Given the growing importance of real-world data (RWD) in drug development, efficacy evaluation, and regulatory decision-making, establishing a scientific and systematic data quality regulatory framework has become a strategic priority for global pharmaceutical regulatory authorities. This paper analyzed the EU's advanced practices in RWD quality regulation, compared the RWD quality regulatory systems of China and the EU, and aimed to derive implications for enhancing China's own framework. The EU has made significant progress by promoting the interconnection, intercommunication, and efficient utilization of data resources, implementing a collaborative responsibility mechanism spanning the entire data lifecycle, developing a standardized, tool-based quality assessment system, and facilitating international cooperation and alignment of rules. While China has established an initial regulatory system for RWD quality, it still confronts challenges such as unclear mechanisms for data acquisition and utilization, underdeveloped operational standards, and unclear responsibility delineation. In contrast, by adapting relevant EU experience, China can refine its regulatory framework, establish mechanisms for the interconnection, intercommunication, and efficient utilization of RWD, develop more practical quality assessment toolkits, improve the lifecycle responsibility-sharing mechanism, and promote the alignment of RWD quality regulation with international standards. These enhancements will advance the standardization and refinement of RWD quality regulation in China, ultimately strengthening the scientific rigor and reliability of regulatory decisions.
The research on brain functional mechanism and cognitive status based on brain network has the vital significance. According to a time–frequency method, partial directed coherence (PDC), for measuring directional interactions over time and frequency from scalp-recorded electroencephalogram (EEG) signals, this paper proposed dynamic PDC (dPDC) method to model the brain network for motor imagery. The parameters attributes (out-degree, in-degree, clustering coefficient and eccentricity) of effective network for 9 subjects were calculated based on dataset from BCI competitions IV in 2008, and then the interaction between different locations for the network character and significance of motor imagery was analyzed. The clustering coefficients for both groups were higher than those of the random network and the path length was close to that of random network. These experimental results show that the effective network has a small world property. The analysis of the network parameter attributes for the left and right hands verified that there was a significant difference on ROI2 (P = 0.007) and ROI3 (P = 0.002) regions for out-degree. The information flows of effective network based dPDC algorithm among different brain regions illustrated the active regions for motor imagery mainly located in fronto-central regions (ROI2 and ROI3) and parieto-occipital regions (ROI5 and ROI6). Therefore, the effective network based dPDC algorithm can be effective to reflect the change of imagery motor, and can be used as a practical index to research neural mechanisms.
ObjectiveTo analyze the status of real world studies (RWS) through registration information of the Chinese Clinical Trials Registry (ChiCTR). MethodsThe website of ChiCTR was searched with the real world as the search term to collect relevant registered items in the real world from inception to May 4, 2022. Descriptive analysis method was used. ResultsA total of 642 registered items were included. The median sample size was 482 cases. RWS were mainly observational studies, and the number of intervention studies was increasing year by year. There were 267 studies (41.59%) at the stage of post-marketing drugs or phase Ⅳ clinical trials. Most of the main measures were endpoints (56.23%), and the most commonly used was overall survival (15.79%). 62.15% of the registered projects met the minimum requirements for registration. ConclusionThe number of RWS registered by ChiCTR shows an increasing trend. At present, the research purpose of RWSs is unclear, and the completeness of registered studies and the overall content compliance of the studies need to be improved.
ObjectivesTo analyze the active areas of real world studies on traditional Chinese medicine in China.MethodsCBM, CNKI, WanFang Data, PubMed and EMbase databases were electronically searched to collect real world studies on traditional Chinese medicine in China from inception to 26th April, 2018. The main research contents (research direction, data sources, and research methods) by Excel were extracted, together with the primary information by BICOMS-2 software and production of the network figures by NetDraw 2.084 software.ResultsEventually, 373 real world studies in traditional Chinese medicine were included, in which the initial one was punished in 2008. The top three ranking of authors involved in real world studies on traditional Chinese were Xie Yanming, Zhuang Yan, Yang Wei, and the top three ranking of institutions were Institute of Basic Research in Clinical Medicine of China Academy of Chinese Medical Sciences, School of Statistics of Renmin University of China, and the PLA Navy General Hospital. The amount of related studies in Beijing accounted for 74.26%. It was found that the active areas involve real world, hospital information system, real world study, drug combination, and propensity score method. In terms of the main studied contents on the use of traditional Chinese medicine in the real world, in which the top three were Fufang Kushen injection, Dengzhanxixin injection, and Shuxuetong injection. Digestive system disease, nervous system disease and cardiovascular disease received the highest attention rate, specifically stroke, coronary heart disease, virus hepatitis and hypertension. 58.18% studies were retrospective studies, 49.60% of the information were from the hospital information system, and 56.30% studies used data mining to carry out statistical analysis.ConclusionsMost real world studies on traditional Chinese medicine are based on HIS, and use data mining to study Chinese medicine preparations. The research attention on Chinese medicine is higher than that of the method of diagnosis and treatment, similarly the Chinese medicine preparations is higher than traditional Chinese medicine. In future, attention should be paid to traditional Chinese medicine, prescription and traditional methods of diagnosis and treatment, such as moxibustion and scraping.
