Journal of Clinical Epidemiology最新文献

{"title":"Visualizing the value of diagnostic tests and prediction models, part II. Net benefit graphs: net benefit as a function of the exchange rate.","authors":"Michael A Kohn, Thomas B Newman","doi":"10.1016/j.jclinepi.2025.111690","DOIUrl":"10.1016/j.jclinepi.2025.111690","url":null,"abstract":"<p><strong>Background and objective: </strong>In this second of a 3-part series, we move from expected gain in utility (EGU) graphs to net benefit (NB) graphs, which show how NB depends on w= C/B, the treatment threshold odds, equal to the harm of treating unnecessarily (C) divided by the benefit of treating appropriately (B).</p><p><strong>Method: </strong>For NB graphs, we shift from the perspective of testing individual patients with varying pretest probabilities of disease to the perspective of applying a test or risk model to an entire population with a given prevalence of disease, P₀. As with EGU graphs, we subtract the harm of testing and the expected harm of treating according to the results of a test or model when it is wrong from the expected benefit of treating when it is right. The difference is that for NB graphs, the prevalence is fixed at P₀ , and the x-axis is w. NB graphs show the NB of 3 strategies: 1) \"Treat None\"; 2) \"Test\" and treat those with predicted risk greater than the treatment threshold; and 3) \"Treat All\" in the population regardless of predicted risk.</p><p><strong>Results: </strong>The \"Treat All\" line intersects the y-axis at NB = P₀ and the x-axis at w = P₀/(1 - P₀). The \"Test\" line intersects the \"Treat All\" line at the Treat-Test threshold value of w; it intersects the x-axis at the Test-No Treat value of w.</p><p><strong>Conclusion: </strong>When NB is plotted as a function of w, NB graphs can be drawn as straight lines from easily calculated intercepts.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111690"},"PeriodicalIF":7.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048559","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":"Visualizing the value of diagnostic tests and prediction models, part III. Numerical example with discrete risk groups and miscalibration.","authors":"Michael A Kohn, Thomas B Newman","doi":"10.1016/j.jclinepi.2025.111691","DOIUrl":"10.1016/j.jclinepi.2025.111691","url":null,"abstract":"<p><strong>Background and objectives: </strong>In this third of a 3-part series, we use net benefit (NB) graphs to evaluate a risk model that divides D-dimer results into 8 intervals to estimate the probability of pulmonary embolism (PE). This demonstrates the effect of miscalibration on NB graphs.</p><p><strong>Method: </strong>We evaluate the risk model's performance using pooled data on 6013 participants from 5 PE diagnostic management studies. For a range of values of the \"exchange rate\" (w, the treatment threshold odds), we obtained NB of applying the risk model by subtracting the number of unnecessary treatments weighted by the exchange rate from the number of appropriate treatments and then dividing by the population size.</p><p><strong>Results: </strong>In NB graphs, in which the x-axis is scaled linearly with the exchange rate w, miscalibration causes vertical changes in NB. If the risk model overestimates risk, as in this example, the NB graph for the risk model has vertical jumps up. These are due to the sudden gain in NB resulting from less overtreatment when the treatment threshold first exceeds the overestimated predicted risks.</p><p><strong>Conclusion: </strong>Calculating NB is a logical approach to quantifying the value of a diagnostic test or risk prediction model. In the same dataset at the same treatment threshold probability, the risk model with the higher net benefit is the better model in that dataset. Most net benefit calculations omit the harm of doing the test or applying the risk model, but if it is nontrivial, this harm can be subtracted from the net benefit.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111691"},"PeriodicalIF":7.3,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048563","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":"To adjust, or not to adjust, for multiple comparisons","authors":"Richard Hooper","doi":"10.1016/j.jclinepi.2025.111688","DOIUrl":"10.1016/j.jclinepi.2025.111688","url":null,"abstract":"<div><div>Questions often arise concerning when, whether, and how we should adjust our interpretation of the results from multiple hypothesis tests. Strong arguments have been put forward in the epidemiological literature against any correction or adjustment for multiplicity, but regulatory requirements (particularly for pharmaceutical trials) can sometimes trump other concerns. The formal basis for adjustment is often the control of error rates, and hence the problems of multiplicity may seem rooted in a purely frequentist paradigm, though this can be a restrictive viewpoint. Commentators may never wholly agree on any of these things. This article draws some of the key threads from the discussion and suggests further reading.