{"title":"Weighted Brier Score - an Overall Summary Measure for Risk Prediction Models with Clinical Utility Consideration.","authors":"Kehao Zhu, Yingye Zheng, Kwun Chuen Gary Chan","doi":"10.1007/s12561-025-09505-5","DOIUrl":"https://doi.org/10.1007/s12561-025-09505-5","url":null,"abstract":"<p><p>As advancements in novel biomarker-based algorithms and models accelerate their use in disease risk prediction, it is crucial to evaluate these models within the context of their intended clinical application. Prediction models output the absolute risk of disease; subsequently, patient counseling and shared decision-making are based on the estimated individual risk and cost-benefit assessment. The overall impact of the application is referred to as clinical utility, which received significant attention and desire to incorporate into model assessment lately. The classic Brier score is a popular measure of prediction accuracy; however, it is insufficient for effectively assessing clinical utility. To address this limitation, we propose a class of weighted Brier scores that aligns with the decision-theoretic framework of clinical utility. Additionally, we decompose the weighted Brier score into discrimination and calibration components, and we link the weighted Brier score to the <math><mi>H</mi></math> measure, which has been proposed as an alternative to the area under the receiver operating characteristic curve. This theoretical link to the <math><mi>H</mi></math> measure further supports our weighting method and underscores the essential elements of discrimination and calibration in risk prediction evaluation. The practical use of the weighted Brier score as an overall summary is demonstrated using data from a prostate cancer study.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12523994/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145309467","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":"Accounting for Competing Risks in the Assessment of Prognostic Biomarkers' Discriminative Accuracy.","authors":"Xinran Huang, Xinyang Jiang, Ruosha Li, Jing Ning","doi":"10.1007/s12561-025-09499-0","DOIUrl":"https://doi.org/10.1007/s12561-025-09499-0","url":null,"abstract":"<p><p>The discriminative performance of biomarkers often changes over time and exhibits heterogeneity across subgroups defined by patient characteristics. Assessing how this performance varies with these factors is crucial for a comprehensive evaluation of biomarkers and to identify areas for improvement in sub-populations with poor performance. Additionally, the presence of competing risks complicates the assessment of discriminative performance. Ignoring competing risks can lead to misleading conclusions, as the biomarker's performance for the event of interest, such as disease onset, may be confounded by its performance for competing events, such as death. To address these challenges, we develop a regression model to assess the impact of covariates on the discriminative performance of biomarkers, characterized by the covariate-specific time-dependent Area-undercurve (AUC) for a specific cause. We construct a pseudo partial-likelihood for estimation and inference and establish the asymptotic properties of the proposed estimators. Through simulation studies, we demonstrate the finite sample performance of these estimators, and we apply the proposed method to data from the African American Study of Kidney Disease and Hypertension (AASK).</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12366773/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144973349","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":"Bias and Efficiency Comparison between Multiple Imputation and Available-Case Analysis for Missing Data in Longitudinal Models.","authors":"Panpan Zhang, Sharon X Xie","doi":"10.1007/s12561-025-09493-6","DOIUrl":"10.1007/s12561-025-09493-6","url":null,"abstract":"<p><p>In this paper, we compare the performance of available-case analysis (ACA) and several multiple imputation (MI) approaches for handling missing data problems in longitudinal analysis through estimation bias and relative efficiency. When the missingness of covariates depends on observed responses, ACA produces estimation bias, but it is preferred when there are only missing values in longitudinal responses. Multilevel MI methods are not always a solution to longitudinal data analysis. Single-level MI methods, like fully conditional specification (FCS), provide unbiased estimates under a variety of missing data scenarios, and improve efficiency gain in certain scenarios. The general assumption of missing data mechanism is missing at random (MAR). We carry out a systematic synthetic data analysis where missing data exist in longitudinal outcomes or/and covariates under different kinds of missing data generation procedures. The analysis model is a linear mixed-effects model. For each of the missing data scenarios, we give our recommendation (between ACA and a specific MI method) based on theoretical justifications and extensive simulations. In addition, a longitudinal neurodegenerative disease dataset is used as a real case study.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12356228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144875909","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":"Covariate-Balancing-Aware Interpretable Deep Learning Models for Treatment Effect Estimation.","authors":"Kan Chen, Qishuo Yin, Qi Long","doi":"10.1007/s12561-023-09394-6","DOIUrl":"10.1007/s12561-023-09394-6","url":null,"abstract":"<p><p>Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the weighted energy distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require the correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC, as well as is examined through the study of the effect of smoking on the blood level of cadmium using NHANES.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"17 1","pages":"132-150"},"PeriodicalIF":0.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11957463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143765096","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}
