BMC Medical Research Methodology最新文献

{"title":"The use of race and ethnicity in sickle cell disease research.","authors":"Aida S Kidane Gebremeskel, Minke A Rab, Erik D van Werkhoven, Teun B Petersen, Marjon H Cnossen, Amade M'charek, Karlijn A C Meeks, Anita W Rijneveld","doi":"10.1186/s12874-025-02513-5","DOIUrl":"10.1186/s12874-025-02513-5","url":null,"abstract":"<p><p>This study explores practices surrounding the operationalization of ethno-racial categories (ERCs) as confounders in biomedical research, with a focus on sickle cell disease (SCD) as a model. ERCs, often aggregate labels encompassing diverse individuals which raises questions about their relevance as confounders. Given SCD's racialization as a \"Black\" disease, understanding ERC utilization is crucial. This study analyzed 1,105 SCD studies published globally. Data were collected on whether ERC adjustment was employed, regional variations in ERC-adjustment rates, labels used for ERCs, rationales provided for ERC matching, and methods used for ERC determination. 28% of the studies utilized ERC adjustment, with significant regional disparities (p < 0.001). Notably, Western studies showed higher rates of ERC adjustment compared to other regions. However, crucial details such as ERC labels and methodology were frequently missing. Commonly used labels included \"African\" or \"Black.\" Only 7% of studies provided explicit rationales for ERC matching, and 70% did not specify the method used for ERC determination. The findings underscore the need to adhere to guidelines on ERC operationalization in biomedicine. The lack of standardized practices raises concerns about potential biases and misinterpretations in research outcomes. Adhering to clear guidelines can mitigate the risk of perpetuating racial stereotypes and inequalities while ensuring research integrity.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"63"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584180","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":"Modeling the temporal prevalence peak drift of chronic diseases.","authors":"Jürgen Rodenkirchen, Annika Hoyer, Ralph Brinks","doi":"10.1186/s12874-025-02517-1","DOIUrl":"10.1186/s12874-025-02517-1","url":null,"abstract":"<p><strong>Background: </strong>Chronic diseases, such as type 2 diabetes, are responsible for a substantial proportion of global deaths and are marked by an increasing number of people that suffer from them. Our objective is to systematically investigate the analytical determination of the drift in prevalence peaks over calendar-time and age, with an emphasis on understanding the intrinsic attributes of temporal displacement. This aims to enhance the understanding of disease dynamics that may contribute to refining medical strategies and to plan future healthcare activities.</p><p><strong>Methods: </strong>We present two distinct yet complementary approaches for identifying and estimating drifts in prevalence peaks. First, assuming incidence and mortality rates are known, we employ a partial differential equation that relates prevalence, incidence, and mortality. From this, we derive an ordinary differential equation to mathematically describe the prevalence peak drift. Second, assuming prevalence data (rather than incidence and mortality data) are available, we establish a logistic function approach to estimate the prevalence peak drift. We applied this method to data on the prevalence of type 2 diabetes in Germany.</p><p><strong>Results: </strong>The first approach provides an exact mathematical prescription of the trajectory of the prevalence peak drift over time and age, assuming incidence and mortality rates are known. In contrast, the second approach, a practical application based on available prevalence data, demonstrates the prevalence peak dynamics in a real-world scenario. The theoretical model, together with the practical application, effectively substantiates the understanding of prevalence peak dynamics in two different scenarios.</p><p><strong>Conclusion: </strong>Our study shows the theoretical derivation and determination of prevalence peak drifts. Our findings underpin the dynamic nature of chronic disease prevalence, highlighting the importance of considering the related age-dependent trends for planning future healthcare activities.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"65"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887115/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584179","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":"How likely is unmeasured confounding to explain meta-analysis-derived associations between alcohol, other substances, and mood-related conditions with HIV risk behaviors?","authors":"Prima Manandhar-Sasaki, Kaoon Francois Ban, Emma Richard, R Scott Braithwaite, Ellen C Caniglia","doi":"10.1186/s12874-025-02490-9","DOIUrl":"10.1186/s12874-025-02490-9","url":null,"abstract":"<p><strong>Background: </strong>HIV transmission and disease progression may be driven by associations HIV risk behaviors have with a constellation of alcohol, other substance, and mood-related conditions (CASM). However, observational study-based measures of these associations are often prone to unmeasured confounding. While meta-analysis offers a systematic approach to summarize effect sizes across studies, the validity of these estimates can be compromised if similar biases exist across studies. Our analysis assesses the likelihood that unmeasured confounding explains meta-analysis-derived measures of association between CASM and HIV risk behaviors, and provides bias-adjusted estimates.