Objective To evaluate the quality of clinical studies on dentistry from the Chinese Journals. Methods Clinical studies in Chinese Journal of Conservative Dentistry of 2002 were searched. The quality of the clinical studies on assessment of treatments’ efficacy was evaluated. Results Among 204 related studies from 12 issues, there were 93 (45.58%) restrospective intervention studies, 6 randomized controlled blinded trials (2.94%), 42 randomized trials without blindness (20.58%), 20 controlled trials without randomization (9.80%) and 25 clinical observational studies (12.25%). The statistical analysis showed that 20 studies were with inappropriate methods. Conclusions It is necessary to improve the design and statistical analysis of clinical studies on stomatology in China to produce high-quality research evidence.
ObjectiveTo summarize and explore the application of machine learning models to survival data with non-proportional hazards (NPH), and to provide a methodological reference for large-scale, high-dimensional survival data. MethodsFirst, the concept of NPH and related testing methods were outlined. Then the advantages and disadvantages of machine learning algorithm-based NPH survival analysis methods were summarized based on the relevant literature. Finally, using real-world clinical data, a case study was conducted with two ensemble machine learning models and two deep learning models in survival data with NPH: a study of the risk of death within 30 days in stroke patients in the ICU. ResultsEight commonly used machine learning model-based NPH survival analyses were identified, including five traditional machine learning models such as random survival forest and three deep learning models based on artificial neural networks (e.g., DeepHit). The case study found that the random survival forest model performed the best (C-index=0.773, IBS=0.151), and the permutation importance-based algorithm found that age was the most important characteristic affecting the risk of death in stroke patients. ConclusionSurvival big data in the era of precision medicine presenting NPH are common, and machine learning model-based survival analysis can be used when faced with more complex survival data and higher survival analysis needs.