Journal of Clinical Epidemiology最新文献

{"title":"Sample Size and Geographical Region Predict Effect Heterogeneity in Psychotherapy Research for Depression: A Meta-Epidemiological Study.","authors":"Paula Kuper, Clara Miguel, Pim Cuijpers, Christian Apfelbacher, Claudia Buntrock, Eirini Karyotaki, Antonia A Sprenger, Mathias Harrer","doi":"10.1016/j.jclinepi.2025.111779","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111779","url":null,"abstract":"<p><strong>Objective: </strong>Depression is a very prevalent and burdensome disease, for which the efficacy of psychological treatments has been extensively studied in randomised controlled trials (RCTs). Meta-analytic evidence in this field is often heavily limited due to heterogeneity, meaning a broad dispersion of true treatment effects and high uncertainty when predicting future outcomes. Causes for this heterogeneity are largely unclear, and cannot be directly examined using conventional meta-analytic methods. Using newly introduced location-scale models, this study is the first to examine direct predictors of between-study heterogeneity in depression psychotherapy trials.</p><p><strong>Study design and setting: </strong>We used a large meta-analytic database containing RCTs on the efficacy of depression psychotherapy. We included studies in all age groups, comparing psychotherapy to control conditions. Risk of bias (RoB) was assessed with the 'Cochrane Collaboration Risk of Bias Tool' (Version 1). Univariate analyses were used to explore associations of study-level variables with treatment effect heterogeneity, and multi-model selection to investigate the predictive effect of all variables simultaneously.</p><p><strong>Results: </strong>We included 539 RCTs with 607 comparisons, with 35% showing low overall RoB. Higher heterogeneity was found in studies with high RoB and lower sample sizes; heterogeneity varied depending on the geographical region where trials were conducted. Based on multi-model selection, the most important predictors of effect heterogeneity were geographical region, baseline sample size, and RoB. These predictors were also significant after model-averaging.</p><p><strong>Conclusion: </strong>Our study shows that several study-level variables predict the heterogeneity of treatment effects in psychotherapy research, and thus their predictability across different thcontexts. To enhance the robustness of pooled effects, meta-analysts may consider restricting their synthesis to methodologically rigorous studies only. Our findings also indicate that the assumption of constant heterogeneity in 'traditional' random-effects analyses might often be violated, making sensitivity analyses imperative.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111779"},"PeriodicalIF":7.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789334","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing major comorbidity indices as predictors of all-cause mortality in the Veterans Affairs healthcare system.","authors":"Hind A Beydoun, Dorota Szymkowiak, May A Beydoun, Neil Nixdorff, Robert Brunner, Jack Tsai","doi":"10.1016/j.jclinepi.2025.111778","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111778","url":null,"abstract":"<p><strong>Objective: </strong>The Charlson Comorbidity Index (CCI), the Elixhauser Comorbidity Index (ECI), and the Functional Comorbidity Index (FCI) are validated clinical measures of comorbidity, but direct comparisons between these measures have rarely been studied especially in high-risk patient populations, such as homeless individuals. The U.S. Department of Veterans Affairs (VA) offers large patient samples to compare these comorbidity measures as predictors of mortality using administrative and clinical records. We examined CCI, ECI, and FCI scores among veterans seeking VA healthcare services, including those experiencing homelessness, and compare their predictive value in relation to all-cause mortality risk.</p><p><strong>Study design and setting: </strong>Several VA databases from 2017-2021 were retrospectively linked and 4,701,711 U.S. veterans [308,553 with homelessness and 4,393,158 without homelessness] were evaluated over a median follow-up of 4.1 years, yielding 917,921 recorded deaths. Regression models were constructed, and Harrell's Concordance Statistic (HCS) was calculated that assessed the ability of z-transformed comorbidity scores to discriminate 'high-risk' vs. 'low-risk' groups of patients for mortality risk, after adjustment for demographic and clinical characteristics.</p><p><strong>Results: </strong>In adjusted models, ECI (HCS: 0.76-0.77) and CCI (HCS: 0.75-0.76) were better able to discriminate 'high-risk' vs. 'low-risk' groups than FCI (HCS: 0.72-0.75) among homeless and non-homeless veterans. Compared to ECI and CCI, FCI was more strongly associated with homelessness.