Journal of Clinical Epidemiology最新文献

{"title":"Swedish prediction model for obstetric anal sphincter injury - need for external validation, continuous updates, and accurate antenatal fetal weight estimation.","authors":"Jennie Larsudd-Kåverud, Sigvard Åkervall, Mattias Molin, Ida E K Nilsson, Ewout W Steyerberg, Ian Milsom, Maria Gyhagen","doi":"10.1016/j.jclinepi.2025.111964","DOIUrl":"10.1016/j.jclinepi.2025.111964","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111964"},"PeriodicalIF":5.2,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001799","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 distinction between causal, predictive, and descriptive research-there is still room for improvement.","authors":"Brett P Dyer","doi":"10.1016/j.jclinepi.2025.111960","DOIUrl":"10.1016/j.jclinepi.2025.111960","url":null,"abstract":"<p><p>It has been proposed that medical research questions can be categorised into three classes: causal, predictive, and descriptive. This distinction was proposed to encourage researchers to think clearly about how study design, analysis, interpretation, and clinical implications should differ according to the type of research question being investigated. This article highlights four common mistakes that remain in observational research regarding the classification of research questions as causal, predictive, or descriptive, and provides suggestions about how they may be rectified. The four common mistakes are (1) Adjustment for \"confounders\" in predictive and descriptive research, (2) Interpreting \"effects\" in prediction models, (3) The use of non-specific terminology that does not indicate which class of research question is being investigated, and (4) Prioritising parsimony over confounder adjustment in causal models.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111960"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994314","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":"Retrospective use of the Pragmatic-Explanatory Continuum Indicator Summary-2 trial design tool to assess design choices in randomized controlled trials: an empirical review","authors":"Andrew Willis ,&nbsp;Frances Shiely ,&nbsp;Alison H. Howie ,&nbsp;Shaun Treweek ,&nbsp;Monica Taljaard ,&nbsp;Kirsty Loudon ,&nbsp;Ellen Murphy ,&nbsp;Aarian Bhakoo ,&nbsp;Yasaman Yazdani ,&nbsp;Frank Ward ,&nbsp;Perrine Janiaud ,&nbsp;Andrea Haren ,&nbsp;Aileen Yining Liang ,&nbsp;Clare Robinson ,&nbsp;Daisy Deng ,&nbsp;Lars Hemkens ,&nbsp;Evelyn O'Sullivan Greene ,&nbsp;Laura Slattery ,&nbsp;Merrick Zwarenstein","doi":"10.1016/j.jclinepi.2025.111959","DOIUrl":"10.1016/j.jclinepi.2025.111959","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Background and Objective&lt;/h3&gt;&lt;div&gt;The Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) tool has been widely used to help investigators design randomized trials, facilitating the task of aligning design choices with an explanatory or pragmatic primary trial intention. PRECIS-2 is increasingly being used to retrospectively assess the degree of pragmatism or explanatoriness among published trials within reviews. There is little information on the interrater reliability of the tool and no consensus on the preferred method of achieving an accurate and reliable judgment of trial “pragmatism” when using PRECIS-2 retrospectively. The aims of this study were to assess the level of pragmatism or explanatoriness of trials that cite PRECIS-2 and to assess interrater reliability of PRECIS-2 using different scoring approaches. We compared agreement between two independent ratings within a single pair with agreement between consensus scores reached by two independent pairs of reviewers and whether widening the agreement criteria increased interrater reliability.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;Thirty randomized controlled trials (RCTs) were randomly selected from trials citing the PRECIS-2 tool. Two pairs of reviewers, a clinician paired with a methodologist in each case, were trained and independently scored each trial and reached a consensus score within pairs. Agreement between reviewers within pairs and between consensus scores across pairs was assessed using kappa statistics for each of the nine PRECIS-2 domains.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;RCTs citing PRECIS-2 had predominantly pragmatic design features. Interrater reliability within pairs was low across all domains, with the highest levels found in the two domains of analysis (0.32) and follow-up (0.33). Agreement across pairs on the consensus scores was similarly low. Agreement between reviewers and reviewer pairs was above 70% when agreement was reclassified as “within 1-point difference on the scoring scale” for eight domains, but no improvement was obtained for the remaining domain.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;Trials citing PRECIS-2 tend to have predominantly pragmatic design features. When using PRECIS-2 to retrospectively score trial publications, agreement between consensus scores across pairs of reviewers was no better than agreement within pairs. Reconfiguring the PRECIS scoring scale and improving scoring guidance may provide a more meaningful, easily interpreted measure of “pragmatism” for trialists wishing to use PRECIS-2 as a review tool.