Methods of Information in Medicine最新文献

{"title":"One Digital Health for more FAIRness.","authors":"Oscar Tamburis,&nbsp;Arriel Benis","doi":"10.1055/a-1938-0533","DOIUrl":"https://doi.org/10.1055/a-1938-0533","url":null,"abstract":"<p><strong>Background: </strong>One Digital Health (ODH) aims to propose a framework that merges One Health's and Digital Health's specific features into an innovative landscape. FAIR (Findable, Accessible, Interoperable, and Reusable) principles consider applications and computational agents (or, in other terms, data, metadata, and infrastructures) as stakeholders with the capacity to find, access, interoperate, and reuse data with none or minimal human intervention.</p><p><strong>Objectives: </strong>This paper aims to elicit how the ODH framework is compliant with FAIR principles and metrics, providing some thinking guide to investigate and define whether adapted metrics need to be figured out for an effective ODH Intervention setup.</p><p><strong>Methods: </strong>An integrative analysis of the literature was conducted to extract instances of the need-or of the eventual already existing deployment-of FAIR principles, for each of the three layers (keys, perspectives and dimensions) of the ODH framework. The scope was to assess the extent of scatteredness in pursuing the many facets of FAIRness, descending from the lack of a unifying and balanced framework.</p><p><strong>Results: </strong>A first attempt to interpret the different technological components existing in the different layers of the ODH framework, in the light of the FAIR principles, was conducted. Although the mature and working examples of workflows for data FAIRification processes currently retrievable in the literature provided a robust ground to work on, a nonsuitable capacity to fully assess FAIR aspects for highly interconnected scenarios, which the ODH-based ones are, has emerged. Rooms for improvement are anyway possible to timely deal with all the underlying features of topics like the delivery of health care in a syndemic scenario, the digital transformation of human and animal health data, or the digital nature conservation through digital technology-based intervention.</p><p><strong>Conclusions: </strong>ODH pillars account for the availability (findability, accessibility) of human, animal, and environmental data allowing a unified understanding of complex interactions (interoperability) over time (reusability). A vision of integration between these two worlds, under the vest of ODH Interventions featuring FAIRness characteristics, toward the development of a systemic lookup of health and ecology in a digitalized way, is therefore auspicable.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e116-e124"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/85/69/10-1055-a-1938-0533.PMC9788917.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9254115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multivariate Sequential Analytics for Cardiovascular Disease Event Prediction.","authors":"William Hsu, Jim Warren, Patricia Riddle","doi":"10.1055/s-0042-1758687","DOIUrl":"10.1055/s-0042-1758687","url":null,"abstract":"<p><strong>Background: </strong>Automated clinical decision support for risk assessment is a powerful tool in combating cardiovascular disease (CVD), enabling targeted early intervention that could avoid issues of overtreatment or undertreatment. However, current CVD risk prediction models use observations at baseline without explicitly representing patient history as a time series.</p><p><strong>Objective: </strong>The aim of this study is to examine whether by explicitly modelling the temporal dimension of patient history event prediction may be improved.</p><p><strong>Methods: </strong>This study investigates methods for multivariate sequential modelling with a particular emphasis on long short-term memory (LSTM) recurrent neural networks. Data from a CVD decision support tool is linked to routinely collected national datasets including pharmaceutical dispensing, hospitalization, laboratory test results, and deaths. The study uses a 2-year observation and a 5-year prediction window. Selected methods are applied to the linked dataset. The experiments performed focus on CVD event prediction. CVD death or hospitalization in a 5-year interval was predicted for patients with history of lipid-lowering therapy.</p><p><strong>Results: </strong>The results of the experiments showed temporal models are valuable for CVD event prediction over a 5-year interval. This is especially the case for LSTM, which produced the best predictive performance among all models compared achieving AUROC of 0.801 and average precision of 0.425. The non-temporal model comparator ridge classifier (RC) trained using all quarterly data or by aggregating quarterly data (averaging time-varying features) was highly competitive achieving AUROC of 0.799 and average precision of 0.420 and AUROC of 0.800 and average precision of 0.421, respectively.