Journal of periodontal research最新文献

{"title":"A Practical Guide to Evidence Synthesis: Meta-Analysis, Network Meta-Analysis, Meta-Regressions and Beyond","authors":"Yu-Kang Tu,&nbsp;Roberto Farina,&nbsp;Alessandra Viola,&nbsp;Tou-Yuan Tsai,&nbsp;James S. Hodges","doi":"10.1111/jre.70150","DOIUrl":"10.1111/jre.70150","url":null,"abstract":"<p>Meta-analysis has become an indispensable tool in medical and dental research, providing an estimate of treatment efficacy or harm by combining data from all relevant studies identified by a systematic review. Meta-analysis produces evidence at the highest level of the study design hierarchy. Meta-analysis is a broad term for a set of statistical tools used to analyse data across a range of research questions. Different research questions have different criteria for selecting relevant studies and extracting and organising data in different forms and require different statistical approaches to synthesising the data. This article focuses on meta-analysis for comparing interventions. We use plain but precise language to explain the statistical concepts and assumptions underlying these methods. This article is divided into three parts. We begin with statistical models for and key assumptions of standard pairwise meta-analysis, comparing two treatment groups. We then extend our discussion to network meta-analysis, comparing multiple treatment groups. Finally, we discuss a few issues that arise in selecting studies and preparing data for a meta-analysis. Throughout, we use examples from periodontal regeneration; all analyses were conducted using the free statistical software R. We strongly encourage close collaboration between periodontal researchers and experienced statisticians when planning and conducting meta-analyses.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"847-875"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70150","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148592219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Study Design in Clinical and Epidemiologic Research: Observational, Experimental, and Beyond","authors":"Gustavo G. Nascimento,&nbsp;Ignacio Leiva-Escobar,&nbsp;Maria Clotilde Carra,&nbsp;Sebastian-Edgar Baumeister","doi":"10.1111/jre.70105","DOIUrl":"10.1111/jre.70105","url":null,"abstract":"<p>Population-based epidemiology and clinical research are complementary and mutually reinforcing: Epidemiology quantifies disease incidence/prevalence and elucidates environmental and social determinants in populations, whereas clinical research evaluates biomarkers, therapeutics, and prognostic outcomes in patients; their convergence underpins translational science, comparative effectiveness, real-world evidence, and precision dentistry.\u0000 <figure>\u0000 <div><picture>\u0000 <source></source></picture><p></p>\u0000 </div>\u0000 </figure></p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"743-748"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147433574","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Methodology Matters: The Story Behind the Cover","authors":"Julia Ríos-Barbero","doi":"10.1111/jre.70141","DOIUrl":"10.1111/jre.70141","url":null,"abstract":"","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"741-742"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Principled Framework for Mendelian Randomization in Oral Health Research","authors":"Nasir Z. Bashir,&nbsp;Mario Romandini,&nbsp;Moritz Kebschull,&nbsp;Konstantinos K. Tsilidis,&nbsp;Dominique S. Michaud,&nbsp;Stephen Burgess","doi":"10.1111/jre.13411","DOIUrl":"10.1111/jre.13411","url":null,"abstract":"<p>In recent years, the popularity of Mendelian randomization (MR) as a technique to investigate causal relationships has grown exponentially. This approach leverages randomness inherent in the allocation of genetic variants to circumvent issues of unobserved confounding and reverse causation that compromise many causal claims based on observational data. In this manuscript, the key assumptions required for MR analyses to yield credible causal inferences are reviewed. It is emphasized that even technically rigorous MR analyses may yield implausible conclusions if these assumptions are violated. The goal is to provide readers from nonstatistical backgrounds with a clear understanding of the foundational assumptions underpinning MR, complemented by a detailed discussion of specific considerations in oral health research, particularly when periodontitis is used as the exposure or outcome. As with any study design, MR is appropriate and can provide compelling evidence for causality in certain contexts, while its application may be unwarranted in others. This methodological article aims to equip readers with the tools to critically assess MR findings, distinguish scenarios where its use is justified, and understand the key steps for correct execution and reporting. A checklist for assessing the credibility of MR investigations is also provided.