Computer methods and programs in biomedicine update最新文献

{"title":"Robust lung segmentation in Chest X-ray images using modified U-Net with deeper network and residual blocks","authors":"Wiley Tam ,&nbsp;Paul Babyn ,&nbsp;Javad Alirezaie","doi":"10.1016/j.cmpbup.2025.100211","DOIUrl":"10.1016/j.cmpbup.2025.100211","url":null,"abstract":"<div><div>Lung diseases remain a leading cause of mortality worldwide, as evidenced by statistics from the World Health Organization (WHO). The limited availability of radiologists to interpret Chest X-ray (CXR) images for diagnosing common lung conditions poses a significant challenge, often resulting in delayed diagnosis and treatment. In response, Computer-Aided Diagnostic (CAD) tools can be used to potentially streamline and expedite the diagnostic process. Recently, deep learning techniques have gained prominence in the automated analysis of CXR images, particularly in segmenting lung regions as a critical preliminary step. This study aims to develop and evaluate a lung segmentation model based on a modified U-Net architecture. The architecture leverages techniques such as transfer learning with DenseNet201 as a feature extractor alongside dilated convolutions and residual blocks. An ablation study was conducted to evaluate these architectural components, along with additional elements like augmented data, alternative backbones, and attention mechanisms. Numerous and extensive experiments were performed on two publicly available datasets, the Montgomery County (MC) and Shenzhen Hospital (SH) datasets, to validate the efficacy of these techniques on segmentation performance. Outperforming other state-of-the-art methods on the MC dataset, the proposed model achieved a Jaccard Index (IoU) of 97.77 and a Dice Similarity Coefficient (DSC) of 98.87. These results represent a significant improvement over the baseline U-Net, with gains of 3.37% and 1.75% in IoU and DSC, respectively. These findings highlight the importance of architectural enhancements in deep learning-based lung segmentation models, contributing to more efficient, accurate, and reliable CAD systems for lung disease assessment.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144780982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Validating and updating GRASP: An evidence-based framework for grading and assessment of clinical predictive tools","authors":"Mohamed Khalifa ,&nbsp;Farah Magrabi ,&nbsp;Blanca Gallego","doi":"10.1016/j.cmpbup.2024.100161","DOIUrl":"10.1016/j.cmpbup.2024.100161","url":null,"abstract":"<div><h3>Background</h3><div>When selecting clinical predictive tools, clinicians are challenged with an overwhelming and ever-growing number, most of which have never been implemented or evaluated for effectiveness. The authors developed an evidence-based framework for grading and assessment of predictive tools (GRASP). The objective of this study is to refine, validate GRASP, and assess its reliability for consistent application.</div></div><div><h3>Methods</h3><div>A mixed-methods study was conducted, involving an initial web-based survey for feedback from a wide group of international experts in clinical prediction to refine the GRASP framework, followed by reliability testing with two independent researchers assessing eight predictive tools. The survey involved 81 experts who rated agreement with the framework's criteria on a five-point Likert scale and provided qualitative feedback. The reliability of the GRASP framework was evaluated through interrater reliability testing using Spearman's rank correlation coefficient.</div></div><div><h3>Results</h3><div>The survey yielded strong agreement of the experts with the framework's evaluation criteria, overall average score: 4.35/5, highlighting the importance of predictive performance, usability, potential effect, and post-implementation impact in grading clinical predictive tools. Qualitative feedback led to significant refinements, including detailed categorisation of evidence levels and clearer representation of evaluation criteria. Interrater reliability testing showed high agreement between researchers and authors (0.994) and among researchers (0.988), indicating strong consistency in tool grading.</div></div><div><h3>Conclusion</h3><div>The GRASP framework provides a high-level, evidence-based, and comprehensive, yet simple and feasible, approach to evaluate, compare, and select the best clinical predictive tools, with strong expert agreement and high interrater reliability. It assists clinicians in selecting effective tools by grading them on the level of validation of predictive performance before implementation, usability and potential effect during planning for implementation, and post-implementation impact on healthcare processes and clinical outcomes. Future studies should focus on the framework's application in clinical settings and its impact on decision-making and guideline development.