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;The Pragmatic-Explanatory Continuum Indicator Summary-2 (PRECIS-2) tool is designed to help researchers match their design decisions to the intended purpose of their trial. The intention of a trial can be “explanatory,” which improves our understanding of how an intervention works, or “pragmatic,” which supports decision-making in health care. Increasingly, ","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111959"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994356","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":"Patterns of preregistration and publication of trials in Cochrane systematic reviews of interventions","authors":"Paul Bramley ,&nbsp;Caroline Bird ,&nbsp;Robert Badgett ,&nbsp;Nicholas J. DeVito","doi":"10.1016/j.jclinepi.2025.111958","DOIUrl":"10.1016/j.jclinepi.2025.111958","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;It is widely recognized that selective reporting clinical trial results based on their outcomes, in the forms of publication bias, outcome reporting bias, or p-hacking, has detrimental effects on the scientific literature and on evidence synthesis. This can be recognized and perhaps ameliorated with comprehensive trial registration. However, previous investigations of clinical trial registration focused on study-level examinations rather than the number of trial participants, which is often more relevant to meta-analysis. Our objective was to investigate the risk of bias from selective reporting considering both trials but also the number of included participants.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Methods&lt;/h3&gt;&lt;div&gt;We took a random sample of 50 Cochrane systematic reviews (SRs) of interventions which included randomized controlled trials, forming a retrospective cohort. Focusing on the primary outcome in the SR we used the review, published trial information, and public trial registration documents to collect information about the reviews themselves, as well as information about “included,” “ongoing,” and studies “awaiting classification”.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;In all 50 selected reviews, there were 423 “included” trials which examined the primary outcome, of which 109 (25.7%) were preregistered. There was substantial variability in proportions of preregistration of included trials among reviews, with a median of 16.0% (interquartile range 0%−79.6%). Registered trials covered 60.1% of all participants, suggesting larger studies were more likely to be preregistered. The proportion of participants in registered trials which were published was high (98.2%), but the proportion of registered trials which were published also varied substantially between reviews.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;We found that in Cochrane reviews, there remains a low rate of preregistration among included studies and evidence for a substantial rate of trial nonreporting of registered trials. However, preregistered trials contributed proportionally more patients to reviews, and findings remain unpublished for only a small proportion of participants in registered trials.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;Trials are an important form of evidence in scientific literature that are often combined into systematic reviews (SRs), which give an overview for the evidence in a specific topic. However, trials and therefore the reviews can be misleading when the authors change the outcomes that they report based on the results they find, which is called reporting bias. One way of minimizing reporting bias is to register the planned methods for the trial in advance, known as preregistration. It is known that the proportion of trials that is registered can be low, but when conducting SRs the results are affected more by the number of participants within trials than the number of trials, which has not been previously studied. We aimed","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111958"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994368","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":"Editors' Choice – September 2025","authors":"Andrea C. Tricco,&nbsp;David Tovey","doi":"10.1016/j.jclinepi.2025.111952","DOIUrl":"10.1016/j.jclinepi.2025.111952","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"185 ","pages":"Article 111952"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145049349","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":"Evaluation of Methods for Joinpoint Analysis of Time Series Using Simulated and Real-World Data","authors":"Lucie Noé ,&nbsp;Zaba Valtuille ,&nbsp;Emilie Lanoy ,&nbsp;Sandrine Katsahian ,&nbsp;Florentia Kaguelidou","doi":"10.1016/j.jclinepi.2025.111966","DOIUrl":"10.1016/j.jclinepi.2025.111966","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;Joinpoint regression (JR) is implemented in time series analysis to identify trend changes without predefined shift points. This study compares the performance of the Joinpoint Regression Program (JRP) and R “&lt;em&gt;segmented&lt;/em&gt;” package in detecting joinpoints using simulated and real-world data on pediatric mental health (MH)–related hospitalizations.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;Simulated datasets (&lt;em&gt;n&lt;/em&gt; = 1000) were generated with varying residual autocorrelation, trend change magnitude, and joinpoint locations to evaluate the performance of both software (accuracy, specificity, coverage of the 95% confidence interval [95% CI] coverage, monthly percent change [MPC], coverage of the last segment). In addition, monthly proportions of pediatric MH-related hospitalizations (January 2016–December 2023) were analyzed to compare the number of joinpoints identified and the average monthly percent change (AMPC).