BMC Medical Research Methodology最新文献

{"title":"Correction: Inclusion of unexposed clusters improves the precision of fixed effects analysis of stepped-wedge cluster randomized trials with binary and count outcomes.","authors":"Kenneth Menglin Lee, Grace Meijuan Yang, Yin Bun Cheung","doi":"10.1186/s12874-024-02415-y","DOIUrl":"10.1186/s12874-024-02415-y","url":null,"abstract":"","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"285"},"PeriodicalIF":3.9,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667186","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":"FAIR data management: a framework for fostering data literacy in biomedical sciences education.","authors":"Rocio Gonzalez Soltero, Debora Pino García, Alberto Bellido, Pablo Ryan, Ana I Rodríguez-Learte","doi":"10.1186/s12874-024-02404-1","DOIUrl":"10.1186/s12874-024-02404-1","url":null,"abstract":"<p><p>Data literacy, the ability to understand and effectively communicate with data, is crucial for researchers to interpret and validate data. However, low reproducibility in biomedical research is nowadays a significant issue, with major implications for scientific progress and the reliability of findings. Recognizing this, funding bodies such as the European Commission emphasize the importance of regular data management practices to enhance reproducibility. Establishing a standardized framework for statistical methods and data analysis is essential to minimize biases and inaccuracies. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aim to enhance data interoperability and reusability, promoting transparent and ethical data practices. The study presented here aimed to train postgraduate students at the Universidad Europea de Madrid in data literacy skills and FAIR principles, assessing their application in master thesis projects. A total of 46 participants, including students and mentors, were involved in the study during the 2022-2023 academic year. Students were trained to prioritize FAIR data sources and implement Data Management Plans (DMPs) during their master's thesis. An 11-item questionnaire was developed to evaluate the FAIRness of research data, showing strong internal consistency. The study found that integrating FAIR principles into educational curricula is crucial for enhancing research reproducibility and transparency. This approach equips future researchers with essential skills for navigating a data-driven scientific environment and contributes to advancing scientific knowledge.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"284"},"PeriodicalIF":3.9,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11568560/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643583","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":"A Bayesian analysis integrating expert beliefs to better understand how new evidence ought to update what we believe: a use case of chiropractic care and acute lumbar disc herniation with early surgery.","authors":"Léonie Hofstetter, Michelle Fontana, George A Tomlinson, Cesar A Hincapié","doi":"10.1186/s12874-024-02359-3","DOIUrl":"10.1186/s12874-024-02359-3","url":null,"abstract":"<p><strong>Background: </strong>A Bayesian approach may be useful in the study of possible treatment-related rare serious adverse events, particularly when there are strongly held opinions in the absence of good quality previous data. We demonstrate the application of a Bayesian analysis by integrating expert opinions with population-based epidemiologic data to investigate the association between chiropractic care and acute lumbar disc herniation (LDH) with early surgery.</p><p><strong>Methods: </strong>Experts' opinions were used to derive probability distributions of the incidence rate ratio (IRR) for acute LDH requiring early surgery associated with chiropractic care. A 'community of priors' (enthusiastic, neutral, and skeptical) was built by dividing the experts into three groups according to their perceived mean prior IRR. The likelihood was formed from the results of a population-based epidemiologic study comparing the relative incidence of acute LDH with early surgery after chiropractic care versus primary medical care, with sensitive and specific outcome case definitions and surgery occurring within 8- and 12-week time windows after acute LDH. The robustness of results to the community of priors and specific versus sensitive case definitions was assessed.</p><p><strong>Results: </strong>The most enthusiastic 25% of experts had a prior IRR of 0.42 (95% credible interval [CrI], 0.03 to 1.27), while the most skeptical 25% of experts had a prior IRR of 1.66 (95% CrI, 0.55 to 4.25). The Bayesian posterior estimates across priors and outcome definitions ranged from an IRR of 0.39 (95% CrI, 0.21 to 0.68) to an IRR of 1.40 (95% CrI, 0.52 to 2.55). With a sensitive definition of the outcome, the analysis produced results that confirmed prior enthusiasts' beliefs and that were precise enough to shift prior beliefs of skeptics. With a specific definition of the outcome, the results were not strong enough to overcome prior skepticism.