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111688"},"PeriodicalIF":7.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pain and physical function are common core domains across 40 core outcome sets of musculoskeletal conditions: a systematic review","authors":"Tamer S. Sabet ,&nbsp;David B. Anderson ,&nbsp;Peter W. Stubbs ,&nbsp;Rachelle Buchbinder ,&nbsp;Caroline B. Terwee ,&nbsp;Alessandro Chiarotto ,&nbsp;Joel Gagnier ,&nbsp;Arianne P. Verhagen","doi":"10.1016/j.jclinepi.2025.111687","DOIUrl":"10.1016/j.jclinepi.2025.111687","url":null,"abstract":"<div><h3>Objectives</h3><div>To determine common domains across existing musculoskeletal core outcome sets (COSs). Secondary aims were to assess the development quality of existing musculoskeletal COSs and whether development quality and patient participation was associated with domain selection.</div></div><div><h3>Study Design and Setting</h3><div>A systematic review of musculoskeletal COSs. We searched six databases from inception until December 2023. Studies were included if they reported on the development of a COS in adults with musculoskeletal conditions for any type of intervention. Quality was assessed using the Core Outcome Set-Standards for Development recommendations (COS-STAD). Data extracted included scope of the COS, health condition, interventions, and outcome domains. We defined a common core domain when present in &gt;66% of all COSs. Analyses were performed using descriptive statistics.</div></div><div><h3>Results</h3><div>We included 51 studies reporting on 40 COSs, 25 were developed for research settings only, five for clinical settings only, and 10 for both. We identified 310 domains consisting of 255 mandatory or compulsory or not specified, 45 important, and 10 for further research. Pain (90%) and physical function (88%) were common core domains. COS development quality varied (range: 4–11 recommendations met); six COS met all standards. Domain definitions were provided in 13 COSs, 27 included patients or representatives in their development process, while nine met all recommendations for the consensus process. COSs involving patients were of higher quality (median: nine vs five for those not involving patients).</div></div><div><h3>Conclusion</h3><div>Pain and physical function core domains should be considered for inclusion in all new musculoskeletal COSs. Developers should follow COS development recommendations and include patients or their representatives.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111687"},"PeriodicalIF":7.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Visualizing the value of diagnostic tests and prediction models, part I: introduction and expected gain in utility as a function of pretest probability.","authors":"Michael A Kohn, Thomas B Newman","doi":"10.1016/j.jclinepi.2025.111689","DOIUrl":"10.1016/j.jclinepi.2025.111689","url":null,"abstract":"<p><strong>Background: </strong>In this first of a 3-part series, we review expected gain in utility (EGU) calculations and graphs; in later parts, we contrast them with net benefit calculations and graphs. Our example is plasma D-dimer as a test for pulmonary embolism.</p><p><strong>Methods: </strong>We approach EGU calculations from the perspective of a clinician evaluating a patient. The clinician is considering 1) not testing and not treating, 2) testing and treating according to the test result; or 3) treating without testing. We use simple algebra and graphs to show how EGU depends on pretest probability and the benefit of treating someone with disease (B) relative to the harms of treating someone without the disease (C) and the harm of the testing the procedure itself (T).</p><p><strong>Results: </strong>The treatment threshold probability, i.e., the probability of disease at which the expected benefit of treating those with disease is balanced by the harm of treating those without disease (EGU = 0) is C/(C + B). When a diagnostic test is available, the course of action with the highest EGU depends on C, B, T, the pretest probability of disease, and the test result. For a given C, B, and T, the lower the pretest probability, the more abnormal the test result must be to justify treatment.</p><p><strong>Conclusion: </strong>EGU calculations and graphs allow visualization of how the value of testing can be calculated from the prior probability of the disease, the benefit of treating those with disease, the harm of treating those without disease, and the harm of testing itself.