</p><p><strong>Methods: </strong>We first conducted systematic reviews and meta-analyses to assess associations between CASM conditions and four HIV risk behaviors (medication non-adherence, unprotected sex, transactional sex, and multiple sexual partners). We then adjusted for potential unmeasured confounders using two methods designed for meta-analyses - Point Estimate and Proportion of Meaningfully Strong Effects methods. We selected \"risk propensity\" as an illustrative and potentially important unmeasured confounder based on the extant literature and mechanistic plausibility.</p><p><strong>Results: </strong>In analyses unadjusted for unmeasured confounding, 89% (24/27) of odds ratios (ORs) show strong evidence of a positive association, with alcohol use and stimulant use emerging as dominant risk factors for HIV risk behaviors. After adjusting for unmeasured confounding by risk propensity, 81% (22/27) of ORs still showed strong evidence of a positive association. Associations between mood-related conditions and HIV risk behaviors were more robust to unmeasured confounding than associations between alcohol use and other substance use and HIV risk behaviors.</p><p><strong>Conclusion: </strong>Despite residual confounding present in constituent studies, there remains strong evidence of associations between CASM and HIV risk behaviors as well as the clustered nature of CASM conditions. Our analysis provides an example of how to assess unmeasured confounding in meta-analysis-derived measures of association.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"62"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887180/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143584189","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":"Assessing racial disparities in healthcare expenditure using generalized propensity score weighting.","authors":"Jiajun Liu, Yi Liu, Yunji Zhou, Roland A Matsouaka","doi":"10.1186/s12874-025-02508-2","DOIUrl":"10.1186/s12874-025-02508-2","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This paper extends current propensity score weighting methods for causal inference to better understand disparities in healthcare access across multiple racial groups. By treating each racial group as a distinct entity (or \"treatment\") in the causal inference framework, we can assess and evaluate heterogeneity in healthcare outcomes across various racial or ethnic categories. Furthermore, we leverage modern propensity score weighting techniques to address the challenges inherent to multiple group evaluations, such as violations of the positivity assumption, and compare the performance of different propensity score weights.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We use generalized propensity score methods to assess racial disparities across 4 specific racial or ethnic groups: Whites, Hispanics, Asians, and Blacks. We first calculate weights that standardize the participants' characteristics and then compare their weighted outcomes. We consider four distinct measures (i.e., causal estimands) and estimation methods: the conventional average treatment effect on the treated (ATT), the ATT trimming, the ATT truncation, and the overlap weighted ATT (OWATT). These estimands are applied under a multi-valued \"treatment\" framework, where the said \"treatment\" is defined by non-manipulable racial or ethnic group memberships. Using data from the Medical Expenditure Panel Survey (MEPS), we assess disparities in healthcare expenditures across the 4 racial and ethnic groups.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;We found significant disparities in healthcare expenditure between White participants and all the other racial or ethnic groups when using OWATT and ATT truncation. Conventional ATT and ATT trimming could indicate non-significant difference due to larger variance estimates. Moreover, the conventional ATT was found to be the least efficient estimation method, even when its variance was estimated via non-parametric bootstrapping. Overall, the OWATT emerges as a promising estimation method; it retains the available information from all samples, avoids subjectivity (inherent to choosing thresholds by its competitors) and mitigates judiciously pernicious inferential effects (such as the inflated variance estimates) by extreme propensity score weights.