</p><p><strong>Conclusion: </strong>A Bayesian analysis integrating expert beliefs highlighted the value of eliciting informative priors to better understand how new evidence ought to update prior existing beliefs. Clinical epidemiologists are encouraged to integrate informative and expert opinions representing the end-user community of priors in Bayesian analyses, particularly when there are strongly held opinions in the absence of definitive scientific evidence.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"281"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614487","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":"Multistate Markov chain modeling for child undernutrition transitions in Ethiopia: a longitudinal data analysis, 2002-2016.","authors":"Getnet Bogale Begashaw, Temesgen Zewotir, Haile Mekonnen Fenta","doi":"10.1186/s12874-024-02399-9","DOIUrl":"10.1186/s12874-024-02399-9","url":null,"abstract":"<p><strong>Background: </strong>The use of the multistate Markov chain model is a valuable tool for studying child undernutrition. This allows us to examine the trends of children's transitions from one state to multiple states of undernutrition.</p><p><strong>Objectives: </strong>In this study, our objective was to estimate the median duration for a child to first transition from one state of undernutrition to another as well as their first recurrence of undernutrition and also to analyze the typical duration of undernourishment. This involves understanding the central tendency of these transitions and durations in the context of longitudinal data.</p><p><strong>Methods: </strong>We used a longitudinal dataset from the Young Lives cohort study (YLCS), which included approximately 1997 Ethiopian children aged 1-15 years. These children were selected from five regions and followed through five survey rounds between 2002 and 2016. The surveys provide comprehensive health and nutrition data and are designed to assess childhood poverty. To analyze this dataset, we employed a Markov chain regression model. The dataset constitutes a cohort with repeated measurements, allowing us to track the transitions of individual children across different states of undernutrition over time.</p><p><strong>Results: </strong>The findings of our study indicate that 46% of children experienced concurrent underweight, stunting, and wasting (referred to as USW). The prevalence of underweight and stunted concurrent condition (US) was 18.7% at baseline, higher among males. The incidence density of undernutrition was calculated at 22.5% per year. On average, it took 3.02 months for a child in a wasting state to transition back to a normal state for the first time, followed by approximately 3.05 months for stunting and 3.89 months for underweight. It is noteworthy that the median duration of undernourishment among children in the US (underweight and stunted concurrently) state was 48.8 months, whereas those concurrently underweight and wasting experienced a median of 45.4 months in this state. Additionally, rural children (HR = 1.75; 95% CI: 1.53-1.97), those with illiterate fathers (HR = 1.50; 95% CI: 1.38-1.62) and mothers (HR = 1.45; 95% CI: 1.02-3.29), and those in households lacking safe drinking water (HR = 1.70; 95% CI: 1.26-2.14) or access to cooking fuel (HR = 1.95; 95% CI: 1.75-2.17) exhibited a higher risk of undernutrition and a slower recovery rate.</p><p><strong>Conclusions: </strong>This study revealed that rural children, especially those with illiterate parents and households lacking safe drinking water but cooking fuels, face an increased risk of undernutrition and slower recovery.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"283"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638365","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":"A study within a trial (SWAT) of clinical trial feasibility and barriers to recruitment in the United Kingdom - the CapaCiTY programme experience.","authors":"Natasha Stevens, Shiva Taheri, Ugo Grossi, Chris Emmett, Sybil Bannister, Christine Norton, Yan Yiannakou, Charles Knowles","doi":"10.1186/s12874-024-02395-z","DOIUrl":"10.1186/s12874-024-02395-z","url":null,"abstract":"<p><strong>Background: </strong>The CapaCiTY programme includes three, multi-centre, randomised controlled trials aiming to develop an evidence based adult chronic constipation treatment pathway. The trials were conducted in the United Kingdom, National Health Service, aiming to recruit 808 participants from 26 March 2015 to 31 January 2019. Sites were selected based on their responses to site feasibility questionnaires (2014-2015), a common tool employed by sponsors to assess a site's recruitment potential and ability to undertake the trial protocol. Failure to recruit the planned sample jeopardises reliability of results and wastes significant time and resources. The purpose of this study was to investigate barriers to recruitment in 2017.