ObjectiveTo investigate the current situation of domestic registry studies, and to provide basis for future research.MethodsWorld Health Organization (WHO) registration platform, ClinicalTrials.gov registration platform and other registration platforms were searched to collect the registered registration studies in Mainland China from inception to July 31st, 2018. Two researchers collected and collated data, analyzed by descriptive statistical methods, and then python-igraph package in Python 3.5 was used to draw the network diagram.ResultsA total of 247 studies were retrieved, mainly for disease registry and pharmaceutical product registry. Cohort and case-series were the principally study design. The research focused on chronic diseases such as cardiovascular and cerebrovascular diseases, and research sites were based on the economically developed area, for example, Beijing, Shanghai and Guangdong. The network diagram of study design and disease system shows that cohort research design is widely used in the nervous system and circulatory system; the network diagram of the research sites and disease system found that Beijing mainly studied circulatory and nervous system diseases, Shanghai mainly studied circulatory system diseases, and Guangdong relatively studied more on nervous system and urinary system diseases.ConclusionThe increasing number of registry studies in China reflects the importance of long-term outcome assessment of diseases.
Scientific and rigorous study design could improve the reliability of results of the comparative diagnostic test accuracy studies. The design procedures of a comparative diagnostic test accuracy study included: constructing the clinical questions, identifying the appropriate gold standard, selecting the representative patient sample, calculating the sample size, blindly interpreting and comparing the results of diagnostic tests, and setting up the cut-off value. This paper introduced 5 categories of the designs of comparative diagnostic test accuracy studies: fully paired, partially paired with a random subset, partially paired with a nonrandom subset, unpaired randomized, and unpaired nonrandomized design.
The use of clinical predictive modeling to guide clinical decision-making and thus provide accurate diagnosis and treatment services for patients has become a clinical consensus and trend. However, the models available for clinical use are more limited due to unstandardised research methods and poor quality of evidence. This paper introduces the development process of clinical prediction models from six aspects, data collection, model development, performance evaluation, model validation, model presentation and model updating, as well as the clinical prediction model research report statement and risk of bias assessment tools in order to provide methodological references for domestic researchers.
The multi-attribute utility function (MAUF) is a commonly used method for measuring health utility, characterized by a solid theoretical foundation and operational feasibility. It contributes to reducing the cognitive burden on respondents, caters to the measurement of utility across multiple dimensions, and represents another most widely applied approach beyond traditional econometrics, warranting exploration and application. However, there is currently a lack of comprehensive and systematic research on this method in China, with insufficient practical application experience. Against this backdrop, this paper aims to systematically explore the methodological background of MAUF and the key points of research design for measuring health utility using this method. By drawing on the utility measurement experiences from internationally developed measurement scales such as HUI 1, HUI 2, HUI 3, 15D, AQol-8D, ASUI, HUG-5, TOOL, and ALSUI, it systematically analyzes the critical steps in the practical implementation of the method. It is hoped that this work will comprehensively and clearly elaborate on the practical approaches of MAUF, promote the further development of MAUF in China, and provide methodological references for future studies on health utility measurement based on MAUF.