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;We found that generalized propensity score weighting (GPSW) methods are valuable quantitative tools to standardize and compare characteristics as well as outcomes of non-manipulable groups. This helps assess disparities across multiple racial and ethnic groups, as demonstrated in this study. These methods offer flexible and semi-parametric analysis on the primary causal parameters of interest (such as the racial disparities), with straightforward and intuitive interpretations. In addition, when there is violation of the positivity assumption, OWATT serves as an excellent alternative due to its greater efficiency, evidenced by relatively smaller variance. More imp","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"64"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11887195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583711","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":"Poor reporting quality and high proportion of missing data in economic evaluations alongside pragmatic trials: a cross-sectional survey.","authors":"Yu Xin, Ruomeng Song, Jun Hao, Wentan Li, Changjin Wu, Ling Zuo, Yuanyi Cai, Xiyan Zhang, Huazhang Wu, Wen Hui","doi":"10.1186/s12874-025-02519-z","DOIUrl":"10.1186/s12874-025-02519-z","url":null,"abstract":"<p><strong>Background: </strong>Lack of data integrity is a common problem in randomized clinical trials and is more serious in economic evaluations conducted alongside explanatory clinical trials. Despite pragmatic randomized controlled trials (pRCTs) becoming recognized as the best design for economic evaluations, information on the proportion, handling approaches, and reporting quality of missing data in pRCTs-based economic evaluations remains limited. This study aimed to investigate the quantity and reporting quality of missing data in economic evaluations conducted alongside pragmatic clinical trials.</p><p><strong>Methods: </strong>In this cross-sectional survey, data were extracted from PubMed and OVID (Embase, CENTRAL, HTA database, and NHS EED) from January 1, 2010, to April 24, 2022. Economic evaluations conducted alongside pRCTs were included. Two independent reviewer groups identified relevant articles, and data were extracted by three groups comprising two reviewers each. Descriptive analyses were performed to assess the characteristics of the included studies, missingness in the included studies, and handling of missing data.</p><p><strong>Results: </strong>Overall, 715 studies were identified, of which 152 met the inclusion criteria. In total, 113, 119, and 132 articles reported missing data, costs, and effects, respectively. More than 50% (58/113) of the articles reported the proportion or quantity of overall missingness, and 64.71% and 54.55% reported missing costs and effects, respectively. The proportion of missingness of < 5% in the overall group was 3.45%, whereas the proportions of missing costs and effects were both < 10% (5.26% vs. 8.45%, respectively). In terms of the proportion of missing data, the overall missingness rate was 30.22% in 58 studies, whereas the median proportion of missing data was slightly higher than that of missing effects (30.92% vs. 27.78%). Of the included studies, 56 (36.84%) conducted a sensitivity analysis on handling missing data. Of these, 12.50% reported missing mechanisms, and 83.93% examined handling methods.</p><p><strong>Conclusions: </strong>Insufficient description and reporting of missing data, along with a high proportion of missing data in pRCT-based economic evaluations, could decrease the reliability and extrapolation of conclusions, leading to misleading decision-making.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"61"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572073","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":"Co-creation methods for public health research - characteristics, benefits, and challenges: a Health CASCADE scoping review.","authors":"Danielle Marie Agnello, Vinayak Anand-Kumar, Qingfan An, Janneke de Boer, Lea Rahel Delfmann, Giuliana Raffaella Longworth, Quentin Loisel, Lauren McCaffrey, Artur Steiner, Sebastien Chastin","doi":"10.1186/s12874-025-02514-4","DOIUrl":"10.1186/s12874-025-02514-4","url":null,"abstract":"<p><strong>Background: </strong>Co-creation engages diverse stakeholders, including marginalized populations, in collaborative problem-solving to enhance engagement and develop contextually appropriate solutions. It is increasingly recognized as a way to democratize research and improve the impact of interventions, services, and policies. However, the lack of synthesized evidence on co-creation methods limits methodological rigor and the establishment of best practices. This review aimed to identify co-creation methods in academic literature and analyze their characteristics, target groups, and associated benefits and challenges.</p><p><strong>Methods: </strong>This scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews. The search was conducted in the Health CASCADE database v1.5 (including CINAHL, PubMed, and 17 additional databases via ProQuest) from January 1970 to March 2022. Data was aggregated and summarized, with qualitative data analyzed using Braun and Clarke's six-phase thematic analysis approach.</p><p><strong>Results: </strong>The review included 266 articles, identifying 248 distinct co-creation methods published between 1998 and 2022. Most methods were rooted in participatory paradigms (147 methods), with 49 methods derived from co-approaches like co-creation, co-design, and co-production, and 11 from community-based health promotion and action research. Methods were applied across 40 target populations, including children, adults, and marginalized groups. Many methods (62.3%) were delivered face-to-face, with 40 articles incorporating digital tools. Thematic analysis revealed nine benefits, such as enhanced creativity, empowerment, and improved communication, and six challenges, including resource constraints and systemic and structural barriers.