</p><p><strong>Conclusion: </strong>CCI and ECI may be more predictive of all-cause mortality risk than FCI, although FCI may be a useful measure of functioning in homeless populations.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111778"},"PeriodicalIF":7.3,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143789333","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How should we assess trustworthiness of randomized controlled trials?","authors":"Jack Wilkinson ,&nbsp;David Tovey","doi":"10.1016/j.jclinepi.2025.111670","DOIUrl":"10.1016/j.jclinepi.2025.111670","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111670"},"PeriodicalIF":7.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143015506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ʻMethodological systematic review recommends improvements to conduct and reporting when meta-analyzing interrupted time series studies’. Journal of Clinical Epidemiology 145 (2022) 55–69","authors":"Elizabeth Korevaar ,&nbsp;Amalia Karahalios ,&nbsp;Simon L. Turner ,&nbsp;Andrew B. Forbes ,&nbsp;Monica Taljaard ,&nbsp;Allen C. Cheng ,&nbsp;Jeremy M. Grimshaw ,&nbsp;Lisa Bero ,&nbsp;Joanne E. McKenzie","doi":"10.1016/j.jclinepi.2025.111706","DOIUrl":"10.1016/j.jclinepi.2025.111706","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111706"},"PeriodicalIF":7.3,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Seven principles for integrating health equity considerations in the practice guideline enterprise","authors":"Omar Dewidar ,&nbsp;Andrea J. Darzi ,&nbsp;Shahab Sayfi ,&nbsp;Jordi Pardo Pardo ,&nbsp;Vivian Welch ,&nbsp;Grace C. Wright ,&nbsp;Elie A. Akl ,&nbsp;Joanne Khabsa ,&nbsp;Jennifer S. Lin ,&nbsp;Rebecca L. Morgan ,&nbsp;Kevin Pottie ,&nbsp;Janice Tufte ,&nbsp;Jana Khawandi ,&nbsp;Xiaoqin Wang ,&nbsp;Oyekola Oloyede ,&nbsp;Tamara Lotfi ,&nbsp;Xiaomei Yao ,&nbsp;Ana Carolina Pereira Nunes Pinto ,&nbsp;Yuan Chi ,&nbsp;Reem A. Mustafa ,&nbsp;Peter Tugwell","doi":"10.1016/j.jclinepi.2025.111777","DOIUrl":"10.1016/j.jclinepi.2025.111777","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Health equity aims to provide all individuals with equal and fair opportunities to achieve optimal health. Practice guidelines can play a pivotal role in advancing health equity; yet, few organizations use tools to systematically integrate health equity considerations. Thus, it is important to establish a foundation for practical tools to support the systematic integration of health equity considerations. This manuscript proposes principles for the integration of health equity considerations in the practice guideline enterprise.</div></div><div><h3>Methods</h3><div>In the process of developing an equity extension for the GIN-McMaster guideline development checklist, we established a diverse advisory group of guideline developers, patients, members of the public experiencing inequities, health equity researchers, and guideline developers. We formulated the principles informed by a methodological review of guideline handbooks and iterative discussions between working group members.</div></div><div><h3>Results</h3><div>We identified the following seven principles for integrating health equity considerations into the practice guideline enterprise: (1) articulating health equity, (2) a priori planning for considering health equity, (3) selection and engagement of individuals with lived experiences of inequities, (4) equity in evidence synthesis, (5) developing equity-informed recommendations, (6) inclusive knowledge mobilization, and (7) evaluating the impact of health equity considerations. We elaborated on the importance of the principles using published examples and mapped them to the different phases of the guideline development process.</div></div><div><h3>Conclusion</h3><div>Guideline developers should adhere to these principles in the development of guidelines and health equity guideline development and appraisal tools. These principles are the foundational concepts for developing health equity extension items for the GIN-McMaster guideline development checklist.