</p><p><strong>Methods: </strong>We conducted site feasibility assessments with thirty-nine sites prior to trial commencement. Twenty-seven were selected to participate in the CapaCiTY programme, twelve were deemed unsuitable. We compared site contracted recruitment rates with actual recruitment rates and conducted a telephone survey and analysis from 5 July to 7 December 2017 (n = 24) to understand barriers to recruitment. Three sites declined to participate in the survey.</p><p><strong>Results: </strong>At the time of survey, 15% of sites in the CapaCiTY programme were meeting recruitment targets, 85% were recruiting half or less of their target. Of these, 28% recruited no participants. The main barriers to recruitment were lack of resources, high workloads, lack of suitable participants and study design not being compatible with routine care. Despite multiple strategies employed to overcome these barriers, the trials were eventually stopped due to futility, recruiting only 34% of the programme sample size.</p><p><strong>Conclusions: </strong>Improving the reliability of site feasibility assessments could potentially save a substantial amount in failed research investments and speed up the time to delivery of new treatments. We recommend 1) investment in training researchers in conducting and completing site feasibility; 2) funders to require pilot and feasibility data in grant applications, with an emphasis on patient and public involvement in trial design; 3) conducting site feasibility assessment at the pre-award stage; 4) development of a national database of sites' previous trial recruitment performance; 5) data-driven site level assessment of recruitment potential.</p><p><strong>Trial registration: </strong>ISRCTN11791740; 16/07/2015, ISRCTN11093872; 11/11/2015, ISRCTN11747152; 30/09/2015.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"282"},"PeriodicalIF":3.9,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11566598/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142643579","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":"Accounting for bias due to outcome data missing not at random: comparison and illustration of two approaches to probabilistic bias analysis: a simulation study.","authors":"Emily Kawabata, Daniel Major-Smith, Gemma L Clayton, Chin Yang Shapland, Tim P Morris, Alice R Carter, Alba Fernández-Sanlés, Maria Carolina Borges, Kate Tilling, Gareth J Griffith, Louise A C Millard, George Davey Smith, Deborah A Lawlor, Rachael A Hughes","doi":"10.1186/s12874-024-02382-4","DOIUrl":"10.1186/s12874-024-02382-4","url":null,"abstract":"<p><strong>Background: </strong>Bias from data missing not at random (MNAR) is a persistent concern in health-related research. A bias analysis quantitatively assesses how conclusions change under different assumptions about missingness using bias parameters that govern the magnitude and direction of the bias. Probabilistic bias analysis specifies a prior distribution for these parameters, explicitly incorporating available information and uncertainty about their true values. A Bayesian bias analysis combines the prior distribution with the data's likelihood function whilst a Monte Carlo bias analysis samples the bias parameters directly from the prior distribution. No study has compared a Monte Carlo bias analysis to a Bayesian bias analysis in the context of MNAR missingness.</p><p><strong>Methods: </strong>We illustrate an accessible probabilistic bias analysis using the Monte Carlo bias analysis approach and a well-known imputation method. We designed a simulation study based on a motivating example from the UK Biobank study, where a large proportion of the outcome was missing and missingness was suspected to be MNAR. We compared the performance of our Monte Carlo bias analysis to a principled Bayesian bias analysis, complete case analysis (CCA) and multiple imputation (MI) assuming missing at random.</p><p><strong>Results: </strong>As expected, given the simulation study design, CCA and MI estimates were substantially biased, with 95% confidence interval coverages of 7-48%. Including auxiliary variables (i.e., variables not included in the substantive analysis that are predictive of missingness and the missing data) in MI's imputation model amplified the bias due to assuming missing at random. With reasonably accurate and precise information about the bias parameter, the Monte Carlo bias analysis performed as well as the Bayesian bias analysis. However, when very limited information was provided about the bias parameter, only the Bayesian bias analysis was able to eliminate most of the bias due to MNAR whilst the Monte Carlo bias analysis performed no better than the CCA and MI.</p><p><strong>Conclusion: </strong>The Monte Carlo bias analysis we describe is easy to implement in standard software and, in the setting we explored, is a viable alternative to a Bayesian bias analysis. We caution careful consideration of choice of auxiliary variables when applying imputation where data may be MNAR.