Jing Zhai, Youngwon Choi, Xingyi Yang, Yin Chen, Kenneth Knox, Homer L Twigg, Joong-Ho Won, Hua Zhou, Jin J Zhou
{"title":"DeepBiome: A Phylogenetic Tree Informed Deep Neural Network for Microbiome Data Analysis.","authors":"Jing Zhai, Youngwon Choi, Xingyi Yang, Yin Chen, Kenneth Knox, Homer L Twigg, Joong-Ho Won, Hua Zhou, Jin J Zhou","doi":"10.1007/s12561-024-09434-9","DOIUrl":"10.1007/s12561-024-09434-9","url":null,"abstract":"<p><p>Evidence linking the microbiome to human health is rapidly growing. The microbiome profile has the potential as a novel predictive biomarker for many diseases. However, tables of bacterial counts are typically sparse, and bacteria are classified within a hierarchy of taxonomic levels, ranging from species to phylum. Existing tools focus on identifying microbiome associations at either the community level or a specific, pre-defined taxonomic level. Incorporating the evolutionary relationship between bacteria can enhance data interpretation. This approach allows for aggregating microbiome contributions, leading to more accurate and interpretable results. We present DeepBiome, a phylogeny-informed neural network architecture, to predict phenotypes from microbiome counts and uncover the microbiome-phenotype association network. It utilizes microbiome abundance as input and employs phylogenetic taxonomy to guide the neural network's architecture. Leveraging phylogenetic information, DeepBiome is applicable to both regression and reduces the need for extensive tuning of the deep learning architecture, minimizes overfitting, and, crucially, enables the visualization of the path from microbiome counts to disease. It classification problems. Simulation studies and real-life data analysis have shown that DeepBiome is both highly accurate and efficient. It offers deep insights into complex microbiome-phenotype associations, even with small to moderate training sample sizes. In practice, the specific taxonomic level at which microbiome clusters tag the association remains unknown. Therefore, the main advantage of the presented method over other analytical methods is that it offers an ecological and evolutionary understanding of host-microbe interactions, which is important for microbiome-based medicine. DeepBiome is implemented using Python packages Keras and TensorFlow. It is an open-source tool available at https://github.com/Young-won/DeepBiome.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"17 1","pages":"191-215"},"PeriodicalIF":0.4,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12395559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144973306","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}
Jeremy Rubin, Fan Fan, Laura Barisoni, Andrew R Janowczyk, Jarcy Zee
{"title":"Novel Scalar-on-matrix Regression for Unbalanced Feature Matrices.","authors":"Jeremy Rubin, Fan Fan, Laura Barisoni, Andrew R Janowczyk, Jarcy Zee","doi":"10.1007/s12561-025-09476-7","DOIUrl":"10.1007/s12561-025-09476-7","url":null,"abstract":"<p><p>Image features that characterize tubules from digitized kidney biopsies may offer insight into disease prognosis as novel biomarkers. For each subject, we can construct a matrix whose entries are a common set of image features (e.g., area, orientation, eccentricity) that are measured for each tubule from that subject's biopsy. Previous scalar-on-matrix regression approaches which can predict scalar outcomes using image feature matrices cannot handle varying numbers of tubules across subjects. We propose the CLUstering Structured laSSO (CLUSSO), a novel scalar-on-matrix regression technique that allows for unbalanced numbers of tubules, to predict scalar outcomes from the image feature matrices. Through classifying tubules into one of two different clusters, CLUSSO averages and weights tubular feature values within-subject and within-cluster to create balanced feature matrices that can then be used with structured lasso regression. We develop the theoretical large tubule sample properties for the error bounds of the feature coefficient estimates. Simulation study results indicate that CLUSSO often achieves a lower false positive rate and higher true positive rate for identifying the image features which truly affect outcomes relative to a naive method that averages feature values across all tubules. Additionally, we find that CLUSSO has lower bias and can predict outcomes with a competitive accuracy to the naïve approach. Finally, we applied CLUSSO to tubular image features from kidney biopsies of glomerular disease subjects from the Nephrotic Syndrome Study Network (NEPTUNE) to predict kidney function and used subjects from the Cure Glomerulonephropathy (CureGN) study as an external validation set.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12456458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145138874","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}
Paula R Langner, Elizabeth Juarez-Colunga, Lucas N Marzec, Gary K Grunwald, John D Rice
