of evidence for NRSI, has accelerated the development of high-quality NRSI. However, there are numerous challenges in integrating evidence from randomized and non-randomized intervention studies due to selection and confounding biases caused by the lack of randomization. Based on previous studies, this paper systematically examines the current status of integrated randomized and non-randomized intervention studies, including integration premise, timing, methods, and result interpretation, in order to provide references for researchers and policy-makers to correctly use non-randomized research evidence and further promote optimal evidence generation and clinical practice translation.
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.
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.
High-quality randomized controlled trials (RCTs) are regarded as the gold standard for assessing the efficiency and safety of drugs. However, conducting RCTs is expensive and time consumed, and providing timely evidence by RCTs for regulatory agencies and medical decision-makers can be challenging, particularly for new or emerging serious diseases. Additionally, the strict design of RCTs often results in a weakly external validity, making it difficult to provide the evidence of the clinical efficacy and safety of drugs in a broader population. In contrast, large simple clinical trials (LSTs) can expedite the research process and provide better extrapolation and reliable evidence at a lower cost. This article presents the development, features, and distinctions between LSTs and RCTs, as well as special considerations when conducting LSTs, in accordance with literature and guidance principles from regulatory agencies both from China and other countries. Furthermore, this paper assesses the potential of real-world data to bolster the development of LSTs, offering relevant researchers’ insight and guidance on how to conduct LSTs.
To reduce the infection risk of 2019-novel coronavirus and to protect medical staffs, “Graded personal protection scheme for preventing medical staffs from 2019-novel coronavirus infection in West China hospital” was formulated according to the guidance and notice issued by the National Health Commission combined with the actual situation of West China Hospital. This scheme could provide reference for preventing such disease for medical staffs.