</p><p><strong>Conclusion: </strong>This study provides evidence that the use of deep temporal models particularly LSTM in clinical decision support for chronic disease would be advantageous with LSTM significantly improving on commonly used regression models such as logistic regression and Cox proportional hazards on the task of CVD event prediction.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e149-e171"},"PeriodicalIF":1.3,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/af/00/10-1055-s-0042-1758687.PMC9788915.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9247803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAIR Aspects of a Health Information Protection and Management System.","authors":"Jaime Delgado,&nbsp;Silvia Llorente","doi":"10.1055/s-0042-1758765","DOIUrl":"https://doi.org/10.1055/s-0042-1758765","url":null,"abstract":"<p><strong>Background: </strong>Privacy management is a key issue when dealing with storage and distribution of health information. However, FAIR (Findability, Accessibility, Interoperability, and Reusability) principles when sharing information are in increasing demand in several organizations, especially for information generated in public-funded research projects.</p><p><strong>Objectives: </strong>The two main objectives of the presented work are the definition of a secure and interoperable modular architecture to manage different kinds of medical content (xIPAMS [x, for Any kind of content, Information Protection And Management System] and HIPAMS [Health Information Protection And Management System]), and the application of FAIR principles to that architecture in such a way that privacy and security are compatible with FAIR.</p><p><strong>Methods: </strong>We propose the concept of xIPAMS as a modular architecture, following standards for interoperability, which defines mechanisms for privacy, protection, storage, search, and access to health-related information.</p><p><strong>Results: </strong>xIPAMS provides FAIR principles and preserves patient's privacy. For each module, we identify how FAIR principles apply.</p><p><strong>Conclusions: </strong>We have analyzed how xIPAMS, and in particular HIPAMS (Health content), support the FAIR principles focusing on security and privacy. We have identified the FAIR principles supported by the different xIPAMS modules, concluding that the four principles are supported. Our analysis has also considered a possible implementation based on the concept of DACS (Document Access and Communication System), a system storing medical documents in a private and secure way. In addition, we have analyzed security aspects of the FAIRification process and how they are provided by xIPAMS modules.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 02","pages":"e172-e182"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/99/13/10-1055-s-0042-1758765.PMC9788908.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9609042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DxGenerator: An Improved Differential Diagnosis Generator for Primary Care Based on MetaMap and Semantic Reasoning.","authors":"Ali Sanaeifar,&nbsp;Saeid Eslami,&nbsp;Mitra Ahadi,&nbsp;Mohsen Kahani,&nbsp;Hassan Vakili Arki","doi":"10.1055/a-1905-5639","DOIUrl":"https://doi.org/10.1055/a-1905-5639","url":null,"abstract":"<p><strong>Background: </strong>In recent years, researchers have used many computerized interventions to reduce medical errors, the third cause of death in developed countries. One of such interventions is using differential diagnosis generators in primary care, where physicians may encounter initial symptoms without any diagnostic presuppositions. These systems generate multiple diagnoses, ranked by their likelihood. As such, these reports' accuracy can be determined by the location of the correct diagnosis in the list.</p><p><strong>Objective: </strong>This study aimed to design and evaluate a novel practical web-based differential diagnosis generator solution in primary care.</p><p><strong>Methods: </strong>In this research, a new online clinical decision support system, called DxGenerator, was designed to improve diagnostic accuracy; to this end, an attempt was made to converge a semantic database with the unified medical language system (UMLS) knowledge base, using MetaMap tool and natural language processing. In this regard, 120 diseases of gastrointestinal organs causing abdominal pain were modeled into the database. After designing an inference engine and a pseudo-free-text interactive interface, 172 patient vignettes were inputted into DxGenerator and ISABEL, the most accurate similar system. The Wilcoxon signed ranked test was used to compare the position of correct diagnoses in DxGenerator and ISABEL. The α level was defined as 0.05.</p><p><strong>Results: </strong>On a total of 172 vignettes, the mean and standard deviation of correct diagnosis positions improved from 4.2 ± 5.3 in ISABEL to 3.2 ± 3.9 in DxGenerator. This improvement was significant in the subgroup of uncommon diseases (<i>p</i>-value < 0.05).