ObjectiveThis study investigates the adherence to ethical principles in doctoral dissertations focused on human as the research subject, aiming to provide a foundation for enhancing ethical awareness among medical doctoral candidates. MethodsUtilizing the Chinese database of doctoral dissertations, a total of 1 733 relevant papers published in 2021 were collected. The study compared ethical considerations among double first-class universities, other high-ranking institutions, different university types, various disciplines, diverse training orientations, enrollment types, and medical doctoral dissertations from different regions. ResultsIn 2021, among Chinese medical doctoral dissertations involving human as the research subject, 73.34% mentioned ethical considerations, and 86.27% mentioned informed consent. Dissertations reporting ethical approval descriptions, approval numbers, ethical approvals, and informed consent constituted only 2.19%. Notably, 12.52% of medical doctoral dissertations failed to incorporate ethical considerations and informed consent details in their content. ConclusionThe ethical awareness of medical doctoral candidates in China and the reporting of ethical information in their dissertations require urgent enhancement and improvement.
Mendelian randomization (MR) studies use genetic variants as instrumental variables to explore the effects of exposures on health outcomes. STROBE-MR (strengthening the reporting of observational studies in epidemiology using Mendelian randomization) assists authors in reporting their MR studies clearly and transparently, and helpfully to improve the quality of MR. This paper interpreted the STROBE-MR, aiming to help Chinese scholars better understand, disseminate, and apply it.
Systematic reviews and meta-analyses have become the cornerstone methodologies for integrating multi-source research data and enhancing the quality of evidence. Traditional meta-analyses often demonstrate limitations when handling multiple treatment options. Network meta-analysis (NMA) overcomes these limitations by constructing a network of evidence that encompasses various treatment options, allowing for the simultaneous comparison of both direct and indirect evidence across multiple treatment plans. This provides more comprehensive and precise support for clinical decision-making. This article comprehensively reviews the statistical principles of NMA, its three fundamental assumptions, and the statistical inference framework. It also critically analyzes the mainstream NMA software and packages currently available, such as R (including gemtc, netmeta, rjags, pcnetmeta), Stata (mvmeta, network), WinBUGS, SAS, ADDIS, and various online applications, highlighting their strengths, weaknesses, and suitable scenarios. This analysis provides researchers with a scientific and unified framework for conducting clinical studies and policy-making.
This study comprehensively reviews the theoretical foundations, historical development, practical applications, and potential challenges of network meta-analysis of diagnostic test accuracy (DTA-NMA). DTA-NMA, as a method for evaluating and comparing the accuracy of different diagnostic tests, demonstrates its unique value in improving diagnostic accuracy and optimizing treatment strategies by integrating direct and indirect evidence, providing crucial support for clinical decision-making. However, despite significant progress in methodology and practice, DTA-NMA still faces multiple challenges in implementation, including enhancing research transparency, integrating diverse evidence, accurately assessing bias risks, presenting and interpreting results, and evaluating evidence quality. In the future, further refinement of reporting standards and evidence grading specific to DTA-NMA research will be crucial for the development of this field, facilitating evidence-based efficient medical decision-making and ultimately improving patient outcomes. This study aims to provide scholars conducting DTA-NMA research with reflection and insights to promote the steady development of this field.