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111777"},"PeriodicalIF":7.3,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143744473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emulating an existing trial of treatments for prostate cancer using real-world data: challenges and lessons learned","authors":"Caroline Chesang ,&nbsp;Linda D. Sharples ,&nbsp;Christen M. Gray ,&nbsp;Julie Nossiter ,&nbsp;Jan van der Meulen ,&nbsp;Thomas E. Cowling ,&nbsp;Ruth H. Keogh","doi":"10.1016/j.jclinepi.2025.111767","DOIUrl":"10.1016/j.jclinepi.2025.111767","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;If randomized controlled trials can be successfully emulated using real-world data (RWD), confidence in the validity of RWD for estimating treatment effects for questions that have not been assessed in trials increases. We used routinely collected administrative and clinical national linked datasets from England to emulate the PR07 trial for high-risk prostate cancer patients, which compared the effects of radiotherapy added to hormone therapy (RT+HT) within 8 weeks of randomization (the “grace period”) and hormone therapy (HT) only on all-cause mortality. We highlight methodological choices required and challenges encountered in emulating this trial.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;Patients diagnosed with prostate cancer from 2014 to 2020 were identified from the routine national linked datasets. Diagnosis was taken as the time zero. As few patients initiated radiotherapy within 8 weeks of diagnosis, we considered target trials with grace periods of 4-6 months. Estimands of interest were hazard ratios (HRs) and survival probabilities over 7 years. The cloning-censoring-and-weighting (CCW) approach was used to control for measured confounding and to allow for the grace period. We also used an extension (the “landmark-CCW” approach), in which we consider several time-origins post-diagnosis, enabling us to use a grace period of 8 weeks as in PR07.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 2,690 patients were eligible for inclusion in the emulated trial. The CCW analysis using a grace period of 6 months gave an estimated HR of 0.48 (95% confidence interval [CI]: 0.34–0.60) and 7-year survival estimates of 80.7% (95% CI: 74.3–87.0) for the RT+HT strategy and 65.6% (95% CI: 62.8–68.1) for HT only strategy, and corresponding risk difference of 15.1% (95% CI: 11.5–18.9). The corresponding HR from the landmark-CCW approach was 0.58 (95% CI: 0.51–0.65) and with survival estimates of 80.7% (95% CI: 77.7–83.8) for RT+HT strategy and 69.8% (95% CI: 68.2–71.4) for the HT only strategy, and a risk difference of 10.9% (95% CI: 6.3–15.9).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;Our findings from the emulated trial using RWD are broadly consistent with those from PR07, with RT+HT estimated to result in better survival compared to HT only. However, the findings were not replicated exactly, with PR07 reporting an HR of 0.77 (95% CI: 0.61–0.98) over 7 years of follow-up. Differences may be in part due to challenges in defining time zero and allowing for a treatment grace period of the same duration as in PR07. Our study considered ways in which these challenges can be addressed, and our findings affirm the utility of RWD for estimating treatment effects.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;Clinical trials are the best way to test whether treatments work, but they are expensive, take years to complete, and focus on narrow research questions. If we can use real-world data (RWD) such as patient he","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111767"},"PeriodicalIF":7.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Across 73 meta-analyses mortality improvements are uncommon with newer interventions in adult cardiac surgery","authors":"Austin Parish ,&nbsp;George Tolis Jr. ,&nbsp;John P.A. Ioannidis","doi":"10.1016/j.jclinepi.2025.111764","DOIUrl":"10.1016/j.jclinepi.2025.111764","url":null,"abstract":"<div><h3>Objectives</h3><div>We aimed to assess how often randomized controlled trials (RCTs) in adult cardiac surgery found significant mortality benefits for newer interventions vs older ones, whether observed treatment effect estimates changed over time and whether RCTs and nonrandomized observational studies gave similar results.</div></div><div><h3>Methods</h3><div>We searched journals likely to publish systematic reviews on adult cardiac surgery for meta-analyses of mortality outcomes and that included at least 1 RCT, with or without observational studies. Relative treatment effect sizes were evaluated overall, over time, and per study design.</div></div><div><h3>Results</h3><div>A total of 73 meta-analysis comparisons (824 study outcomes on mortality, 519 from RCTs, 305 from observational studies) were eligible. The median mortality effect size was 1.00, IQR 0.54–1.30 (1.00 among RCTs, 0.91 among observational studies, <em>P</em> = .039). Four RCTs and six observational studies reached <em>P</em> &lt; .005 favoring newer interventions. Two meta-analyses reached <em>P</em> &lt; .005 favoring newer interventions. Effect size for experimental interventions relative to controls did not change over time overall (<em>P</em> = .64) or for RCTs (<em>P</em> = .30), and there was a trend for increase in observational studies (<em>P</em> = .027). In 34 meta-analyses with both RCTs (<em>n</em> = 95) and observational studies (<em>n</em> = 305), the median relative summary effect (summary effect in observational studies divided by summary effect in RCTs) was 0.87 (IQR, 0.55–1.29); meta-analysis of the relative summary effects yielded a summary of 0.93 (95% CI, 0.74–1.18).