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111689"},"PeriodicalIF":7.3,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143048556","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":"Scoping review of registration of observational studies finds inadequate registration policies, increased registration, and a debate converging toward proregistration","authors":"Daniel Malmsiø ,&nbsp;Simon Norlén ,&nbsp;Cecilie Jespersen ,&nbsp;Victoria Emilie Neesgaard ,&nbsp;Zexing Song ,&nbsp;An-Wen Chan ,&nbsp;Asbjørn Hróbjartsson","doi":"10.1016/j.jclinepi.2025.111686","DOIUrl":"10.1016/j.jclinepi.2025.111686","url":null,"abstract":"<div><h3>Objectives</h3><div>We aimed to examine a) the policies of national and international clinical trial registries regarding observational studies; b) the time trends of observational study registration; and c) the published arguments for and against observational study registration.</div></div><div><h3>Study Design and Setting</h3><div>Scoping review of registry practices and published arguments. We searched the websites and databases of all 19 members of the World Health Organization's Registry Network to identify policies relating to observational studies and the number of observational studies registered annually from the beginning of the registries to 2022. Regarding documents with arguments, we searched Medline, Embase, Google Scholar, and top medical and epidemiological journals from 2009 to 2023. We classified arguments as “main” based on the number (<em>n</em> ≥ 3) of documents they occurred in.</div></div><div><h3>Results</h3><div>Of 19 registries, 15 allowed observational study registration, of which seven (35%) had an explicit policy regarding what to register and two (11%) about when to register. The annual number of observational study registrations increased over time in all registries; for example, ClinicalTrials.gov increased from 313 in 1999 to 9775 in 2022. Fifty documents provided arguments concerning observational study registration: 31 argued for, 18 against, and one was neutral. Since 2012, 19 out of 25 documents argued for. We classified nine arguments as main: five for and four against. The two most prevalent arguments for were the prevention of selective reporting of outcomes (<em>n</em> = 16) and publication bias (<em>n</em> = 12), and against were that it will hinder exploration of new ideas (<em>n</em> = 17) and it will waste resources (<em>n</em> = 6).</div></div><div><h3>Conclusion</h3><div>Few registries have policies regarding observational studies; an increasing number of observational studies were registered; there was a lively debate on the merits of registration of observational studies, which, since 2012, seems to converge toward proregistration.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111686"},"PeriodicalIF":7.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Heterogeneity across outcomes in clinical trials on sodium-glucose cotransporter 2 inhibitors in chronic heart failure: a cross-sectional study","authors":"Fran Šaler ,&nbsp;Marin Viđak ,&nbsp;Ružica Tokalić ,&nbsp;Livia Puljak","doi":"10.1016/j.jclinepi.2025.111685","DOIUrl":"10.1016/j.jclinepi.2025.111685","url":null,"abstract":"<div><h3>Objectives</h3><div>This study aimed to analyze the outcomes, outcome domains, and prevalence of the use of clinical outcome endpoints (COE) in clinical trials on sodium-glucose cotransporter 2 (SGLT2) inhibitors for chronic heart failure (CHF) registered on ClinicalTrials.gov and compare them to COE for cardiovascular trials.</div></div><div><h3>Study Design and Setting</h3><div>We conducted a cross-sectional methodological study. Trials and trial outcomes were extracted from ClinicalTrials.gov, classified, and analyzed. For pivotal trials, registrations were compared with matching publications and supplementary documentation. The adherence of outcomes in pivotal clinical trials to COE developed by the European Society of Cardiology (ESC) was checked.</div></div><div><h3>Results</h3><div>In 71 included trials, we found 170 individual clinical outcomes and divided them into 11 groups (10 clinical outcome groups and ESC COE). Heart failure with reduced ejection fraction (HFrEF) was analyzed in 33 (46%) trials, and heart failure with preserved ejection fraction (HFpEF) in 25% of trials. ESC COE outcomes were used in less than 30% of trials, and only in 9 as primary outcomes (13%). Trials included 59 different biomarker endpoints. Patient-reported outcomes were highly heterogeneous, utilizing various nonvalidated questionnaires. All five pivotal trials used primary outcomes from ESC COE. The adherence of pivotal trials to the ESC COE was moderately high, with insufficient data on dyspnea and heart failure events such as intensification of diuretic therapy. All pivotal trials had at least one change in study protocol at one point during the trial, in outcome measures, statistical model, enrollment, or trial duration.