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.
Objective To systematically investigate the implementation and reporting quality of statistical analysis methods in observational studies for the clinical evaluation of heart failure treatment and management devices, and to provide references for the standardized design and reporting of statistical analyses in future studies within this field. Methods A comprehensive search was conducted in the PubMed database for observational studies published between October 2014 and September 2024 that aimed to evaluate the effectiveness and/or safety of heart failure treatment devices with a control group. Two researchers independently screened the literature and extracted data. The basic characteristics of the included studies and the implementation and reporting features of their statistical analysis methods were analyzed. Results A total of 65 studies were included, comprising 63 (96.92%) cohort studies and 2 (3.08%) case-control studies. Among these, only 39 (60.00%) studies performed multivariable analyses. The median number of confounders included was 9 (IQR 5 to 16), and only 22 (56.41%) studies reported specific methods for identifying confounders. None of the studies considered procedure-related confounders such as operator experience or institutional procedure volume. The most frequently used multivariable method was Cox regression (20, 51.28%), followed by propensity score methods (13, 33.33%). Only 15 (23.08%) studies conducted subgroup analyses and 11 (16.92%) performed sensitivity analyses. Compared with studies published in non-Q1 journals according to the journal citation reports (JCR), studies published in Q1 journals had larger sample sizes and higher proportions of using multivariable analysis. Conclusion Observational studies on the clinical evaluation of heart failure treatment devices exhibit notable deficiencies in the implementation of statistical analysis methods, including inadequate identification and control of confounding factors and low proportions of subgroup and sensitivity analyses. Addressing these methodological limitations in future research will be essential for generating robust, high-quality evidence to inform clinical decision-making.