OBJECTIVE: To investigate the effectiveness of problem-based learning (PBL) and case-based learning (CBL) teaching methods in clinical practical teaching in transarterial chemoembolization (TACE) treatment in China.
METHODS: A comprehensive search of PubMed, the Chinese National Knowledge Infrastructure (CNKI) database, the Weipu database and the Wanfang database up to June 2023 was performed to collect studies that evaluate the effectiveness of problem-based learning and case-based learning teaching methods in clinical practical teaching in TACE treatment in China. Statistical analysis was performed by R software (4.2.1) calling JAGS software (4.3.1) in a Bayesian framework using the Markov chain-Monte Carlo method for direct and indirect comparisons. The R packages \"gemtc\", \"rjags\", \"openxlsx\", and \"ggplot2\" were used for statistical analysis and data output.
RESULTS: Finally, 7 studies (five RCTs and two observational studies) were included in the meta-analysis. The combination of PBL and CBL showed more effectiveness in clinical thinking capacity, clinical practice capacity, knowledge understanding degree, literature reading ability, method satisfaction degree, learning efficiency, learning interest, practical skills examination scores and theoretical knowledge examination scores.
CONCLUSIONS: Network meta-analysis revealed that the application of PBL combined with the CBL teaching mode in the teaching of liver cancer intervention therapy significantly improves the teaching effect and significantly improves the theoretical and surgical operations, meeting the requirements of clinical education.

This paper briefly introduces the characteristics, research significance, and global reporting status of effect modification in network Meta-analysis, demonstrates the heterogeneity caused by effect modification in network Meta-analysis, and emphasizes the importance of exploring effect modification in network Meta-analysis. This paper also summarizes the normalized description and analysis strategies of effect modification in network Meta-analysis. Finally, by the case of \"comparison of efficacy of three new hypoglycemic drugs in reducing body weight in type 2 diabetes patients\", this paper demonstrates the realization of subgroup analysis and network Meta-regression in exploring effect modification, summarizes the advantages and disadvantages of the two methods, to provide references for future researchers.
本文简要介绍网状Meta分析中效应修饰作用的特点、研究意义和现状，并通过图表展示了效应修饰作用在网状Meta分析中导致的异质性问题，强调在网状Meta分析中探索效应修饰作用的重要性，总结5个不同场景下效应修饰作用的规范化描述和分析策略。通过“3类新型降糖药降低2型糖尿病患者体重的疗效比较”这一实际案例展示了亚组分析和网状Meta回归在网状Meta分析中对效应修饰作用的探索，并对二者的优缺点及注意事项进行总结，旨在为未来研究者提供参考。.

OBJECTIVE: Little guidance exists on the conduct of randomised clinical trials (RCT) that seek to randomise patients away from standard of care. We sought to test the technique of network meta-analysis (NMA) to ascertain best available evidence for the purposes of informing the ethical evaluation of RCTs under these circumstances. We used the example of RCTs for patients with symptomatic, moderate to severe carotid stenosis that seek to compare surgical intervention plus medical therapy (standard of care) versus medical therapy (less than standard of care).
METHODS: Network meta-analysis of RCTs of adults with symptomatic carotid artery stenosis of 50%-99% who were treated with carotid endarterectomy (CEA), carotid artery stenting (CAS), or medical therapy (MT). The primary outcome was any stroke or death until end of follow-up, and secondary outcome was 30-day risk of ipsilateral stroke/death.
RESULTS: We analysed eight studies, with 7187 subjects with symptomatic moderate/severe stenosis (50%-99%). CEA was more efficacious than MT (HR = 0.82, 95% credible intervals [95% CrI] = 0.73-0.92) and CAS (HR 0.73, 95% CrI = 0.62-0.85) for the prevention of any stroke/death. At 30 days, the odds of experiencing an ipsilateral stroke/death were significantly lower in the CEA group compared to both MT (OR = 0.58, 95% CrI = 0.47-0.72) and CAS (OR = 0.68, 95% CrI = 0.55-0.83).
CONCLUSIONS: Our results support the feasibility of using NMA to assess best available evidence to inform the ethical evaluation of RCTs seeking to randomise patients away from standard of care. Our results suggest that a strong argument is required to ethically justify the conduct of RCTs that seek to randomise patients away from standard of care in the setting of symptomatic moderate to severe carotid stenosis.

Different network meta-analyses (NMAs) on the same topic result in differences in findings. In this review, we investigated NMAs comparing aflibercept with ranibizumab for diabetic macular oedema (DME) in the hope of illuminating why the differences in findings occurred.
Studies were searched for in English and Chinese electronic databases (PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, VIP; see detailed search strategy in the main body). Two independent reviewers systematically screened to identify target NMAs that included a comparison of aflibercept and ranibizumab in patients with DME. The key outcome of interest in this review is the change in best-corrected visual acuity (BCVA), including various ways of reporting (such as the proportion of participants who gain ≥ 10 ETDRS letters at 12 months; average change in BCVA at 12 months).
For the binary outcome of BCVA, different NMAs all agreed that there is no clear difference between the two treatments, while continuous outcomes all favour aflibercept over ranibizumab. We discussed four points of particular concern that are illustrated by five similar NMAs, including network differences, PICO (participants, interventions, comparators, outcomes) differences, different data from the same measures of effect, and differences in what is truly significant.
A closer inspection of each of these trials shows how the methods, including the searches and analyses, all differ, but the findings, although presented differently and sometimes interpreted differently, were similar.