</div></div><div><h3>Conclusion</h3><div>The vast majority of newer interventions had no mortality differences over older ones both overall and specifically in RCTs, while benefits for newer interventions were reported more frequently in observational studies.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111764"},"PeriodicalIF":7.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRADE notes 5: comparing a new intervention with an established treatment. Practical and methodological issues","authors":"Francesco Nonino ,&nbsp;Elisa Baldin ,&nbsp;Nick Rijke ,&nbsp;Joanna Laurson-Doube ,&nbsp;Thomas Piggott ,&nbsp;Holger J. Schünemann","doi":"10.1016/j.jclinepi.2025.111768","DOIUrl":"10.1016/j.jclinepi.2025.111768","url":null,"abstract":"<div><h3>Objectives</h3><div>Recommendations about the comparison of multiple interventions should ideally be based on direct evidence. Issues may arise in a guideline development group (GDG) if the intervention of interest is compared with an alternative, widely accepted intervention, and direct evidence suggests that the former may be at least as effective as the latter.</div></div><div><h3>Study Design and Setting</h3><div>In this report, we present our experience during the development of evidence-based recommendations, according to the Grading of Recommendations Assessment, Development and Evaluation methodology, on azathioprine as an off-label treatment for multiple sclerosis in settings with limited resources.</div></div><div><h3>Results</h3><div>Direct evidence from small studies on critical outcomes (relapse and worsening of disability) probably favored, with very low certainty, azathioprine over interferon (IFN), a widely accepted labeled treatment for multiple sclerosis. Indirect evidence on IFN compared to placebo, despite being supported by larger trials, favored IFN with very low certainty. These observations were surprising for some GDG members and challenged their confidence in the seemingly established health effects of IFN and on how they should comparatively evaluate azathioprine. De facto, the long-established treatment that before the availability of newer disease-modifying treatments had been considered for years as a standard of care, was only based on very low certainty evidence, which was paired by very low certainty evidence in favor of another treatment. To not deviate from the mandate and scope of the guideline, through discussion and voting, conditional recommendations supporting the use of azathioprine were made where IFN and/or other treatments were not available and affordable. Although the GDG concluded that there was insufficient evidence to recommend azathioprine ahead of IFN, some of its members would have preferred to rank azathioprine ahead of IFN, based on the published evidence. An alternative solution may have been to add a new question and rediscuss the role of the established treatment, that is, IFN.</div></div><div><h3>Conclusion</h3><div>Recommendations should be developed according to the target recommendation question and the scope of the guideline. Scenarios that question the perceived health effects of long-established interventions must be met by an openness to reconsider such standards of care. If directed by the GDG, external, indirect evidence on the widely accepted intervention may have to be assessed and this should be considered when planning a guideline recommendation. Potential alternative approaches on how to formulate the final recommendation should be carefully weighted in relation to the context and setting to which recommendations are targeted.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111768"},"PeriodicalIF":7.3,"publicationDate":"2025-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applying advanced psychometric approaches yields differential randomised trial effect sizes: Secondary analysis of individual participant data from antidepressant studies using the Hamilton Rating Scale for Depression.","authors":"David Byrne, Fiona Boland, Susan Brannick, Robert M Carney, Pim Cuijpers, Alexandra L Dima, Kenneth E Freedland, Suzanne Guerin, David Hevey, Bishember Kathuria, Emma Wallace, Frank Doyle","doi":"10.1016/j.jclinepi.2025.111762","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111762","url":null,"abstract":"<p><strong>Objective: </strong>As multiple sophisticated techniques are used to evaluate psychometric scales, in theory reducing error and enhancing measurement of patient reported outcomes, we aimed to determine whether applying different psychometric analyses would demonstrate important differences in treatment effects.