Objective To learn if appropriate methods and clinically relevant outcomes were used by analyzing methods of outcome assessment in Chinese acute stroke trials. Method Randomised and quasi-randomised controlled trials on acute stroke published before March 2003 in 6 Chinese neurological journals were included. Types of outcome measures, blinding of outcome assessment, duration of follow up, statistical methods used for data analysis and the significance of the results were evaluated. Types of outcomes were classified as death and four levels: ① Pathology. ② Impairment. ③ Disability. ④ Handicap/quality of life. Results Two hundreds and ten trials were included in this analysis. 57% of the trials used outcomes in pathology level, 77% in impairment level, 12% in disability level and none in the quality of life level. No dichotomous data was analyzed for disability measures. Only 16% of the trials reported number of death but few of them designed death as an outcome measure. Duration of follow up ranged from 3h to 3 years (median 17 d, interquartile range 14-30 d). Most trials did not assess outcomes blindly. Results in 95% of the trials were favorable to the tested interventions. Conclusions In Chinese acute stroke trials, outcome measures used were mainly in pathology and impairment levels and very few trials used functional outcome or death. Blinding of outcome assessment was not commonly used. The average duration of follow up was short.
理论上,临床实践与临床对照试验的方法相互不断影响.恰当的试验设计应该能反映医生和病人的想法,反之,熟悉临床试验可以改进日常临床实践的方法.换句话说,每一种方法学的原则都源于临床实践.例如:随机化(带有建设性地怀疑是否形成真正的对照),独立性(对药品公司善意的不信任),知情同意(与患者共同分担不确定性),Ⅰ型错误(对单个试验不要错误地乐观),Ⅱ型错误(对单个试验不要错误地悲观),选择正确的判效指标(相关性比精确性重要),意向性治疗原则(重实效的分析)以及亚组分析的危险["我的前一个患者综合征"(the my-last-patient syndrome)].
To show that a new drug is better than, as good as, or no worse than that of a known effective drug. Theoretically, it is necessary to confirm the efficacy of a treatment, but the current practice of clinical trial suggests that there exists many problems in its confirmation including the objectives of clinical investigation vary based on the fact that more and more clinical trials use active controls. Applied statistical methods have to adapt to these changes. In this paper, we illustrated some statistical issues of confirming efficacy in clinical trials, including its conditions, the determination of clinical margin, the forms of the null and alternative hypothesis and confidence intervals, the choice of endpoints and some miscellaneous considerations. We bly suggests that it is necessary to make biostatisticians and clinical trialists understand the importance of using the right statistical methods when investigating clinical trials. We also think these methods introduced in the paper may provide some help in trial design and evaluation.
Artificial intelligence (AI) for science (AI4S) technology, the AI technology for scientific research, has shown tremendous potential and influence in the field of healthcare, redefining the research paradigm of medical science under the guidance of computational medicine. We reviewed the main technological trends of AI4S in reshaping healthcare paradigm: knowledge-driven AI, leveraging extensive literature mining and data integration, emerges an important tool for understanding disease mechanisms and facilitating novel drug development; data-driven AI, delving into clinical and human-related omics data, unveils individual variances and disease mechanisms, and further establishes patient-centric digital twins to guide drug development and personalized medicine. Meanwhile, based on authentic patient digital twin models, adaptable strategies are employed to further propel the development of "e-drugs" that mimic the authentic mechanisms. These digital twins of drugs are evaluated for drug efficacy and safety through large-scale cloud-based virtual clinical trials, and followed by rationally designed real-world clinical trials, thus notably reducing drug development costs and enhancing success rates. Despite encountering challenges such as data scale, quality control, model interpretability, the transition from science insights to engineering solutions, and regulatory hurdles, we anticipate the integration of AI4S technology to revolutionize drug development and clinical practices. This transformation brings revolutionary changes to the medical field, offering novel opportunities and challenges for the development of medical science, and more importantly, providing necessary but personalized healthcare solutions for humankind.
Coronavirus disease (COVID-19) is currently a world-wide major public health event. Since study sites of clinical trials are primarily at healthcare institutions and investigators are primarily clinicians, the epidemic inevitably has a huge impact on a large number of ongoing clinical trials. The proper implementation of clinical trials in key aspects and the quality of core data collection will greatly influence the validity of the final results. In this paper, we analyzed the potential impact of the outbreak of a new epidemic infectious diseases on the clinical trials from seven aspects, which involves the selection of study participants, randomization, blinding, implementation of intervention measures, follow-up of primary outcomes, safety monitoring and project management. Corresponding countermeasures were put forward.
Poor compliance in clinical studies is a risk factor leading to bias of results of clinical research. However, while the subject compliance has received extensive attention, researcher compliance has not been paid enough attention. The problem of researcher compliance runs through the whole process of clinical research. How to control and evaluate the researcher compliance is the key problem in clinical research. Based on the current situation of poor compliance of clinical researchers, this paper summaried the information of five different dimensions that affects the researcher compliance in clinical research, clarified the relevant factors that may affect the researcher compliance in the process of clinical research, and analyzed the influence of the factors related to the researcher compliance on the quality control of clinical research, hence establishing a foundation for further research on control strategies and evaluation techniques of researcher compliance.
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.
目前临床研究协调员（CRC）在完成高质量的临床试验中所扮演的角色越来越受到国内药物临床试验机构及药物申办者的广泛关注。作为临床试验过程中重要的一员，CRC承担着协调及管理临床试验项目的任务，具有“项目管理助手、后勤保障支持”的特点。四川大学华西医院国家药物临床试验机构在中医专业新药临床试验过程中，尝试配备CRC并在实际工作中取得了一定成效，同时也积累了实践经验。现就该机构中医专业新药临床试验过程中，CRC的运行机制和具体工作职责进行简要介绍，为各药物临床试验机构的建设和管理、药物临床试验的质量和整体水平的提高提供参考。