Journal of Clinical Epidemiology最新文献

{"title":"Calculating Follow-Up Completeness: a comparison of multiple methods under different simulated scenarios and a use-case.","authors":"Carlijn C E M van der Ven, M Arfan Ikram, Frank J A van Rooij, Jolanda Kluin, Johanna J M Takkenberg, Kevin M Veen","doi":"10.1016/j.jclinepi.2025.111757","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111757","url":null,"abstract":"<p><strong>Background: </strong>Completeness of follow-up is a crucial aspect of data quality in cohort studies and clinical trials. This study aims to provide an overview of different methods to calculate follow-up completeness. Additionally, the performance of these methods is tested in several scenarios using simulated datasets and a use-case, with the aim of guiding researchers in selecting the most appropriate method for their data.</p><p><strong>Methods: </strong>The literature was searched for methods of quantification of follow-up completeness. These methods were investigated in simulated datasets, in which the true completeness of follow-up was known. A total of 27 different scenarios were investigated, based on different survival distributions, total proportions of drop-out of participants and different time points of drop-out. The methods were also investigated using real-world mortality data from the population-based Rotterdam Study cohort. Kaplan-Meier curves were used in order to depict observed survival, and completeness of follow-up was calculated in percentages using a freely available GitHub package developed by our research group.</p><p><strong>Results: </strong>In total, six methods were found in the literature for quantification of follow-up completeness. Overall, two methods (the Simplified Person-Time Method and the modified Clark's Completeness Index C*) were closest to the true follow-up completeness in the 27 scenarios.</p><p><strong>Conclusions: </strong>Researchers should make attempts to report follow-up completeness. This simulation study may assist researchers in selecting the most appropriate method to calculate follow-up completeness in different scenarios.</p>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111757"},"PeriodicalIF":7.3,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639839","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":"A metaresearch study finds unclear impact of institutional conflicts of interest on conclusions of studies investigating volume–outcome relationships","authors":"Charlotte M. Kugler ,&nbsp;Kaethe Goossen ,&nbsp;Elie A. Akl ,&nbsp;Dawid Pieper","doi":"10.1016/j.jclinepi.2025.111756","DOIUrl":"10.1016/j.jclinepi.2025.111756","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;This study aimed to explore institutional conflicts of interest (COIs) in volume–outcome studies investigating whether higher hospital volume is associated with better patient outcomes.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;We used a sample of studies (&lt;em&gt;n&lt;/em&gt; = 68) included in a systematic review on the hospital volume–outcome relationship in total knee arthroplasty. For studies in which at least one of the study authors was affiliated with a hospital, we contacted the study authors by email to obtain their institutional volume and to survey them about their opinion on institutional COIs. We categorized the studies' conclusions (positive vs nonpositive) and authors' hospital volume (high, intermediate, low). We compared conclusions for high- vs intermediate-/low–hospital volume categories.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;Of the 29 hospital-affiliated authors contacted, 20 replied. Authors from high-volume institutions were more likely to conclude that a hospital volume–outcome relationship existed compared to authors from intermediate- or low-volume institutions, although this was not statistically significant (odds ratio, 2.0; 95% CI: 0.21–18.7). Six out of 17 authors (35%) believed that institutional factors such as the case volume were (very) likely to influence the study design, analysis, or conclusions of research in the field of volume–outcome studies; four of 17 (24%) were neutral; and seven of 17 (41%) believed that this was (very) unlikely.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;This is the first study explicitly investigating institutional financial interests with benefit through increasing services provided by the institution. The findings suggest the possibility that institutional COI may influence the conclusions of volume–outcome studies, although the results are inconclusive. Surveyed authors had divergent opinions on whether institutional factors are likely to influence research integrity. Further research is needed to investigate institutional COIs.