</div></div><div><h3>Conclusion</h3><div>Outcomes used in CHF trials of SGLT2 inhibitors were highly heterogeneous. Core outcome sets developed especially for CHF were underutilized. Standardization of outcomes is needed in the CHF field to enable between-trial comparisons and evidence syntheses.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111685"},"PeriodicalIF":7.3,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043290","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 the PICAR framework can benefit guideline systematic reviews: a call for greater attention (Letter commenting on: J Clin Epidemiol. 2019; 108:64-76)","authors":"Yin Yu,&nbsp;Zihan Huang,&nbsp;Hui Liu,&nbsp;Xuanlin Li,&nbsp;Lin Huang,&nbsp;Chengping Wen,&nbsp;Yaolong Chen","doi":"10.1016/j.jclinepi.2025.111679","DOIUrl":"10.1016/j.jclinepi.2025.111679","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111679"},"PeriodicalIF":7.3,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143043292","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":"The importance of properly specifying your target trial emulation: commentary on Mésidor et al","authors":"Andrea L. Schaffer,&nbsp;William J. Hulme","doi":"10.1016/j.jclinepi.2025.111683","DOIUrl":"10.1016/j.jclinepi.2025.111683","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111683"},"PeriodicalIF":7.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143029441","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":"Risk of bias assessment tools often addressed items not related to risk of bias and used numerical scores","authors":"Madelin R. Siedler ,&nbsp;Hassan Kawtharany ,&nbsp;Muayad Azzam ,&nbsp;Defne Ezgü ,&nbsp;Abrar Alshorman ,&nbsp;Ibrahim K. El Mikati ,&nbsp;Sadiya Abid ,&nbsp;Ali Choaib ,&nbsp;Qais Hamarsha ,&nbsp;M. Hassan Murad ,&nbsp;Rebecca L. Morgan ,&nbsp;Yngve Falck-Ytter ,&nbsp;Shahnaz Sultan ,&nbsp;Philipp Dahm ,&nbsp;Reem A. Mustafa","doi":"10.1016/j.jclinepi.2025.111684","DOIUrl":"10.1016/j.jclinepi.2025.111684","url":null,"abstract":"<div><h3>Objectives</h3><div>We aimed to determine whether the existing risk of bias assessment tools addressed constructs other than risk of bias or internal validity and whether they used numerical scores to express quality, which is discouraged and may be a misleading approach.</div></div><div><h3>Methods</h3><div>We searched Ovid MEDLINE and Embase to identify quality appraisal tools across all disciplines in human health research. Tools designed specifically to evaluate reporting quality were excluded. Potentially eligible tools were screened by independent pairs of reviewers. We categorized tools according to conceptual constructs and evaluated their scoring methods.</div></div><div><h3>Results</h3><div>We included 230 tools published from 1995 to 2023. Access to the tool was limited to a peer-reviewed journal article in 63% of the sample. Most tools (76%) provided signaling questions, whereas 39% produced an overall judgment across multiple domains. Most tools (93%) addressed concepts other than risk of bias, such as the appropriateness of statistical analysis (65%), reporting quality (64%), indirectness (41%), imprecision (38%), and ethical considerations and funding (22%). Numerical scoring was used in 25% of tools.</div></div><div><h3>Conclusion</h3><div>Currently available study quality assessment tools were not explicit about the constructs addressed by their items or signaling questions and addressed multiple constructs in addition to risk of bias. Many tools used numerical scoring systems, which can be misleading. Limitations of the existing tools make the process of rating the certainty of evidence more difficult.</div></div><div><h3>Plain Language Summary</h3><div>Many tools have been made to assess how well a scientific study was designed, conducted, and written. We searched for these tools to better understand the types of questions they ask and the types of studies to which they apply. We found 230 tools published between 1995 and 2023. One in every four tools used a numerical scoring system. This approach is not recommended because it does not distinguish well between different ways quality can be assessed. Tools assessed quality in a number of different ways, with the most common ways being risk of bias (how a study is designed and run to reduce biased results; 98%), statistical analysis (how the data were analyzed; 65%), and reporting quality (whether important details were included in the article; 64%). People who make tools in the future should carefully consider the aspects of quality that they want the tool to address and distinguish between questions of study design, conduct, analysis, ethics, and reporting.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"180 ","pages":"Article 111684"},"PeriodicalIF":7.3,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143030346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}