Objective To develop an artificial intelligence (AI)-driven lung cancer database by structuring and standardizing clinical data, enabling advanced data mining for lung cancer research, and providing high-quality data for real-world studies. Methods Building on the extensive clinical data resources of the Department of Thoracic Surgery at Peking Union Medical College Hospital, this study utilized machine learning techniques, particularly natural language processing (NLP), to automatically process unstructured data from electronic medical records, examination reports, and pathology reports, converting them into structured formats. Data governance and automated cleaning methods were employed to ensure data integrity and consistency. Results As of September 2024, the database included comprehensive data from 18 811 patients, encompassing inpatient and outpatient records, examination and pathology reports, physician orders, and follow-up information, creating a well-structured, multi-dimensional dataset with rich variables. The database’s real-time querying and multi-layer filtering functions enabled researchers to efficiently retrieve study data that meet specific criteria, significantly enhancing data processing speed and advancing research progress. In a real-world application exploring the prognosis of non-small cell lung cancer, the database facilitated the rapid analysis of prognostic factors. Research findings indicated that factors such as tumor staging and comorbidities had a significant impact on patient survival rates, further demonstrating the database’s value in clinical big data mining. Conclusion The AI-driven lung cancer database enhances data management and analysis efficiency, providing strong support for large-scale clinical research, retrospective studies, and disease management. With the ongoing integration of large language models and multi-modal data, the database’s precision and analytical capabilities are expected to improve further, providing stronger support for big data mining and real-world research of lung cancer.
Compared with traditional clinical trials, the real-world studies set higher requirements on the authenticity (reality), applicability, and timeliness of the evidence obtained. In this paper, we brought up a hypothesis that creating synergies between observational and experimental studies may meet these requirements. And then it was discussed in three aspects including providing evidence, research design and execution. In addition, data analysis facilitated generating efficient and robust evidence which was in support of decision making. Finally, some enlightenment may be offered for Traditional Chinese Medicine evaluation methods based on the synergies of both study types.
As an important policy tool, real-world evidence is the basis for health insurance catalogue adjustment, and relevant policies and regulations have been issued in foreign countries to guide the use of real-world research for health insurance access, but the field of traditional Chinese medicine in China in particular is still in the exploratory stage. Since TCM protocols are widely used in clinical practice and have significant clinical value, this paper takes TCM protocols as an example and systematically constructs a technical pathway based on real-world research to support health insurance access, including clinical needs assessment, basic requirements of protocols, key points for conducting real-world research and evaluating real-world evidence, the process of access, the strategy of access, and the dynamic monitoring of access, with the aim of providing guidance for the application of real-world research in China's health insurance catalogue adjustment. Access to real-world research to provide reference for the application of real-world research in China's health insurance.
Real-world data (RWD) in clinical research on specific categories of medical devices can generate sufficient quality evidence which will be used in decision making. This paper discusses the limitations of traditional randomized controlled trials in clinical research of medical devices, summarizes and analyses the applicable conditions of real-world evidence (RWE) for medical devices, interprets the new FDA guidance document on the characteristics of RWD for medical devices, in order to provide evidence for the use of RWE in medical devices in our country.
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.
Diabetic retinopathy (DR), which is a common complication of diabetic and the main cause of blindness, brings not only a heavy economic burden to society, but also seriously threatens to the patients’ quality of life. Clinical researches on the therapies of DR are active at present, but how to perform a good clinical research with scientific design should be considered with high priority. The randomized controlled trial (RCT) is considered to be the gold standard for evidence-based medicine, but RCT is not always perfect. Limitations still exist in certain circumstance and the conclusions from RCTs also need to be interpreted by an objective point of view before clinical practice. Real world study (RWS) bridges the gap between RCT and clinical practice, in which the data can be easily collected without much cost, and results might be obtained within a short period. However, RWS is also faced with the challenge of not having standardized data and being susceptible to confounding bias. The standardized single disease database for DR and propensity score matching method can provide a wide range of data sources and avoid of bias for RWS in DR.