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"821-836"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7618009/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023700","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cross-Sectional Studies: Strengths, Limitations, and Methodological Considerations","authors":"Sebastian-Edgar Baumeister,&nbsp;Thomas Kocher,&nbsp;Panos N. Papapanou,&nbsp;Birte Holtfreter,&nbsp;Ryan T. Demmer","doi":"10.1111/jre.70063","DOIUrl":"10.1111/jre.70063","url":null,"abstract":"<p>Cross-sectional studies capture health states, exposures, and risk factors at a single time point, providing essential data for estimating disease prevalence and informing public health planning. These studies serve multiple epidemiological purposes: characterizing population health, monitoring temporal trends through repeated surveys, and evaluating interventions via interrupted time series designs. They also offer practical advantages for validating self-reported measures and creating diagnostic models. Cross-sectional designs are efficient and well-suited to descriptive epidemiology, but they have limited utility for causal inference. The simultaneous measurement of exposures and outcomes creates temporal ambiguity that fundamentally constrains etiologic interpretation. However, causal inferences can be strengthened under specific conditions—when temporal sequence is unambiguous (e.g., genetic variants preceding outcomes) or when valid instrumental variables are available. This methodological tutorial equips readers with concepts and tools to critically appraise cross-sectional studies across the application domains outlined and to design and analyze their own cross-sectional studies that yield high-quality epidemiologic descriptions.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"749-760"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145900833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic Accuracy Studies: Sensitivity, Specificity, and Beyond","authors":"Nancy Vegas,&nbsp;Lisa Friedlander,&nbsp;Jérémie F. Cohen","doi":"10.1111/jre.70128","DOIUrl":"10.1111/jre.70128","url":null,"abstract":"<p>Diagnostic tests are central to medical care, and their evaluation remains methodologically challenging. This methodological article outlines the key measures used to quantify diagnostic accuracy, highlights common sources of bias and variability in diagnostic accuracy studies, and discusses how the clinical effectiveness of medical tests can be evaluated. Examples of diagnostic tests from various fields, including clinical, radiographic, biomarker, and emerging AI-based approaches, are discussed. We argue that diagnostic accuracy represents only the first step in test evaluation and must be complemented by assessments of feasibility, clinical impact, and patient-centered outcomes.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"782-791"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70128","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148252114","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cohort and Case–Control Studies: Strengths, Limitations and Methodological Considerations","authors":"Nasir Z. Bashir,&nbsp;João Botelho,&nbsp;John Rong Hao Tay,&nbsp;Dian Yi Chow,&nbsp;Gustavo G. Nascimento","doi":"10.1111/jre.70145","DOIUrl":"10.1111/jre.70145","url":null,"abstract":"<p>Cohort studies are central to modern health research, enabling the investigation of disease incidence, progression and long-term consequences of exposures. However, cohort designs vary substantially in structure and analytic implementation, and these differences have important implications for their validity and interpretation. This article provides a methodological overview of cohort study design and analysis in periodontology, with emphasis on how design choices and analytical strategies influence the potential sources of bias. We describe the major cohort variants, including prospective, birth, retrospective and nested case–control designs and examine how each shapes exposure measurement, follow-up and outcome ascertainment. Key design considerations include definition of the source population, specification of time zero, handling of dynamic populations and risk sets and the distinction between fixed and time-varying exposures. We then discuss major sources of bias relevant to cohort studies, including confounding, selection bias (index event bias and prevalent user bias included), immortal time bias, censoring and competing risks. Design-based strategies, notably the new-user design and target trial emulation, are presented. We further clarify the basis of a recurring study type in the periodontal literature—single-arm treatment series framed as cohort studies—and discuss how these relate to the cohort design and the inferences they support. Finally, we compare cohort and case–control designs, highlighting both their shared methodological foundations and their important differences in sampling, efficiency, interpretation and application. The credibility of a cohort study depends less on the application of any single statistical method and more on the coherence between the research question, the structure of the data and the assumptions required for valid interpretation. Careful design, transparent reporting and explicit acknowledgment of limitations are essential if cohort studies are to support reliable inference in periodontal research and beyond.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"761-781"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70145","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148421972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Inference in Clinical Research: A Primer","authors":"Nasir Z. Bashir,&nbsp;Moritz Kebschull,&nbsp;Stephen Burgess,&nbsp;Gustavo G. Nascimento","doi":"10.1111/jre.70142","DOIUrl":"10.1111/jre.70142","url":null,"abstract":"<p>The study of causal relationships is central to scientific inquiry. Understanding what disease is, why it arises, and how it can be treated are a set of fundamentally causal questions. In this article, an overview of causal inference is provided for the applied researcher, with an emphasis placed on intuition and the use of examples from periodontology to support understanding. Causal reasoning goes beyond the description of associations, with a focus instead placed on