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;In simulations without residual autocorrelation and no joinpoint, JRP exhibited a specificity of 92.7% compared to 97.9% for R. With an important trend change, the accuracy and 95% CI coverage were 59.0% and 81.7% using JRP and 69.4% and 76.8% using R. The coverage of the MPC of the last time segment varied from 93.0% to 97.9% using JRP and from 0% to 98.3% using R. When residual autocorrelation was introduced with a moderate trend change toward the end of the dataset, the accuracy and 95% CI coverage were 72.6% and 95.0% using JRP and 52.8% and 67.1% using R. The coverage of the MPC of the last time segment varied from 4.8% to 98.6% using JRP and from 0% to 96.9% with R. In the analysis of MH-related hospitalizations, among girls aged 6–11 years, JRP detected four joinpoints (AMPC: 0.11%), while R found 1 (AMPC: 0.05%). For boys aged 12–17 years, JRP identified four joinpoints vs three using R.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;The choice of JR software should be guided by the characteristics of the dataset. The R “&lt;em&gt;segmented”&lt;/em&gt; package may be more appropriate for datasets without residual autocorrelation, whereas JRP appears to provide more reliable estimates when analyzing autocorrelated health care data or data with no underlying trend changes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;This study compares two methods for analyzing changes in trends over time. The two tools examined are the Joinpoint Regression Program (JRP) and the R “segmented” package. Using both simulated data and real-world hospital data, we assessed the performance of these tools. The simulated data included two scenarios: one without residual autocorrelation (simpler) and another with residual autocorrelation (where data points are related to previous ones, often seen in health care data). In the simpler scenario, the R package outperformed JRP by being more accurate in detecting changes and avoiding false detections. It also provided more precise esti","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111966"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994376","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":"Developing, running, and evaluating a simulation of a guideline development meeting","authors":"Claire Glenton ,&nbsp;Thale C. Hartmann ,&nbsp;Simon Lewin ,&nbsp;Ingeborg Lidal ,&nbsp;Susan Munabi-Babigumira ,&nbsp;Gry Velvin ,&nbsp;Ariane Kwiet","doi":"10.1016/j.jclinepi.2025.111925","DOIUrl":"10.1016/j.jclinepi.2025.111925","url":null,"abstract":"<div><h3>Objective</h3><div>We developed a simulation of a guideline development group (GDG) meeting to increase people’s knowledge and familiarity with the guideline development process. In this paper, we describe how we developed, ran, and evaluated the simulation and offer guidance to others who wish to do the same.</div></div><div><h3>Study design and setting</h3><div>We ran five simulations in Norway, hosted by one hospital, two universities, and one public health institute. Our development process included determining the simulation’s aim and objectives; defining our target audience; developing simulation content, including selecting the guideline topic; preparing meeting materials; defining, preparing and allocating roles; determining the simulation length and structure; and evaluating and improving the simulation.</div><div>We gathered data regarding participants’ expectations and feedback about positive and negative aspects of the simulation and made note of our observations during simulations and used this information to inform cycles of improvement.</div></div><div><h3>Results</h3><div>We defined our target audience as people involved in commissioning, preparing, using, or disseminating guidelines. We selected an existing World Health Organization recommendation as our guideline topic. We prepared the same meeting materials as for a real guideline meeting, using the GRADE Evidence-to-Decision (EtD) framework. We developed roles for GDG members, chair, methodologist, guideline commissioner, and observers. We gave participants GDG member roles. The simulation included an online preparatory meeting and a face-to-face meeting.</div><div>Overall, participants were positive to the simulation, describing it as useful, interesting, educational, and enjoyable. Most participants were enthusiastic about the role-play element, although some were initially sceptical. Other issues we identified include the need for sufficient time to read and discuss the evidence before making a recommendation; the importance of well-prepared chairs, methodologists, and participants; and the usefulness of preparatory meetings. Participants found the time used for simulation was suitable. Some suggested providing more information in advance. However, most had not read the materials beforehand and wished they had prepared better.</div></div><div><h3>Conclusion</h3><div>Participants participating in a simulation of a guideline development process experienced this as useful and enjoyable. By offering practical guidance, we hope to support others developing similar simulations.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111925"},"PeriodicalIF":5.2,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144994344","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":"Trustworthiness of treatment clinical practice guidelines has modestly improved since the introduction of Institute of Medicine standards: a systematic survey","authors":"Maryam Ghadimi ,&nbsp;Gordon Guyatt ,&nbsp;Carlos Zaror ,&nbsp;João Pedro Lima ,&nbsp;Sana Gupta ,&nbsp;Hamed Movahed ,&nbsp;Wimonchat Tangamornsuksan ,&nbsp;Gonzalo Bravo-Soto ,&nbsp;Sahrish Masood ,&nbsp;Huda Gomaa ,&nbsp;Yanjiao Shen ,&nbsp;Nima Behravan ,&nbsp;Rachel Couban ,&nbsp;Romina Brignardello-Petersen","doi":"10.1016/j.jclinepi.2025.111962","DOIUrl":"10.1016/j.jclinepi.2025.111962","url":null,"abstract":"<div><h3>Objectives</h3><div>To explore the extent to which trustworthiness of treatment guidelines has changed since the introduction of Institute of Medicine (IOM) standards.