Aim: The presence of two or more publications that report on overlapping patient cohorts poses a challenge for quantitatively synthesizing real-world evidence (RWE) studies. Thus, we evaluated eight approaches for handling such related publications in network meta-analyses (NMA) of RWE studies. Methods: Bayesian NMAs were conducted to estimate the annualized relapse rate (ARR) of disease-modifying therapies in multiple sclerosis. The NMA explored the impact of hierarchically selecting one pivotal study from related publications versus including all of them while adjusting for correlations. Results: When selecting one pivotal study from related publications, the ARR ratios were mostly similar regardless of the pivotal study selected. When including all related publications, there were shifts in the point estimates and the statistical significance. Conclusion: An a priori hierarchy should guide the selection among related publications in NMAs of RWE. Sensitivity analyses modifying the hierarchy should be considered for networks with few or small studies.

Research overlap and duplication is a recognised problem in the context of both pairwise and network systematic reviews and meta-analyses. As a case study, we carried out a scoping review to identify and examine duplicated network meta-analyses (NMAs) in a specific disease setting where several novel therapies have recently emerged: hormone-sensitive metastatic prostate cancer (mHSPC).
MEDLINE and EMBASE were systematically searched, in January 2020, for indirect or mixed treatment comparisons or network meta-analyses of the systemic treatments docetaxel and abiraterone acetate in the mHSPC setting, with a time-to-event outcome reported on the hazard-ratio scale. Eligibility decisions were made, and data extraction performed, by two independent reviewers.
A total of 13 eligible reviews were identified, analysing between 3 and 8 randomised comparisons, and comprising between 1773 and 7844 individual patients. Although the included trials and treatments showed a high degree of overlap, we observed considerable variation between identified reviews in terms of review aims, eligibility criteria and included data, statistical methodology, reporting and inference. Furthermore, crucial methodological details and specific source data were often unclear.
Variation across duplicated NMAs, together with reporting inadequacies, may compromise identification of best-performing treatments. Particularly in fast-moving fields, review authors should be aware of all relevant studies, and of other reviews with potential for overlap or duplication. We recommend that review protocols be published in advance, with greater clarity regarding the specific aims or scope of the project, and that reports include information on how the work builds upon existing knowledge. Source data and results should be clearly and completely presented to allow unbiased interpretation.

Network meta-analysis (NMA) of time-to-event outcomes based on constant hazard ratios can result in biased findings when the proportional hazards (PHs) assumption does not hold in a subset of trials. We aimed to summarize the published non-PH NMA methods for time-to-event outcomes, demonstrate their application, and compare their results.
The following non-PH NMA methods were compared through an illustrative case study in oncology of 4 randomized controlled trials in terms of progression-free survival and overall survival: (1) 1-step or (2) 2-step multivariate NMAs based on traditional survival distributions or fractional polynomials, (3) NMAs with restricted cubic splines for baseline hazard, and (4) restricted mean survival NMA.
For progression-free survival, the PH assumption did not hold across trials and non-PH NMA methods better reflected the relative treatment effects over time. The most flexible models (fractional polynomials and restricted cubic splines) fit better to the data than the other approaches. Estimated hazard ratios obtained with different non-PH NMA methods were similar at 5 years of follow-up but differed thereafter in the extrapolations. Although there was no strong evidence of PH violation for overall survival, non-PH NMA methods captured this uncertainty in the relative treatment effects over time.
When the PH assumption is questionable in a subset of the randomized controlled trials, we recommend assessing alternative non-PH NMA methods to estimate relative treatment effects for time-to-event outcomes. We propose a transparent and explicit stepwise model selection process considering model fit, external constraints, and clinical validity. Given inherent uncertainty, sensitivity analyses are suggested.

Single-case designs (SCDs) are used to evaluate the effects of interventions on individual participants. By repeatedly measuring participants under different conditions, SCD studies focus on individual effects rather than on group summaries. The main limitation of SCDs remains its generalisability to wider populations, reducing the relevance of their findings for practice and policy making. With this limitation in mind, methodological developments for synthesising SCD data from different studies that investigate the same research question have intensified in the past decades (e.g. multilevel modelling). However, these techniques are restricted to comparing two interventions at a time and can only incorporate evidence from studies that directly compare the two treatments of interest. These limitations could be addressed by using network meta-analysis that incorporates both direct and indirect evidence to simultaneously compare multiple interventions. Despite its potential, network meta-analytical techniques have yet to be applied to SCD data. Thus, in this paper, we argue that network meta-analysis can be a valuable tool to synthesise SCD data. We demonstrate the use of network meta-analysis in SCD data using a real dataset, and we conclude by reflecting on the challenges that SCD researchers might face when applying network meta-analysis methods to their data.

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Conducting sensitivity analyses is an integral part of the systematic review process to explore the robustness of results derived from the primary analysis. When the primary analysis results can be sensitive to assumptions concerning a model\'s parameters (e.g., missingness mechanism to be missing at random), sensitivity analyses become necessary. However, what can be concluded from sensitivity analyses is not always clear. For instance, in a pairwise meta-analysis (PMA) and network meta-analysis (NMA), conducting sensitivity analyses usually boils down to examining how \'similar\' the estimated treatment effects are from different re-analyses to the primary analysis or placing undue emphasis on the statistical significance. To establish objective decision rules regarding the robustness of the primary analysis results, we propose an intuitive index, which uses the whole distribution of the estimated treatment effects under the primary and alternative re-analyses. This novel index is compared to an objective threshold to infer the presence or lack of robustness. In the case of missing outcome data, we additionally propose a graph that contrasts the primary analysis results to those of alternative scenarios about the missingness mechanism in the compared arms. When robustness is questioned according to the proposed index, the suggested graph can demystify the scenarios responsible for producing inconsistent results to the primary analysis. The proposed decision framework is immediately applicable to a broad set of sensitivity analyses in PMA and NMA. We illustrate our framework in the context of missing outcome data in both PMA and NMA using published systematic reviews.