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100161"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843443","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative approach of analyzing data uncertainty in parameter estimation for a Lumpy Skin Disease model","authors":"Edwiga Renald ,&nbsp;Miracle Amadi ,&nbsp;Heikki Haario ,&nbsp;Joram Buza ,&nbsp;Jean M. Tchuenche ,&nbsp;Verdiana G. Masanja","doi":"10.1016/j.cmpbup.2025.100178","DOIUrl":"10.1016/j.cmpbup.2025.100178","url":null,"abstract":"<div><div>The livestock industry has been economically affected by the emergence and reemergence of infectious diseases such as Lumpy Skin Disease (LSD). This has driven the interest to research efficient mitigating measures towards controlling the transmission of LSD. Mathematical models of real-life systems inherit loss of information, and consequently, accuracy of their results is often complicated by the presence of uncertainties in data used to estimate parameter values. There is a need for models with knowledge about the confidence of their long-term predictions. This study has introduced a novel yet simple technique for analyzing data uncertainties in compartmental models which is then used to examine the reliability of a deterministic model of the transmission dynamics of LSD in cattle which involves investigating scenarios related to data quality for which the model parameters can be well identified. The assessment of the uncertainties is determined with the help of Adaptive Metropolis Hastings algorithm, a Markov Chain Monte Carlo (MCMC) standard statistical method. Simulation results with synthetic cases show that the model parameters are identifiable with a reasonable amount of synthetic noise, and enough data points spanning through the model classes. MCMC outcomes derived from synthetic data, generated to mimic the characteristics of the real dataset, significantly surpassed those obtained from actual data in terms of uncertainties in identifying parameters and making predictions. This approach could serve as a guide for obtaining informative real data, and adapted to target key interventions when using routinely collected data to investigate long-term transmission dynamic of a disease.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100178"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ACD-ML: Advanced CKD detection using machine learning: A tri-phase ensemble and multi-layered stacking and blending approach","authors":"Mir Faiyaz Hossain,&nbsp;Shajreen Tabassum Diya,&nbsp;Riasat Khan","doi":"10.1016/j.cmpbup.2024.100173","DOIUrl":"10.1016/j.cmpbup.2024.100173","url":null,"abstract":"<div><div>Chronic Kidney Disease (CKD), the gradual loss and irreversible damage of the kidney’s functionality, is one of the leading contributors to death and causes about 1.3 million people to die annually. It is extremely important to slow down the progression of kidney deterioration to prevent kidney dialysis or transplant. This study aims to leverage machine learning algorithms and ensemble models for early detection of CKD using the “Chronic Kidney Disease (CKD15)” and “Risk Factor Prediction of Chronic Kidney Disease (CKD21)” datasets from the UCI Machine Learning Repository. Two encoding techniques are introduced to combine the datasets, i.e., Discrete Encoding and Ranged Encoding, resulting in Discrete Merged and Ranged Merged datasets. The preprocessing stage employs normalization, class balancing with synthetic oversampling, and five feature selection techniques, including RFECV and Pearson Correlation. This work proposes a novel Tri-phase Ensemble technique combining Voting, Bagging, and Stacking approaches and two other ensemble models: Multi-layer Stacking and Multi-layer Blending classifiers. The investigation reveals that, for the Discrete Merged dataset, the novel Tri-phase Ensemble and Multi-layer Stacking with layers interchanged achieves an accuracy of 99.5%. For the Ranged Merged dataset, AdaBoost attains an accuracy of 97.5%. Logistic Regression accomplishes an accuracy of 99.5% in validating with the discrete dataset, whereas for validating with the ranged dataset, both Random Forest and SVM achieve 100% accuracy. Finally, to interpret and understand the behavior and prediction of the model, a LIME explainer has been utilized.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100173"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical modelling and stability analysis of fractional smoking model","authors":"Zafar Iqbal ,&nbsp;Nauman Ahmed ,&nbsp;Abid Ali ,&nbsp;Ali Raza ,&nbsp;Muhammad Rafiq ,&nbsp;Ilyas Khan","doi":"10.1016/j.cmpbup.2025.100201","DOIUrl":"10.1016/j.cmpbup.2025.100201","url":null,"abstract":"<div><div>In this work, the effects and propagation of smoking in society are studied by considering the fractional tobacco smoking model. For this reason, the underlying model is investigated both analytically and numerically. The system has two equilibrium points, namely the tobacco-free and endemic equilibrium points. Furthermore, the stability of the model is observed by applying the Jacobian matrix technique. For numerical study, the non-standard finite difference