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;This study examined COIs in research, focusing on benefits to the institution rather than to individual researchers. Authors might publish results in favor of their hospital to increase the amount of a service provided, such as surgery. We looked at 68 studies. These studies investigated whether hospitals performing more knee replacement surgeries had better patient outcomes. We found that authors from hospitals with many knee surgeries were more likely to report positive conclusions compared to those from lower-volume hospitals. We also surveyed study authors working at hospitals to get their views on COIs. Out of the 20 authors who responded, 35% thought institutional factors likely influenced study conclusions, 24% were neutral, and 41% thought this influence was unlikely. Our findings suggest that it is possible that COIs through researchers' institutions may affect study con","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111756"},"PeriodicalIF":7.3,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631049","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":"Methodological challenges in pilot trials of herbal medicine: barriers to evidence-based practice","authors":"Yixuan Li ,&nbsp;Ziwen Xu ,&nbsp;Peipei Du ,&nbsp;Jierong Gao ,&nbsp;Sijin Wang ,&nbsp;Xu Pang ,&nbsp;Chenyu Ren ,&nbsp;Yan Liu ,&nbsp;Chi Zhang","doi":"10.1016/j.jclinepi.2025.111754","DOIUrl":"10.1016/j.jclinepi.2025.111754","url":null,"abstract":"&lt;div&gt;&lt;h3&gt;Objectives&lt;/h3&gt;&lt;div&gt;The growing popularity of herbal medicine (HM) underscores the need for high quality clinical trials to support its evidence-based integration. Pilot trials are essential for addressing methodological challenges in this field. This study evaluates the design quality, feasibility, and reporting of HM pilot trials, with a focus on their capacity to inform future full-scale studies.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Study Design and Setting&lt;/h3&gt;&lt;div&gt;A comprehensive collection of HM pilot trials was conducted using PubMed, Web of Science, and Embase, based on predefined inclusion criteria. Data were extracted on trial characteristics, reporting quality, and progression to full-scale studies. To gather additional information on follow-up studies, authors of selected trials were contacted directly by email. Adherence to Consolidated Standards of Reporting Trials (CONSORT) guidelines for pilot trials was evaluated, and Poisson regression was applied to identify factors influencing reporting completeness.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Results&lt;/h3&gt;&lt;div&gt;A total of 123 HM pilot trials were reviewed, predominantly from Asia (78.1%). Trials most commonly addressed respiratory (14.6%), nervous (14.6%), and reproductive systems (13.0%). Key gaps in reporting included feasibility assessments (13.1%), sample size rationale (47.2%), and randomization methods (35.8%). HM-specific details, including ingredient processing, quality control, and safety assessments, were inconsistently reported. Among the trials, 4 (3.3%) progressed to full-scale studies. Factors such as trial registration (incidence rate ratio (IRR) = 1.20, 95% CI: 1.11–1.30) and protocol publication (IRR = 1.16, 95% CI: 1.08–1.24) were positively associated with reporting completeness. Moreover, an analysis of the origin of HMs revealed that modern HM trials were 4.7 times more likely to progress to full-scale studies compared to traditional HM trials (odds ratio = 4.70, 95% CI: 0.37–252.91), although the result did not reach statistical significance (&lt;em&gt;P&lt;/em&gt; = .300).&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Conclusion&lt;/h3&gt;&lt;div&gt;HM pilot trials, as they stand, are not yet equipped to reliably guide full-scale studies. Core issues in methodological rigor, particularly in feasibility assessment, sample size justification, and randomization processes, limit their effectiveness and integration into evidence-based practice. A dedicated checklist that merges pilot study standards with the unique needs of HM trials is essential.