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"278"},"PeriodicalIF":3.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614505","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":"A comparison of time-varying propensity score vs sequential stratification approaches to longitudinal matching with a time-varying treatment.","authors":"Morgan Richey, Matthew L Maciejewski, Lindsay Zepel, David Arterburn, Aniket Kawatkar, Caroline E Sloan, Valerie A Smith","doi":"10.1186/s12874-024-02391-3","DOIUrl":"10.1186/s12874-024-02391-3","url":null,"abstract":"<p><strong>Background: </strong>Methods for matching in longitudinal cohort studies, such as sequential stratification and time-varying propensity scores, facilitate causal inferences in the context of time-dependent treatments that are not randomized where patient eligibility or treatment status changes over time. The tradeoffs in available approaches have not been compared previously, so we compare two methods using simulations based on a retrospective cohort of patients eligible for weight loss surgery, some of whom received it.</p><p><strong>Methods: </strong>This study compares matching completeness, bias, coverage, and precision among three approaches to longitudinal matching: (1) time-varying propensity scores (tvPS), (2) sequential stratification that matches exactly on all covariates used in tvPS (SS-Full) and (3) sequential stratification that exact matches on a subset of covariates (SS-Selected). These comparisons are made in the context of a deep sampling frame (50:1) and a shallow sampling frame (5:1) of eligible comparators. A simulation study was employed to estimate the relative performance of these approaches.</p><p><strong>Results: </strong>In 1,000 simulations each, tvPS retained more than 99.9% of treated patients in both the deep and shallow sampling frames, while a smaller proportion of treated patients were retained for SS-Full (91.6%) and SS-Selected (98.2%) in the deep sampling frame. In the shallow sampling frame, sequential stratification retained many fewer treated patients (73.9% SS-Full, 92.0% SS-Selected) than tvPS yet coverage, precision and bias were comparable for tvPS, SS-Full and SS-Selected in the deep and shallow sampling frames.</p><p><strong>Conclusion: </strong>Time-varying propensity scores have comparable performance to sequential stratification in terms of coverage, bias, and precision, with superior match completeness. While performance was generally comparable across methods, greater match completeness makes tvPS an attractive option for longitudinal matching studies where external validity is highly valued.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"280"},"PeriodicalIF":3.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614490","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":"geessbin: an R package for analyzing small-sample binary data using modified generalized estimating equations with bias-adjusted covariance estimators.","authors":"Ryota Ishii, Tomohiro Ohigashi, Kazushi Maruo, Masahiko Gosho","doi":"10.1186/s12874-024-02368-2","DOIUrl":"10.1186/s12874-024-02368-2","url":null,"abstract":"<p><strong>Background: </strong>The generalized estimating equation (GEE) method is widely used for analyzing longitudinal and clustered data. Although the GEE estimate for regression coefficients and sandwich covariance estimate are consistent regardless of the choice of covariance structure, they are generally biased for small sample sizes. Various researchers have proposed modified GEE methods and covariance estimators to handle small-sample bias.</p><p><strong>Results: </strong>We briefly present bias-corrected and penalized GEE methods, along with 11 bias-adjusted covariance estimators. In addition, we focus on analyzing longitudinal or clustered data with binary outcomes using the logit link function and introduce package geessbin in R to implement conventional and modified GEE methods with bias-adjusted covariance estimators. Finally, we illustrate the implementation and detail a usage example of the package. The package is available from the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/web/packages/geessbin/index.html .</p><p><strong>Conclusions: </strong>The geessbin package provides three GEE estimates with numerous covariance estimates. It is useful for analyzing correlated data such as longitudinal and clustered data. Additionally, the geessbin is designed to be user-friendly, making it accessible to non-statisticians.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"277"},"PeriodicalIF":3.