scheme (NSFD) is hybridized with the Grunwald-Letnikov (GL) approximation for the Caputo differential operator. The key features of the continuous model are examined for the projected GL-NSFD scheme. The numerically simulated graphs are plotted to guarantee the positivity, boundedness, and convergence towards the exact steady states. Since the integer order epidemic model cannot accurately capture the nonlinear real phenomenon. Moreover, they cannot predict the future state exactly as the integer order derivatives involved in the models are local by nature, and they do not have the memory effect or history of the system. On the contrary, the fractional order model can capture all the necessary features of the continuous model. The proposed numerical method preserves the structure of the continuous system, for instance, the positivity, boundedness and convergence toward the exact steady states. It is worth mentioning that the projected numerical scheme is consistent with the continuous system.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100201"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680166","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Unifying heterogeneous hyperspectral databases for in vivo human brain cancer classification: Towards robust algorithm development","authors":"Alberto Martín-Pérez ,&nbsp;Beatriz Martinez-Vega ,&nbsp;Manuel Villa ,&nbsp;Raquel Leon ,&nbsp;Alejandro Martinez de Ternero ,&nbsp;Himar Fabelo ,&nbsp;Samuel Ortega ,&nbsp;Eduardo Quevedo ,&nbsp;Gustavo M. Callico ,&nbsp;Eduardo Juarez ,&nbsp;César Sanz","doi":"10.1016/j.cmpbup.2025.100183","DOIUrl":"10.1016/j.cmpbup.2025.100183","url":null,"abstract":"<div><h3>Background and objective</h3><div>Cancer is one of the leading causes of death worldwide, and early and accurate detection is crucial to improve patient outcomes. Differentiating between healthy and diseased brain tissue during surgery is particularly challenging. Hyperspectral imaging, combined with machine and deep learning algorithms, has shown promise for detecting brain cancer <em>in vivo</em>. The present study is distinguished by an analysis and comparison of the performance of various algorithms, with the objective of evaluating their efficacy in unifying hyperspectral databases obtained from different cameras. These databases include data collected from various hospitals using different hyperspectral instruments, which vary in spectral ranges, spatial and spectral resolution, as well as illumination conditions. The primary aim is to assess the performance of models that respond to the limited availability of <em>in vivo</em> human brain hyperspectral data. The classification of healthy tissue, tumors and blood vessels is achieved through the utilisation of different algorithms in two databases: <em>HELICoiD</em> and <em>SLIMBRAIN</em>.</div></div><div><h3>Methods</h3><div>This study evaluated conventional and deep learning methods (<em>KNN, RF, SVM, 1D-DNN, 2D-CNN, Fast 3D-CNN,</em> and a <em>DRNN</em>), and advanced classification frameworks (<em>LIBRA</em> and <em>HELICoiD</em>) using cross-validation on 16 and 26 patients from each database, respectively.</div></div><div><h3>Results</h3><div>For individual datasets,<em>LIBRA</em> achieved the highest sensitivity for tumor classification, with values of 38 %, 72 %, and 80 % on the <em>SLIMBRAIN, HELICoiD</em> (20 bands), and <em>HELICoiD</em> (128 bands) datasets, respectively. The <em>HELICoiD</em> framework yielded the best <em>F1 Scores</em> for tumor tissue, with values of 11 %, 45 %, and 53 % for the same datasets. For the <em>Unified dataset, LIBRA</em> obtained the best results identifying the tumor, with a 40 % of sensitivity and a 30 % of <em>F1 Score</em>.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100183"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143429202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature selection based on Mahalanobis distance for early Parkinson disease classification","authors":"Mustafa Noaman Kadhim ,&nbsp;Dhiah Al-Shammary ,&nbsp;Ahmed M. Mahdi ,&nbsp;Ayman Ibaida","doi":"10.1016/j.cmpbup.2025.100177","DOIUrl":"10.1016/j.cmpbup.2025.100177","url":null,"abstract":"<div><div>Standard classifiers struggle with high-dimensional datasets due to increased computational complexity, difficulty in visualization and interpretation, and challenges in handling redundant or irrelevant features. This paper proposes a novel feature selection method based on the Mahalanobis distance for Parkinson's disease (PD) classification. The proposed feature selection identifies relevant features by measuring their distance from the dataset's mean vector, considering the covariance structure. Features with larger Mahalanobis distances are deemed more relevant as they exhibit greater discriminative power relative to the dataset's distribution, aiding in effective feature subset selection. Significant improvements in classification performance were observed across all models. On the \"Parkinson Disease Classification Dataset\", the feature