</p><p><strong>Study design and setting: </strong>We conducted secondary analysis of individual participant data from 20 antidepressant treatment trials obtained from Vivli.org (n=6,843). Pooled item-level data from the HRSD-17 were analysed using confirmatory factory analysis (CFA), item response theory (IRT) and network analysis (NA). Multilevel models were used to analyse differences in trial effects at approximately 8 weeks (range 4-12 weeks) post-treatment commencement, with standardised mean differences calculated as Cohen's d. Effect size outcomes for the original total depression scores were compared with psychometrically-informed outcomes based on abbreviated and weighted depression scores.</p><p><strong>Results: </strong>Several items performing poorly during psychometric analyses and were eliminated, resulting in different models being obtained for each approach. Treatment effects were modified as follows per psychometric approach: 10.4%-14.9% increase for CFA, 0%-2.9% increase for IRT, 14.9%-16.4% reduction for NA.</p><p><strong>Conclusion: </strong>Psychometric analyses differentially moderate effect size outcomes depending on the method used. In a 20-trial sample, factor analytic approaches increased treatment effect sizes relative to the original outcomes, NA decreased them, and IRT results reflected original trial outcomes.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111762"},"PeriodicalIF":7.3,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143732869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epidemiology and reporting characteristics of systematic reviews of clinical prediction models: a scoping review","authors":"Yunhui Xia ,&nbsp;Mei Zhang ,&nbsp;Yunliang Yao ,&nbsp;Tingting Cai ,&nbsp;Hangfeng Mo ,&nbsp;Jiantong Shen ,&nbsp;Jianlin Lou","doi":"10.1016/j.jclinepi.2025.111763","DOIUrl":"10.1016/j.jclinepi.2025.111763","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to explore research trends and areas of interest in systematic reviews (SRs) and meta-analysis of clinical prediction models (CPMs), while summarizing their conduct and reporting characteristics.</div></div><div><h3>Study Design and Setting</h3><div>A scoping review was conducted, with searches performed in PubMed, Embase, and Cochrane Library from inception to January 7, 2023. Pairs of reviewers independently screened potentially eligible studies. Data on bibliographic and methodological characteristics were collected and analyzed descriptively.</div></div><div><h3>Results</h3><div>A total of 1004 SRs published between 2001 and 2023 were included. The number of SRs increased significantly after 2020, with the majority originating from Europe (44.1%) and Asia (26.7%). Populations and outcomes were categorized into 19 and 34 classifications, respectively. The general population was the most frequently targeted (38.7%), and mortality was the most common outcome (18.9%). The prediction or diagnosis of neoplasms in the general population was the most prevalent focus (7.2%). Prognostic models were included only in 69.6% of SRs, while diagnostic models were included in 16.8%; 13.6% included both. The number of primary studies included in SRs ranged from 1 to 495, and the models ranged from 1 to 731. Most SRs lacked standardized reporting: 88.3% did not frame their review questions using established frameworks, and 79.8% did not follow standardized checklists for data extraction. Quality and risk of bias assessments were reported in 76.5% of SRs, with the Prediction model Risk of Bias Assessment Tool (27.9%) and the Quality Assessment of Diagnostic Accuracy Studies-2 tool (17.0%) being the most common. Narrative synthesis was the predominant method for evidence summarization (63.5%), while meta-analysis was conducted in 36.5%. Measures of model performance were summarized in 80.5% of SRs, with discrimination being the most frequently reported (67.7%). Only 5.2% assessed the certainty of evidence. Moreover, 42.2% of SRs published a protocol, 76.0% clearly stated support, and 91.1% stated competing interests.</div></div><div><h3>Conclusion</h3><div>The number of SRs of CPMs has grown substantially, with increasing diversity in populations and outcomes. However, significant variability in conduct and reporting was observed. Future SRs should strictly follow well-developed guidelines, and a dedicated study assessing the reporting quality and risk of bias in SRs of CPMs is warranted.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111763"},"PeriodicalIF":7.3,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143694469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}