{"title":"Efficiency loss with binary pre-processing of continuous monitoring data.","authors":"Paula R Langner, Elizabeth Juarez-Colunga, Lucas N Marzec, Gary K Grunwald, John D Rice","doi":"10.1007/s12561-025-09473-w","DOIUrl":"10.1007/s12561-025-09473-w","url":null,"abstract":"<p><p>In studies with a recurrent event outcome, events may be captured as counts during subsequent intervals or follow-up times either by design or for ease of analysis. In many cases, recurrent events may be further coarsened such that only an indicator of one or more events in an interval is observed at the follow-up time, resulting in a loss of information relative to a record of all events. In this paper, we examine efficiency loss when coarsening longitudinally observed counts to binary indicators and aspects of the design which impact the ability to estimate a treatment effect of interest. The investigation was motivated by a study of patients with cardiac implantable electronic devices in which investigators aimed to examine the effect of a treatment on events detected by the devices over time. In order to study components of such a recurrent event process impacted by data coarsening, we derive the asymptotic relative efficiency (ARE) of a treatment effect estimator utilizing a coarsened binary outcome relative to an alternative estimator using the count outcome. We compare the efficiencies and consider conditions where the binary process maintains good efficiency in estimating a treatment effect. We present an application of the methods to a data set consisting of seizure counts in a sample of patients with epilepsy.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.8,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12266715/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144660627","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":"Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.","authors":"Subharup Guha, Yi Li","doi":"10.1007/s12561-024-09470-5","DOIUrl":"https://doi.org/10.1007/s12561-024-09470-5","url":null,"abstract":"<p><p>Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional covariates. Motivated by an integrative analysis of breast cancer patients across seven medical centers, this paper tackles the challenges of integrating multiple observational datasets. The proposed inferential technique, called Bayesian Motif Submatrices for Covariates (B-MSC), addresses the curse of dimensionality by a hybrid of Bayesian and frequentist approaches. B-MSC uses nonparametric Bayesian \"Chinese restaurant\" processes to eliminate redundancy in the high-dimensional covariates and discover latent <i>motifs</i> or lower-dimensional structures. With these motifs as potential predictors, standard regression techniques can be utilized to accurately infer the PSs and facilitate covariate-balanced group comparisons. Simulations and meta-analysis of the motivating cancer investigation demonstrate the efficacy of the B-MSC approach to accurately estimate the propensity scores and efficiently address covariate imbalance when integrating observational health studies with high-dimensional covariates.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12320959/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144973414","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}
Zeyu Yang, Hua Liang, Huiling Liu, Shannon Barth, Morgan Byrne, Elisabeth Andersen, Vinay Bhandaru, Amanda Castel
{"title":"Model Checking for Logistic Models with Study of Telehealth During the COVID-19 Pandemic Among PWH in DC.","authors":"Zeyu Yang, Hua Liang, Huiling Liu, Shannon Barth, Morgan Byrne, Elisabeth Andersen, Vinay Bhandaru, Amanda Castel","doi":"10.1007/s12561-024-09457-2","DOIUrl":"10.1007/s12561-024-09457-2","url":null,"abstract":"<p><p>We propose a projection-based test to check logistic regression models and apply the test to study telehealth utilization during the COVID-19 pandemic among patients with HIV. The test is shown to be consistent and can detect root- <math><mi>n</mi></math> local alternatives. The use of the proposed test to investigate a COVID-19 dataset reveals that the probability of telehealth utilization depends on the following variables: overweight, education, and age and the interaction between age and ethnicity. Specifically, the probability for the Hispanic group decreases with older age, whereas there is no trend between the probability with the age for the group of non-Hispanic. This interaction may be ignored when we apply other goodness-of-fit methods. The simulation studies also show the performance of the proposed method is remarkably attractive compared to its competitors.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12306521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144754775","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":"Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.","authors":"Yuying Lu, Tian Gu, Rui Duan","doi":"10.1007/s12561-024-09449-2","DOIUrl":"10.1007/s12561-024-09449-2","url":null,"abstract":"<p><p>Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of \"gold standard\" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions. In response to these challenges, we introduce FEderated Semi-Supervised Transfer Learning (FEST) for improving disease risk predictions in underrepresented populations. FEST facilitates the collaborative training of models across various institutions by leveraging both labeled and unlabeled data from diverse subpopulations. It addresses distributional variations across different populations and healthcare institutions by combining density ratio reweighting and model calibration techniques. Federated learning algorithms are developed for training models using only summary-level statistics. We perform simulation studies to assess the efficacy of FEST in comparisons with a few alternative methods. Subsequently, we apply FEST to training a genetic risk prediction model for type 2 diabetes that targets the African-Ancestry population using data from the Massachusetts General Brigham (MGB) Biobank. Both our computational experiments and real-world data application underline the superior performance of FEST over competing methods.</p>","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013364","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}