questions of the form: what would happen to an outcome if a particular exposure were changed? Directed acyclic graphs are introduced as tools through which assumptions can be made explicit and structures that may threaten valid causal inference can be identified, including confounding, mediation, and collider pathways. Randomized controlled trials are highlighted as the gold standard of causal evidence, primarily because they ensure that, on average, comparisons are made between like-with-like groups. However, observational data must often be relied upon due to ethical, practical, and logistical constraints. A three-step framework to answering causal questions is presented, based on (i) asking a causal question, (ii) identifying the causal effect, and (iii) carrying out the analysis. Within this context, two principal strategies for causal identification are outlined: design-based identification and adjustment-based identification. It is illustrated that each approach relies on different sets of assumptions to connect observed data to causal effects. The analysis stage carries out a pre-defined associational comparison that is taken to have a causal interpretation under the proposed assumptions. Across all approaches, a central theme is that causal inference is inherently driven by assumptions. The credibility of a causal claim is determined not primarily by the statistical method, but by whether the underlying assumptions are considered plausible within the context of the scientific question.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"792-810"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70142","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148471451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk of Bias Evaluation of Cross-Sectional Studies: Adaptation of the Newcastle-Ottawa Scale","authors":"Maria Clotilde Carra,&nbsp;Pierluigi Romandini,&nbsp;Mario Romandini","doi":"10.1111/jre.13405","DOIUrl":"10.1111/jre.13405","url":null,"abstract":"<p>Cross-sectional studies are widely utilized in medical research to estimate prevalence and examine associations. As such, they can serve as a significant source of data for systematic reviews. However, specific considerations are necessary when evaluating the risk of bias (RoB) of cross-sectional studies, as several potential biases can undermine the validity, reliability, and robustness of their findings. This article introduces a novel, context-specific tool designed to assess the RoB of cross-sectional studies for use in systematic reviews. The proposed tool represents an adaptation of the Newcastle-Ottawa Scale (NOS), originally developed for cohort and case–control studies. Similar to the original NOS, the new tool (named “NOS-xs”) features a nine-star rating system to assess six specific items across three main domains: (i) study sample selection, (ii) assessment of exposure(s) and outcome(s), and (iii) confounding factors. Based on the number of awarded stars, studies are categorized as having high (0–3 stars), moderate (4–6 stars), or low (7–9 stars) RoB. The NOS-xs tool maintains consistency with the original NOS tool, facilitating its integration into systematic reviews that also include cohort and/or case–control studies. While the NOS-xs is suited to analytical cross-sectional studies (i.e., association studies), a simplified version (“NOS-xs2”) is also introduced for descriptive cross-sectional studies (i.e., prevalence studies). The NOS-xs2 features a four-star rating system to assess three of the six specific items included in the NOS-xs. To streamline their application, spreadsheets for both NOS-xs and NOS-x2 are provided.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"837-846"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.13405","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144012649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Target Trial Emulation in Observational Research—Strengths, Limitations, and Methodological Considerations","authors":"Sebastian-Edgar Baumeister,&nbsp;Ignacio Leiva-Escobar,&nbsp;Ryan T. Demmer,&nbsp;Panos N. Papapanou,&nbsp;Thomas Kocher,&nbsp;Birte Holtfreter,&nbsp;Michael Nolde","doi":"10.1111/jre.70095","DOIUrl":"10.1111/jre.70095","url":null,"abstract":"<p>Cohort studies are vulnerable to confounding and time-related biases. Recent advances emphasize the importance of emulating a hypothetical target trial to minimize several of these biases. The target trial framework is a critical tool for designing cohort studies. It requires explicit specification of protocol components: eligibility criteria, treatment strategies, outcomes, timing of follow-up (time zero), and the causal contrast to be estimated. Critical to this framework is defining “time zero” when treatment assignment takes place and follow-up begins, which prevents prevalent user and immortal time bias and ensures proper temporal sequencing of confounders, exposure, and outcome. By guiding design, data collection, and analysis, the target trial framework helps align observational studies with the principles of randomized trials, improving the validity and interpretability of their findings. While it cannot eliminate unmeasured confounding, this approach promotes transparent, bias-conscious cohort design, minimizes time-related bias, and supports more robust causal inference with observational data.</p>","PeriodicalId":16715,"journal":{"name":"Journal of periodontal research","volume":"61 7","pages":"811-820"},"PeriodicalIF":5.4,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jre.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147369746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}