&lt;/div&gt;&lt;/div&gt;&lt;div&gt;&lt;h3&gt;Plain Language Summary&lt;/h3&gt;&lt;div&gt;HM is increasingly used worldwide, but there are challenges in ensuring its effectiveness through clinical trials. This study aimed to evaluate the quality and reporting of pilot trials involving HM, which are smaller studies conducted before larger trials. Pilot trials are essential to identify potential issues in study design and ensure the reliability of future full-scale trials. We reviewed 123 trials from 21 countries and found that many lac","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111754"},"PeriodicalIF":7.3,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143626778","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":"Suboptimal practices in harm reporting: a meta-epidemiological study on metrics, recurrence, and exposure duration in clinical trials","authors":"Qiao Huang ,&nbsp;Wen Wang ,&nbsp;Liang Zheng ,&nbsp;Yue-Xian Shi ,&nbsp;Long Ge ,&nbsp;Xian-Tao Zeng ,&nbsp;Ying-Hui Jin","doi":"10.1016/j.jclinepi.2025.111755","DOIUrl":"10.1016/j.jclinepi.2025.111755","url":null,"abstract":"<div><h3>Objectives</h3><div>The Consolidated Standards of Reporting Trials (CONSORT) Harms 2022 statement emphasizes the necessity for clinical trials to clearly address the duration of follow-up and recurrence of adverse events in safety analysis, highlighting the importance of using appropriate measures for a comprehensive risk assessment. This study aimed to provide guidance on metrics in harm profile reporting and evaluating current practices in clinical trials against the CONSORT Harms 2022 recommendations.</div></div><div><h3>Study Design and Setting</h3><div>We have summarized characteristics of four reporting metrics—cumulative incidence rate, cumulative event rate, exposure-adjusted incidence rate (EAIR), and exposure-adjusted event rate (EAER). To evaluate the current reporting patterns, we conducted a meta-epidemiological study of 116 clinical trials published in four top-tier medical journals from September 1, 2023, to December 31, 2023.</div></div><div><h3>Results</h3><div>The cumulative incidence rate was the most frequently used metric (81.03%), followed by a simple count (16.38%), EAER (3.45%), EAIR (2.59%), and cumulative event rate (0.86%). A total of 105 trials (91.38%) used a single measure and 10 trials (8.62%) incorporated 2 different measures. Only 14 trials (12.07%) gave explicit evidence for the reporting of recurrent adverse events and six trials (5.17%) explicitly stated their rationale for not considering recurrence. Adjustments for exposure duration were notably absent in trials with unequal dropout rates and exposure times.</div></div><div><h3>Conclusion</h3><div>Recurrence of adverse events and varied exposure duration were inadequately addressed in current practices. Future trials should adopt transparent and sophisticated metrics in reporting them to capture a multidimensional and reliable representation of harm profiles.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111755"},"PeriodicalIF":7.3,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143598331","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":"Exploring the effect of COVID-19 restrictions on the social functioning scale in a clinical trial of antipsychotic reduction: using multiple imputation to target a hypothetical estimand","authors":"Louise Marston ,&nbsp;Joanna Moncrieff ,&nbsp;Stefan Priebe ,&nbsp;Suzie Cro ,&nbsp;Victoria R. Cornelius","doi":"10.1016/j.jclinepi.2025.111753","DOIUrl":"10.1016/j.jclinepi.2025.111753","url":null,"abstract":"<div><h3>Objectives</h3><div>Many trials are affected by unforeseen events after recruitment has commenced. The aim of this study is to explore a hypothetical strategy for dealing with an intercurrent event that occurred during trial follow-up; COVID-19 restrictions.</div></div><div><h3>Study Design and Setting</h3><div>Secondary analysis of a randomized controlled trial (RCT) in schizophrenia, comparing antipsychotic reduction vs maintenance medication on the social functioning scale (SFS) score at 12 months' follow-up. A hypothetical analysis strategy was used to estimate the treatment effect in a COVID-19 restriction-free world. Outcome data were set to missing, and multiple imputation was used to replace values affected by COVID-19.</div></div><div><h3>Results</h3><div>The trial randomized 253 participants, 187 participants had an SFS score at 12 months, and 75 of those were collected during COVID-19 restrictions. In the original complete case regression analysis, targeting a treatment policy estimand, the treatment effect was estimated to be 0.51 (95% CI −1.33, 2.35) points higher in the reduction group. After multiple imputation, targeting the hypothetical estimand, the mean SFS score was −3.01 (95% CI −7.22, 1.20) points lower in the reduction group, but varied with different assumptions about the timing of events and in sensitivity analyses to increase the size of difference between randomized groups.