</div></div><div><h3>Study Design and Setting</h3><div>In this systematic survey, we searched Medline, Embase, PsycINFO, and Trip from January to December 2010 (pre-IOM) and 2022 (post-IOM) for guidelines that were informed by a systematic review of evidence, written in English, and included recommendations for the treatment of any health conditions in individuals of any age. Using the 10-item modified National Guideline Clearinghouse Extent of Adherence to Trustworthy Standards (NEATS) instrument, paired reviewers independently assessed trustworthiness of 70 randomly selected guidelines from 2010 and 70 from 2022. We calculated mean difference (MD) and corresponding 95% confidence interval (CI) comparing guidelines from 2010 with 2022 for mean score across four critical items, mean score across all items, and score of individual items on 5-point Likert scales in which 1 was least adherence and 5, highest adherence. We also explored change in the proportion of guidelines that achieved a mean score of 4 or higher across critical items.</div></div><div><h3>Results</h3><div>The average of mean scores increased from 2.28 to 2.74 (MD: 0.46, 95% CI: 0.13–0.79) across critical items and from 2.20 to 2.52 (MD: 0.32, 95% CI: 0.08–0.56) across all items. The proportion of guidelines with a mean score of 4 or higher across critical items increased from 2.9% to 18.6% (absolute difference: 15.7%, 95% CI: 5.8%–25.6%). The items with the greatest increase in the average score included disclosure of funding sources and disclosure and management of financial conflict of interests, followed by a smaller increase in synthesis of evidence, study selection, and search strategy. Other items showed little to no change from 2010.</div></div><div><h3>Conclusion</h3><div>As assessed by the modified NEATS instrument, although trustworthiness of treatment guidelines has improved after the introduction of IOM standards, improvements proved modest and post-IOM guidelines trustworthiness remains suboptimal.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111962"},"PeriodicalIF":5.2,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977655","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":"EORTC QLU-C10D was similarly valid and sensitive as EQ-5D-5L but more responsive to cancer patients' health deterioration","authors":"Luying Wang ,&nbsp;Mihir Gandhi ,&nbsp;Ravindran Kanesvaran ,&nbsp;Mohamad Farid Bin Harunal Rashid ,&nbsp;Dawn Qingqing Chong ,&nbsp;Wen-Yee Chay ,&nbsp;Richard Norman ,&nbsp;Madeleine T. King ,&nbsp;Nan Luo","doi":"10.1016/j.jclinepi.2025.111965","DOIUrl":"10.1016/j.jclinepi.2025.111965","url":null,"abstract":"<div><h3>Objectives</h3><div>The psychometric performance of the Quality of Life Utility-Core 10 Dimensions (QLU-C10D), a recently developed disease-specific preference-based measure (PBM) for patients with cancer, is not well understood yet. This study aimed to compare the construct validity, sensitivity, and responsiveness of QLU-C10D with that of 5-level EQ-5D (EQ-5D-5L), a generic PBM.</div></div><div><h3>Study Design and Setting</h3><div>We recruited patients with cancer from outpatient clinics of a tertiary cancer hospital then interviewed them face-to-face at two consecutive clinic visits using both QLU-C10D and EQ-5D-5L questionnaires. Construct validity was assessed through known-group comparisons and correlation analysis, agreement of utility scores was examined using intraclass correlation coefficient (ICC), and sensitivity and responsiveness compared using effect sizes (ESs) derived from known-group comparisons and standardized response means (SRMs) derived from within-group comparisons, respectively.</div></div><div><h3>Results</h3><div>We surveyed 626 patients; 280 of whom also completed the follow-up survey. Mean baseline QLU-C10D and EQ-5D-5L utility scores were 0.799 (SD: 0.224) and 0.916 (SD: 0.156), respectively. Both utility scores demonstrated known-groups and convergent/discriminant validity and their agreement was moderate (ICC: 0.60). EQ-5D-5L's ES was slightly higher than QLU-C10D's ES for cancer stage, whereas QLU-C10D's ES was higher for Eastern Cooperative Oncology Group. The SRM of QLU-C10D was considerably higher than that of EQ-5D-5L for deteriorated patients (−0.43 vs −0.02), whereas their SRMs were similar for the improved patients.</div></div><div><h3>Conclusion</h3><div>Although QLU-C10D and EQ-5D-5L utility scores are both valid in patients with cancer, they may not be used interchangeably, and the QLU-C10D scores appear to be more responsive to deterioration in health status than the EQ-5D-5Lscores.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111965"},"PeriodicalIF":5.2,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977651","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":"Letter to the editor: The necessity of specifying measurement models: a critical reappraisal specification issues in PRECIOUS","authors":"Xin Meng ,&nbsp;Daqiu Wang ,&nbsp;Yan Huo ,&nbsp;Jie Ding ,&nbsp;Xiaochun Zhang","doi":"10.1016/j.jclinepi.2025.111956","DOIUrl":"10.1016/j.jclinepi.2025.111956","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"187 ","pages":"Article 111956"},"PeriodicalIF":5.2,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144977694","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}