</p><p><strong>Conclusion: </strong>Using UMLS knowledge base and MetaMap Tools can improve the accuracy of diagnostic systems in which terms are entered in a free text manner. Applying these new methods will help the medical community accept medical diagnostic systems better.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 5-06","pages":"174-184"},"PeriodicalIF":1.7,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data.","authors":"Brian L Thomas, Lawrence B Holder, Diane J Cook","doi":"10.1055/s-0042-1756649","DOIUrl":"10.1055/s-0042-1756649","url":null,"abstract":"<p><strong>Background: </strong>Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.</p><p><strong>Objective: </strong>The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.</p><p><strong>Methods: </strong>We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.</p><p><strong>Results: </strong>We validate our approach using continuous smartwatch data for <i>n</i> = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from <i>r</i> = 0.1230 to <i>r</i> = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 3-04","pages":"99-110"},"PeriodicalIF":1.3,"publicationDate":"2022-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9847015/pdf/nihms-1862055.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10616391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reliability of cusp angulation using three-dimensional (3D) digital models：A Preliminary In Vitro Study.","authors":"Xinggang Liu, Xiao-xian Chen","doi":"10.1055/a-1868-6555","DOIUrl":"https://doi.org/10.1055/a-1868-6555","url":null,"abstract":"Background At present, artificial intelligence (AI) is incrementally used in clinical data analysis and clinical decision-making. Dental cusp angulation provide valuable insight into chewing efficiency and prosthesis safety issues. AI-enable computing cusp angles have potential important value but there is no reliable digital measurement method at present. Objectives To establish a digital method for measuring cusp angles and investigate the inter-rater and intra-rater reliability. Methods Two cusp angles (angle α and angle β) of the first molar were measured on 21 plaster casts using a goniometer, and on their corresponding digital models using PicPick software after scanning with a CEREC Bluecam three-dimensional (3D) intraoral scanner. Means±standard deviations as well as intraclass correlation coefficients (ICCs) and Pearson's correlation coefficients (PCCs) were calculated and paired sample t-test was carried out. Results Angle α was 139.19°±13.86°, angle β was 19.25°±6.86°. A very strong positive correlation between the two methods was found when the examiner was experienced (r>0.914; p<0.05), and no significant difference between the two methods was found using the paired sample t-test (p>0.20). For inter-rater and intra-rater assessments, the PCC and ICC of the cusp angulation using the digital method showed that 15 of 16 values were higher than the corresponding values measured on traditional plaster casts. However, both measurement methods showed weak positive correlation (r<0.501) and significant differences (p=0.00) for repeated measurements of angle β at two different time points by an inexperienced examiner. Conclusionss Cusp angulation using 3D digital models was a clinical option and appeared to improve the reliability of cusp angulation compared with measuring plaster casts using a goniometer. Intra-rater variability was still evident in measuring small cusp angles using the digital model.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2022-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43806671","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Privacy-Preserving Artificial Intelligence Techniques in Biomedicine.","authors":"Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Spaeth, Nina Kerstin Wenke, Jan Baumbach","doi":"10.1055/s-0041-1740630","DOIUrl":"10.1055/s-0041-1740630","url":null,"abstract":"<p><strong>Background: </strong>Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g., in the interpretation of next-generation sequencing data and in the design of clinical decision support systems.</p><p><strong>Objectives: </strong>However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy.</p><p><strong>Method: </strong>This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems.</p><p><strong>Conclusion: </strong>As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy-preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 01","pages":"e12-e27"},"PeriodicalIF":1.3,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/dd/7f/10-1055-s-0041-1740630.PMC9246509.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9246732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Privacy-Preserving Distributed Analytics Platform for Health Care Data.","authors":"Sascha Welten,&nbsp;Yongli Mou,&nbsp;Laurenz Neumann,&nbsp;Mehrshad Jaberansary,&nbsp;Yeliz Yediel Ucer,&nbsp;Toralf Kirsten,&nbsp;Stefan Decker,&nbsp;Oya Beyan","doi":"10.1055/s-0041-1740564","DOIUrl":"https://doi.org/10.1055/s-0041-1740564","url":null,"abstract":"<p><strong>Background: </strong>In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest.</p><p><strong>Objective: </strong>We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location.</p><p><strong>Methods: </strong>In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers.</p><p><strong>Results: </strong>We show that our infrastructure enables the training of data models based on distributed data sources.</p><p><strong>Conclusion: </strong>Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 S 01","pages":"e1-e11"},"PeriodicalIF":1.7,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ftp.ncbi.nlm.nih.gov/pub/pmc/oa_pdf/66/85/10-1055-s-0041-1740564.PMC9246511.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9253597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security and Privacy in Distributed Health Care Environments","authors":"Stephen Flowerday, C. Xenakis","doi":"10.1055/s-0042-1744484","DOIUrl":"https://doi.org/10.1055/s-0042-1744484","url":null,"abstract":"There is an increasing demand for distributed health care systems. Nevertheless, distributed health care environments do not come without risks. At the same time that distributed health care systems are growing, so are the cybersecurity threats targeting them. Additionally, the demand for compliance to new regulations increases as these distributed health caresystemshold sensitivepatientdata. Theuseofdata-driven technologies presents a promising opportunity for significant advances in the field toward improved health care for patients and the general public.1,2 Several recent studies have highlighted the importance and the necessity of developing a data-driven approach where patient data are collected, analyzed, and leveraged for medical research purposes with the help of different types of artificial intelligence. To address the privacy-related challenges, novel methods, such as protection of personal health information, ensuring compliance, guaranteeing FAIR information processing, and building of trust, are required. In this issue, newparadigmsandprominent applications are presented for secure, trustworthy, and privacy-preserving data sharing and knowledge representation to address the emerging needs.","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 1","pages":"1 - 2"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42386958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comparison of Methods to Detect Changes in Prediction Models.","authors":"Erin M Schnellinger,&nbsp;Wei Yang,&nbsp;Michael O Harhay,&nbsp;Stephen E Kimmel","doi":"10.1055/s-0042-1742672","DOIUrl":"https://doi.org/10.1055/s-0042-1742672","url":null,"abstract":"<p><strong>Background: </strong>Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed.</p><p><strong>Methods: </strong>We simulated data, informed by post-lung transplant mortality data and tested the following two approaches for detecting model change: (1) the \"Direct Approach,\" it compares coefficients of the model refit on recent data to those at baseline; and (2) \"Calibration Regression,\" it fits a logistic regression model of the log-odds of the observed outcomes versus the linear predictor from the baseline model (i.e., the log-odds of the predicted probabilities obtained from the baseline model) and tests whether the intercept and slope differ from 0 and 1, respectively. Four scenarios were simulated using logistic regression for binary outcomes as follows: (1) we fixed all model parameters, (2) we varied the outcome prevalence between 0.1 and 0.2, (3) we varied the coefficient of one of the ten predictors between 0.2 and 0.4, and (4) we varied the outcome prevalence and coefficient of one predictor simultaneously.</p><p><strong>Results: </strong>Calibration regression tended to detect changes sooner than the Direct Approach, with better performance (e.g., larger proportion of true claims). When the sample size was large, both methods performed well. When two parameters changed simultaneously, neither method performed well.</p><p><strong>Conclusion: </strong>Neither change detection method examined here proved optimal under all circumstances. However, our results suggest that if one is interested in detecting a change in overall incidence of an outcome (e.g., intercept), the Calibration Regression method may be superior to the Direct Approach. Conversely, if one is interested in detecting a change in other model covariates (e.g., slope), the Direct Approach may be superior.</p>","PeriodicalId":49822,"journal":{"name":"Methods of Information in Medicine","volume":"61 1-02","pages":"19-28"},"PeriodicalIF":1.7,"publicationDate":"2022-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413959/pdf/nihms-1887521.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9976306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}