</div></div><div><h3>Conclusion</h3><div>We demonstrated how the intervention effect can change when estimating the intervention effect in a pandemic world (treatment policy estimand) vs a pandemic restriction-free world (hypothetical estimand), and that estimates are sensitive to imputation and input assumptions. Trialists should be aware of potential intercurrent events and plan the analysis to take them into account.</div></div><div><h3>Plain Language Summary</h3><div>Many medical research studies that enable us to find out how well things work had to change due to COVID-19 restrictions. This may have altered the results. We used data from a randomized controlled trial (RCT) to examine whether there was evidence for this. The main outcome included questions on how often participants went to the cinema, swimming, to church or saw friends or relatives. Many of these activities were not possible during COVID-19 restrictions and became possible again over time. We used statistical methods to replace data that were collected during COVID-19 restrictions with the best possible estimate if COVID-19 had not happened. We found that the trial results were likely to have been different if the effect of COVID-19 restrictions were taken away. It is likely that most studies will be interested in results that do not include data collected during COVID-19.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111753"},"PeriodicalIF":7.3,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588016","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":"Measuring the environmental impact of health interventions in randomised controlled trials - a scoping review.","authors":"Beatriz Goulao, Dwayne Boyers, Oscar Forbes, Kristin Konnyu","doi":"10.1016/j.jclinepi.2025.111751","DOIUrl":"https://doi.org/10.1016/j.jclinepi.2025.111751","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Healthcare accounts for 4% of the overall greenhouse gas emissions worldwide. Reducing carbon emissions is a key strategic aim for health systems in multiple countries. To achieve this, environmental data needs to be considered in health decision making. Clinical trials are a key element to inform clinical practice, but to what extent they have considered environmental outcomes in their design, analysis and interpretation is unclear. We conducted a scoping review of environmental outcomes in clinical trials, including protocols, to assess what outcomes are being collected, how and why, and to what extent environmental outcomes influence interpretation of the clinical findings by trial authors.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a search strategy in Embase, MEDLINE and Cochrane library to identify eligible English language studies with no date restrictions. Inclusion criteria were healthcare randomised controlled trials (RCTs) that collected, or protocols of RCTs that planned to collect, environmental outcomes in their abstracts. Two researchers conducted abstract and full text screening independently. We single extracted data using a pre-agreed and piloted data extraction form. Any uncertainties in extraction were discussed as a group until an agreement was reached. We summarised quantitative data using descriptive statistics and analysed verbatim text of qualitative data into summary categories that were agreed by the team.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We identified 1,318 abstracts, from which 27 full texts were screened, and 16 RCTs (or protocols of RCTs) were included in our review. Nine studies were published from 2022; nine reported environmental outcomes in secondary publications; seven were in the nutrition field followed by oncology (n=2) and anaesthesiology (n=2). The primary reason for including environmental outcomes was to confirm that the intervention reduced emissions (n=12), rather than focusing on broader goals of lowering carbon emissions or environmental considerations in health systems. Included trials assessed various environmental impacts of the studied interventions including carbon footprint, greenhouse gas emissions, or wider environmental impacts. Most trials used life cycle assessments, or publicly available carbon footprint data, to calculate their environmental outcomes. The analysis was not pre-specified in eight trials; all trials but one undertook separate analyses for clinical and environmental outcomes. In all but two cases, environmental and clinical outcomes were in agreement (in favour of, or against, the intervention). Patient and public involvement was rarely reported (n= 3). Five trials presented examples or contextual information to aid interpretation of the environmental outcome results.