</p><p><strong>Conclusion: </strong>This review emphasizes the importance of robust documentation and analysis of co-creation methods to inform their application in public health. Findings support the development of collaborative co-creation processes that are responsive to the needs of diverse populations, thereby enhancing the overall effectiveness and cultural sensitivity of the outcomes. This review highlights the potential of co-creation methods to promote equity and inclusion while emphasizing the importance of evaluating and selecting methods tailored to specific objectives, offering a critical resource for planning, conducting, and evaluating co-creation projects.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"60"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884017/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572067","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":"Validity of using a semi-automated screening tool in a systematic review assessing non-specific effects of respiratory vaccines.","authors":"Charlie Holland, Daniel B Oakes, Mohinder Sarna, Kevin Ek Chai, Leo Ng, Hannah C Moore","doi":"10.1186/s12874-025-02511-7","DOIUrl":"10.1186/s12874-025-02511-7","url":null,"abstract":"<p><strong>Background: </strong>The abstract screening process of systematic reviews can take thousands of hours by two researchers. We aim to determine the reliability and validity of Research Screener, a semi-automated abstract screening tool within a systematic review on non-specific and broader effects of respiratory vaccines on acute lower respiratory infection hospitalisations and antimicrobial prescribing patterns in young children.</p><p><strong>Methods: </strong>We searched online databases for Medline, Embase, CINAHL, Scopus and ClinicalTrials.gov from inception until 24th January 2024. We included human studies involving non-specific and broader effects of respiratory vaccines and excluded studies investigating live-attenuated vaccines. The RS trial compared relevant abstracts flagged by RS to manual screening. RS ranks abstracts by relevance based on seed articles used to validate the search strategy. Abstracts are re-ranked following reviewers' feedback. Two reviewers screened RS independently with a third reviewer resolving conflicts; three reviewers screened manually with a fourth reviewer resolving conflicts.</p><p><strong>Results: </strong>After removal of duplicates, 9,727 articles were identified for abstract screening. Of those, 3,000 were randomly selected for screening in RS, with 18% (540) screened in RS and 100% manually. In RS, 99 relevant articles were identified. After comparing RS to manual screening and completing full-text review on 26 articles not captured by RS, 4 articles were missed by RS (2 due to human error, 2 not yet screened). Hence, RS captured articles accurately whilst reducing the screening load.</p><p><strong>Conclusions: </strong>RS is a valid and reliable tool that reduces the amount of time spent screening articles for large-scale systematic reviews. RS is a useful tool that should be considered for streamlining the process of systematic reviews.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"59"},"PeriodicalIF":3.9,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11884005/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143572083","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":"Addressing challenges with Matching-Adjusted Indirect Comparisons to demonstrate the comparative effectiveness of entrectinib in metastatic ROS-1 positive Non-Small Cell Lung Cancer.","authors":"Cyril Esnault, Louise Baschet, Vanessa Barbet, Gaëlle Chenuc, Maurice Pérol, Katia Thokagevistk, David Pau, Matthias Monnereau, Lise Bosquet, Thomas Filleron","doi":"10.1186/s12874-025-02500-w","DOIUrl":"10.1186/s12874-025-02500-w","url":null,"abstract":"<p><strong>Background: </strong>Matching Adjusted Indirect Comparison (MAIC) is a statistical method used to adjust for potential biases when comparing treatment effects between separate data sources, with aggregate data in one arm, and individual patients data in the other. However, acceptance of MAIC in health technology assessment (HTA) is challenging because of the numerous biases that can affect the estimates of treatment effects - especially with small sample sizes, increasing the risk of convergence issues. We suggest statistical approaches to address some of the challenges in supporting evidence from MAICs, applied to a case study.</p><p><strong>Methods: </strong>The proposed approaches were illustrated with a case study comparing an integrated analysis of three single-arm trials of entrectinib with the French standard of care using the Epidemio-Strategy and Medical Economics (ESME) Lung Cancer Data Platform, in metastatic ROS1-positive Non-Small Cell Lung Cancer (NSCLC) patients. To obtain convergent models with balanced treatment arms, a transparent predefined workflow for variable selection in the propensity score model, with multiple imputation of missing data, was used. To assess robustness, multiple sensitivity analyses were conducted, including Quantitative Bias Analyses (QBA) for unmeasured confounders (E-value, bias plot), and for missing at random assumption (tipping-point analysis).