set was reduced from 22 to 11 features, resulting in accuracy improvements ranging from 10.17 % to 20.34 %, with the K-Nearest Neighbors (KNN) classifier achieving the highest accuracy of 98.31 %. Similarly, on the \"Parkinson Dataset with Replicated Acoustic Features\", the feature set was reduced from 45 to 18 features, achieving accuracy improvements ranging from 1.38 % to 13.88 %, with the Random Forest (RF) classifier achieving the best accuracy of 95.83 %. By identifying convergence features and eliminating divergence features, the proposed method effectively reduces dimensionality while maintaining or improving classifier performance. Additionally, the proposed feature selection method significantly reduces execution time, making it highly suitable for real-time applications in medical diagnostics, where timely and accurate disease identification is critical for improving patient outcomes.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100177"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143179429","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A sustainable neuromorphic framework for disease diagnosis using digital medical imaging","authors":"Rutwik Gulakala,&nbsp;Marcus Stoffel","doi":"10.1016/j.cmpbup.2024.100171","DOIUrl":"10.1016/j.cmpbup.2024.100171","url":null,"abstract":"<div><h3>Background and objective:</h3><div>In the diagnosis of medical images, neural network classifications can support rapid diagnosis together with existing imaging methods. Although current state-of-the-art deep learning methods can contribute to this image recognition, the aim of the present study is to develop a general classification framework with brain-inspired neural networks. Following this intention, spiking neural network models, also known as third-generation models, are included here to capitalize on their sparse characteristics and capacity to significantly decrease energy consumption. Inspired by the recent development of neuromorphic hardware, a sustainable neural network framework is proposed, leading to an energy reduction down to a thousandth compared to the current state-of-the-art second-generation counterpart of artificial neural networks. Making use of sparse signal transmissions as in the human brain, a neuromorphic algorithm for imaging diagnostics is introduced.</div></div><div><h3>Methods:</h3><div>A novel, sustainable, brain-inspired spiking neural network is proposed to perform the multi-class classification of digital medical images. The framework comprises branched and densely connected layers described by a Leaky-Integrate and Fire (LIF) neuron model. Backpropagation of discontinuous spiking activations in the forward pass is achieved by surrogate gradients, in this case, fast sigmoid. The data for the spiking neural network is encoded into binary spikes with a latency encoding strategy. The proposed model is evaluated on a publicly available dataset of digital X-rays of chest and compared with an equivalent classical neural network. The models are trained using enhanced and pre-processed X-ray images and are evaluated based on classification metrics.</div></div><div><h3>Results:</h3><div>The proposed neuromorphic framework had an extremely high classification accuracy of 99.22<span><math><mtext>%</mtext></math></span> on an unseen test set, together with high precision and recall figures. The framework achieves this accuracy, all the while consuming 1000 times less electrical power than classical neural network architectures.</div></div><div><h3>Conclusion:</h3><div>Though there is a loss of information due to encoding, the proposed neuromorphic framework has achieved accuracy close to its second-generation counterpart. Therefore, the benefit of the proposed framework is the high accuracy of classification while consuming a thousandth of the power, enabling a sustainable and accessible add-on for the available diagnostic tools, such as medical imaging equipment, to achieve rapid diagnosis.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"7 ","pages":"Article 100171"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143180351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing laboratory data for poliovirus eradication: contributions of the Africa regional polio laboratory data management team, 2022 – 2024","authors":"Brook Tesfaye,&nbsp;Reggis Katsande,&nbsp;Doungmo Wakem Yannick Arthur,&nbsp;Julius E Chia,&nbsp;Chefor Ymele Demeveng Derrick,&nbsp;Ikeonu Obianuju Caroline,&nbsp;Kabore Sakma,&nbsp;Mahmud Zubairu,&nbsp;Busisiwe Ngobe,&nbsp;Abdulahi Walla Hamisu,&nbsp;Ticha Johnson Muluh,&nbsp;Kebba Touray,&nbsp;Modjirom Ndoutabe,&nbsp;Jamal A Ahmed,&nbsp;Anfumbom Kfutwah","doi":"10.1016/j.cmpbup.2025.100214","DOIUrl":"10.1016/j.cmpbup.2025.100214","url":null,"abstract":"<div><h3>Background and Objectives</h3><div>Polio laboratory data is crucial in providing timely and accurate information on poliovirus outbreaks and therefore an important component of the overall poliovirus eradication strategies. This paper discusses the contributions of the Africa Regional Polio Laboratory Data Management Team (RPLDMT) in optimizing data-driven polio eradication efforts in the African region from 2022 to 2024.