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;Including environmental outcomes in RCTs appears to be increasing in prominence, although the role of these outcomes in clinical decision maki","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111751"},"PeriodicalIF":7.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588019","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":"Definitions of validity terms for use in discussions of randomized controlled trials","authors":"Yasaman Yazdani ,&nbsp;Monica Taljaard ,&nbsp;Merrick Zwarenstein","doi":"10.1016/j.jclinepi.2025.111752","DOIUrl":"10.1016/j.jclinepi.2025.111752","url":null,"abstract":"<div><h3>Objectives</h3><div>We review existing definitions and usages of validity terms and propose a single definition for each term for use in communicating inferences from Randomized Controlled trials (RCTs).</div></div><div><h3>Methods</h3><div>Two trialists and a statistician reviewed definitions in various dictionaries and literature to identify confusions and propose unified definition for each term.</div></div><div><h3>Results</h3><div>We propose the following disambiguated and mutually coherent set of definitions for validity terms: A well-defined population, for whom inferences from an RCT are asserted as valid by the investigators or by other users. An assertion that an inference from an RCT is at low risk of confounding (distortion of the effects of the intervention by differences in prognostic factors between arms of the trial other than the difference in intervention exposure). An umbrella term asserting that an internally valid inference from an RCT applies to a target population specified by the investigators or by other users, requiring any of three justifications: representativeness, applicability, or extrapolatability. An assertion of external validity of an inference based on the statistical representativeness of the participants, as a random sample of the source (and target) population. An assertion of the external validity of an inference based on subjective assessment of the similarity in context and in distribution of known predictors of outcome between the participant sample and the target population. An assertion of the external validity of an inference from an RCT to a target population based on a common mechanism of action. Bias is the opposite of validity and may be internal due to confounding from systematic error in design, measurement, and analysis; or external, due to mismatch between the population represented by the RCT participants and their setting, and the target population and its context.</div></div><div><h3>Conclusion</h3><div>With wide uptake, this coherent set of definitions for key terms related to validity could improve understanding and design of RCTs.</div></div>","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"182 ","pages":"Article 111752"},"PeriodicalIF":7.3,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588012","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":"Response to \"the inappropriateness of internal consistency testing and factor analysis for formative indicators: comment on Felicia et al, 2024\".","authors":"Felicia Jia Ler Ang, Mihir Gandhi, Yin Bun Cheung","doi":"10.1016/j.jclinepi.2025.111750","DOIUrl":"10.1016/j.jclinepi.2025.111750","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":" ","pages":"111750"},"PeriodicalIF":7.3,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143574609","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":"From introducing Table 0 to forgetting Figure 1: it is time for a reporting guideline for publishing with real-world clinical data. Author's reply","authors":"Jip W.T.M. de Kok,&nbsp;Bas C.T. van Bussel,&nbsp;Iwan C.C. van der Horst,&nbsp;Frank van Rosmalen","doi":"10.1016/j.jclinepi.2024.111656","DOIUrl":"10.1016/j.jclinepi.2024.111656","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"179 ","pages":"Article 111656"},"PeriodicalIF":7.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924001","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":"From introducing Table 0 to forgetting Figure 1: it is time for a reporting guideline for publishing with real-world clinical data—Response to de Kok et al","authors":"Carolina Hincapié-Osorno,&nbsp;Raymond J. van Wijk,&nbsp;Douwe F. Postma,&nbsp;Jan C. ter Maaten,&nbsp;Hjalmar R. Bouma,&nbsp;Jacqueline Koeze","doi":"10.1016/j.jclinepi.2024.111655","DOIUrl":"10.1016/j.jclinepi.2024.111655","url":null,"abstract":"","PeriodicalId":51079,"journal":{"name":"Journal of Clinical Epidemiology","volume":"179 ","pages":"Article 111655"},"PeriodicalIF":7.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142924002","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}