</p><p><strong>Results: </strong>The proposed workflow was successful in generating satisfactory models for all sub-populations, that is, without convergence problems and with effectively balanced key covariates between treatment arms. It also gave an indication of the number of models tested. Sensitivity analyses confirmed the robustness of the results, including to unmeasured confounders. The QBA performed on the missing data allowed to exclude the potential impact of the missing data on the estimate of comparative effectiveness, even though approximately half of the ECOG Performance Status data were missing.</p><p><strong>Conclusions: </strong>To the best of our knowledge, we present the first in-depth application of QBA in the context of MAIC. Despite the real-world data limitations, with this MAIC, we show that it is possible to confirm the robustness of the results by using appropriate statistical methods.</p><p><strong>Trial registration: </strong>NA.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"57"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536471","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":"The reporting quality and methodological quality of dynamic prediction models for cancer prognosis.","authors":"Peijing Yan, Zhengxing Xu, Xu Hui, Xiajing Chu, Yizhuo Chen, Chao Yang, Shixi Xu, Huijie Cui, Li Zhang, Wenqiang Zhang, Liqun Wang, Yanqiu Zou, Yan Ren, Jiaqiang Liao, Qin Zhang, Kehu Yang, Ling Zhang, Yunjie Liu, Jiayuan Li, Chunxia Yang, Yuqin Yao, Zhenmi Liu, Xia Jiang, Ben Zhang","doi":"10.1186/s12874-025-02516-2","DOIUrl":"10.1186/s12874-025-02516-2","url":null,"abstract":"<p><strong>Background: </strong>To evaluate the reporting quality and methodological quality of dynamic prediction model (DPM) studies on cancer prognosis.</p><p><strong>Methods: </strong>Extensive search for DPM studies on cancer prognosis was conducted in MEDLINE, EMBASE, and the Cochrane Library databases. The Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and the Prediction model Risk of Bias Assessment Tool (PROBAST) were used to assess reporting quality and methodological quality, respectively.</p><p><strong>Results: </strong>A total of 34 DPM studies were identified since the first publication in 2005, the main modeling methods for DPMs included the landmark model and the joint model. Regarding the reporting quality, the median overall TRIPOD adherence score was 75%. The TRIPOD items were poorly reported, especially the title (23.53%), model specification, including presentation (55.88%) and interpretation (50%) of the DPM usage, and implications for clinical use and future research (29.41%). Concerning methodological quality, most studies were of low quality (n = 30) or unclear (n = 3), mainly due to statistical analysis issues.</p><p><strong>Conclusions: </strong>The Landmark model and joint model show potential in DPM. The suboptimal reporting and methodological qualities of current DPM studies should be improved to facilitate clinical application.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"58"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872325/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536489","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":"Handling missing values in patient-reported outcome data in the presence of intercurrent events.","authors":"Doranne Thomassen, Satrajit Roychoudhury, Cecilie Delphin Amdal, Dries Reynders, Jammbe Z Musoro, Willi Sauerbrei, Els Goetghebeur, Saskia le Cessie","doi":"10.1186/s12874-025-02510-8","DOIUrl":"10.1186/s12874-025-02510-8","url":null,"abstract":"<p><strong>Introduction: </strong>As patient-reported outcomes (PROs) are increasingly used in the evaluation of medical treatments, it is important that PROs are carefully analyzed and interpreted. This may be challenging due to substantial missing values. The missingness in PROs is often closely related to patients' disease status. In that case, using observed information about intercurrent events (ICEs) such as disease progression and death will improve the handling of missing PRO data. Therefore, the aim of this study was to develop imputation models for repeated PRO measurements that leverage information about ICEs.</p><p><strong>Methods: </strong>We assumed a setting in which missing PRO measurements are missing at random given observed measurements, as well as the occurrence and timing of ICEs, and potentially other (baseline or time-varying) covariates. We then showed how these missingness assumptions can be translated into concrete imputation models that also account for a longitudinal data structure. The resulting models were applied to impute anonymized PRO data from a single-arm clinical trial in patients with advanced lung cancer.</p><p><strong>Results: </strong>In our trial example, accounting for death and other ICEs in the imputation of missing data led to lower estimated mean health-related quality of life (while alive) compared to an available case analysis and a naive linear mixed model imputation.</p><p><strong>Conclusion: </strong>Information about the timing and occurrence of ICEs contribute to a more plausible handling of missing PRO data. To account for ICE information when handling missing PROs, the missing data model should be separated from the analysis model.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"25 1","pages":"56"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11872335/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143536485","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}