</div></div><div><h3>Methods</h3><div>We explored key data management activities performed by the RPLDMT from 2022 to 2024 and assessed their contribution on enhancing polio eradication efforts in the African region.</div></div><div><h3>Results</h3><div>The RPLDMT has significantly advanced polio eradication efforts in Africa through multiple initiatives. Notably, the team has supported the Africa Regional Emergency Operations Center (EOC) by providing 218 daily line lists of polioviruses identified, improving real-time case tracking and decision-making. The integration of Open Data Kit (ODK), an open-source electronic data collection tool, has enhanced poliovirus environmental surveillance, benefitting 23 countries in 2022, 13 in 2023, and 14 as of August 2024. The development of a sophisticated automated data quality assurance script has improved data accuracy and reliability, with 65 weekly line lists of errors provided for data correction. Additionally, the introduction of the biweekly Africa Regional Polio Laboratory Network (ARPLN) bulletin and real-time dashboards has optimized data use, aiding in actionable insights and decision-making. Efforts to transition to the Web-based Information for Action (WebIFA) system and capacity building through training workshops have further strengthened data management and surveillance capabilities across the region.</div></div><div><h3>Conclusion</h3><div>The contributions provided by the RPLDMT has played a key role in boosting the polio eradication efforts with a focus on enhancing human resource skills embracing new technologies and implementing real-time performance monitoring tools to improve data quality and strengthen data-driven decision-making processes essential for speeding up the progress towards eradicating polio in the region.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144766934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"R package to estimate intracluster correlation coefficient for nominal and ordinal data","authors":"Hrishikesh Chakraborty ,&nbsp;Nicole Solomon","doi":"10.1016/j.cmpbup.2025.100200","DOIUrl":"10.1016/j.cmpbup.2025.100200","url":null,"abstract":"<div><h3>Background and Objective</h3><div>: The intracluster correlation coefficient (ICC) is a critical parameter to assess the degree of similarity or correlation between observations within the same cluster or group. It is commonly applied in cluster-randomized trials to estimate average within-cluster correlation. Although methods to estimate ICC exist for binary, continuous, and survival data, a new resampling-based approach has been developed for nominal or ordinal responses with more than two categories. The objective of this paper is to present both the resampling methods estimator and method of moments (MoM) based estimator for categorical ICC estimation. To facilitate the adoption and use of these estimators we developed an R package, <span>iccmult</span>, which calculates the ICC point estimate and confidence interval (CI) for categorical response data under each of these two methods.</div></div><div><h3>Methods</h3><div>: In this paper we incorporated the resampling based estimation method and MoM originally developed to characterize population genetic structure. A simulation study was conducted to compare estimates from MoM to the resampling method under different event rates, varying numbers of clusters, and various cluster sizes. The <span>iccmult</span> package provides two estimates of ICC and its CI, computed using these two methods. Additionally, the package also generates clustered categorical response data.</div></div><div><h3>Results</h3><div>: The <span>iccmult</span> package provides two functions for users. The function <span>rccat()</span> generates clustered categorical data, while the function <span>iccmulti()</span> estimates ICC and its CI. The simulation study revealed that the resampling and MoM methods perform nearly identically in estimating population ICC. However, the MoM method demonstrated greater precision in scenarios with fewer clusters and smaller cluster sizes.</div></div><div><h3>Conclusions</h3><div>: The <span>R</span> package <span>iccmult</span> offers easy-to-use ways to generate clustered categorical data and estimate ICC and its CI for a nominal or ordinal response using different methods. The package is freely available for use with <span>R</span> from the CRAN repository (<span><span>https://cran.r-project.org/package=iccmult</span><svg><path></path></svg></span>). We believe that this package can be a very useful tool for researchers designing cluster randomized trials with a categorical outcome.</div></div>","PeriodicalId":72670,"journal":{"name":"Computer methods and programs in biomedicine update","volume":"8 ","pages":"Article 100200"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144711912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}