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11558877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614460","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":"Evaluating the current methodological practices and issues in existing literature in pooling complex surveys: a systematic review.","authors":"Md Sabbir Ahmed Mayen, Salwa Nawsheen Nisha, Sumya Afrin, Tanvir Ahammed, Muhammad Abdul Baker Chowdhury, Md Jamal Uddin","doi":"10.1186/s12874-024-02400-5","DOIUrl":"10.1186/s12874-024-02400-5","url":null,"abstract":"<p><strong>Background: </strong>Pooling data from complex survey designs is increasingly used in the health and medical sciences. However, current methodological practices are not well documented in the literature while performing the pooling strategy. We aimed to review related pooling studies and evaluate the quality of pooling within the framework of specific methodological guidelines, particularly when combining complex surveys such as Demographic & Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).</p><p><strong>Methods: </strong>We performed a systematic literature search focusing on studies utilizing the pooling method with DHS and MICS survey data. These studies were selected from those published between 2010 and 2021 and were retrieved from electronic databases (PubMed and Scopus) in accordance with pre-defined inclusion criteria. Then, we extracted 355 studies for the final review and evaluated the reporting quality of the pooling strategy while considering some methodological issues.</p><p><strong>Results: </strong>The majority of studies (81.4%) reported using a pooled (one-stage) approach, while 11.8% used a separate (two-stage) approach, and 6.8% used both approaches. Approximately 63.3% of studies did not clearly describe their pooling strategy. Only 3.4% of the studies mentioned the variable harmonization process, while 66.9% addressed dealing with heterogeneity between surveys. All studies that used the separate (two-stage) approach conducted a meta-analytic procedure, while 38.1% of studies using the pooled approach employed a multilevel model. More than half of the studies (55.6%) mentioned the use of clustered standard errors. The Delta method, Bootstrap, and Taylor linearization were each applied in 11.1% of the studies for variance estimation. Survey weights, primary sampling unit (PSU) or cluster, and strata were used together in 30.5% of the studies. Survey weights were employed by 69.8%, PSU or cluster by 43.8%, and the strata variable by 31.7%. Sensitivity analysis was conducted in 16% of the studies.</p><p><strong>Conclusions: </strong>Our study revealed that fundamental methodological issues associated with pooling complex survey databases, such as the selection of pooling procedures, data harmonization, accounting for cycle effects, quality control checks, addressing heterogeneity, selecting model effects, utilizing survey design variables, and dealing with missing values, etc., were inadequately reported in the included studies. We recommend authors, readers, reviewers, and editors examine pooling studies more attentively and utilize the customized checklist developed by our study to assess the quality of future pooling studies.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"279"},"PeriodicalIF":3.9,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11562085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614531","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":"On the assessment of the ability of measurements, nowcasts, and forecasts to track changes.","authors":"Jonas Rieger, Bolin Liu, Bernd Saugel, Oliver Grothe","doi":"10.1186/s12874-024-02397-x","DOIUrl":"10.1186/s12874-024-02397-x","url":null,"abstract":"<p><strong>Background: </strong>Measurements, nowcasts, or forecasts ideally should correctly reflect changes in the values of interest. In this article, we focus on how to assess the ability of measurements, nowcasts, or forecasts to correctly predict the direction of changes in values - which we refer to as the ability to track changes (ATC).</p><p><strong>Methods: </strong>We review and develop visual techniques and quantitative measures to assess ATC. Extensions for noisy data and estimation uncertainty are implemented using bootstrap confidence intervals and exclusion areas.</p><p><strong>Results: </strong>We exemplarily illustrate the proposed methods to assess the ability to track changes for nowcasting during the COVID-19 pandemic, patient admissions to an emergency department, and non-invasive blood pressure measurements. The proposed methods effectively evaluate ATC across different applications.</p><p><strong>Conclusions: </strong>The developed ATC assessment methods offer a comprehensive toolkit for evaluating the ATC of measurements, nowcasts, and forecasts. These techniques provide valuable insights into model performance, complementing traditional accuracy measures and enabling more informed decision-making in various fields, including public health, healthcare management, and medical diagnostics.</p>","PeriodicalId":9114,"journal":{"name":"BMC Medical Research Methodology","volume":"24 1","pages":"275"},"PeriodicalIF":3.9,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11555927/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142614484","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}