Statistics in Medicine最新文献

Risk-Profile Based Monitoring Intervals for Multivariate Longitudinal Biomarker Measurements and Competing Events With Applications in Stable Heart Failure. 基于风险概况的多变量纵向生物标志物测量和竞争事件监测间隔在稳定型心力衰竭中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70088

Teun B Petersen, Eric Boersma, Isabella Kardys, Dimitris Rizopoulos

引用次数: 0

Accounting for Inconsistent Use of Covariate Adjustment in Group Sequential Trials. 在组序贯试验中不一致使用协变量调整的解释。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70082

Marlena S Bannick, Sonya L Heltshe, Noah Simon

引用次数: 0

Integrating Multimodal EHR Data for Mortality Prediction in ICU Sepsis Patients. 整合多模式电子病历数据预测ICU脓毒症患者死亡率。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70060

Yi Wang, Weihua Li

{"title":"Integrating Multimodal EHR Data for Mortality Prediction in ICU Sepsis Patients.","authors":"Yi Wang, Weihua Li","doi":"10.1002/sim.70060","DOIUrl":"https://doi.org/10.1002/sim.70060","url":null,"abstract":"Rapid and accurate prediction of mortality risk among intensive care unit (ICU) sepsis patients is crucial for timely intervention and improving patient outcomes. However, due to the multimodal and dynamic time-series nature of patient visit information and the limited data samples, it is challenging to obtain discriminative patient representations, leading to suboptimal mortality prediction results. To address this issue, we design a time-aware graph embedding attention model (TGAM) to integrate multimodal data and predict mortality in ICU sepsis patients. Our approach involves modeling and generating patient representations that encompass not only demographic information but also dynamic time-series data reflecting patient health status. Additionally, the graph convolutional network is used to obtain informative concept embeddings from medical ontologies, and an improved transformer is used to capture the temporal information of the patient's health status and handle missing values, overcoming the limitations of small samples. The experimental results on the MIMIC-III and MIMIC-IV datasets demonstrate that TGAM significantly improves prediction accuracy, with AUROC scores of 87.65% and 87.00% on the MIMIC-III and MIMIC-IV datasets, respectively, outperforming baseline models by over 5 percentage points. TGAM also achieves higher sensitivity, specificity, and AUPRC metrics, and lower Brier Score compared with baseline models, highlighting its effectiveness in identifying high-risk patients. These findings suggest that TGAM has the potential to become a valuable tool for identifying high-risk sepsis patients, enabling clinicians to make more informed and timely intervention decisions.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70060"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080438","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}

引用次数: 0

A Personalized Predictive Model That Jointly Optimizes Discrimination and Calibration. 一种联合优化判别和校准的个性化预测模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70077

Tatiana Krikella, Joel A Dubin

引用次数: 0

Bayesian Estimation of Hierarchical Linear Models From Incomplete Data: Cluster-Level Interaction Effects and Small Sample Sizes. 基于不完全数据的层次线性模型的贝叶斯估计：聚类水平的相互作用效应和小样本量。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70051

Dongho Shin, Yongyun Shin, Nao Hagiwara

{"title":"Bayesian Estimation of Hierarchical Linear Models From Incomplete Data: Cluster-Level Interaction Effects and Small Sample Sizes.","authors":"Dongho Shin, Yongyun Shin, Nao Hagiwara","doi":"10.1002/sim.70051","DOIUrl":"10.1002/sim.70051","url":null,"abstract":"We consider Bayesian estimation of a hierarchical linear model (HLM) from partially observed data, assumed to be missing at random, and small sample sizes. A vector of continuous covariates <math> <semantics><mrow><mi>C</mi></mrow> <annotation>$$ C $$</annotation></semantics> </math> includes cluster-level partially observed covariates with interaction effects. Due to small sample sizes from 37 patient-physician encounters repeatedly measured at four time points, maximum-likelihood estimation is suboptimal. Existing Gibbs samplers impute missing values of <math> <semantics><mrow><mi>C</mi></mrow> <annotation>$$ C $$</annotation></semantics> </math> by a Metropolis algorithm using proposal densities that have constant variances while the target posterior distributions have nonconstant variances. Therefore, these samplers may not ensure compatibility with the HLM and, as a result, may not guarantee unbiased estimation of the HLM. We introduce a compatible Gibbs sampler that imputes parameters and missing values directly from the exact posterior distributions. We apply our Gibbs sampler to the longitudinal patient-physician encounter data and compare our estimators with those from existing methods by simulation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70051"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12083211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080373","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}

引用次数: 0

Improved Pharmacovigilance Signal Detection Using Bayesian Generalized Linear Mixed Models. 基于贝叶斯广义线性混合模型的改进药物警戒信号检测。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70086

Paloma Hauser, Xianming Tan, Fang Chen, Joseph G Ibrahim

{"title":"Improved Pharmacovigilance Signal Detection Using Bayesian Generalized Linear Mixed Models.","authors":"Paloma Hauser, Xianming Tan, Fang Chen, Joseph G Ibrahim","doi":"10.1002/sim.70086","DOIUrl":"https://doi.org/10.1002/sim.70086","url":null,"abstract":"Vaccine safety monitoring is a critical component of public health given the extensive vaccination rate among the general population. However, most signal detection approaches overlook the inherently related biological nature of adverse events (AEs). We hypothesize that integrating AE field knowledge into the statistical process can facilitate and improve the accuracy of identifying vaccine-AE associations. For this purpose, we propose a Bayesian generalized linear multiple low-rank mixed model (GLMLRM) for analyzing high-dimensional post-market drug safety databases. The GLMLRM combines integration of AE ontology in the form of outcome-level groupings, low-rank matrices corresponding to these groupings to approximate the high-dimensional regression coefficient matrix, a factor analysis model to describe the dependence among responses, and a sparse coefficient matrix to capture uncertainty in both the imposed low-rank structures and user-specified groupings. An efficient Metropolis/Gamerman-within-Gibbs sampling procedure is employed to obtain posterior estimates of the regression coefficients and other model parameters, from which testing of outcome-covariate pair associations is based. The proposed approach is evaluated through simulation studies and is further illustrated by an application to the Vaccine Adverse Event Reporting System (VAERS).","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70086"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080469","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}

引用次数: 0

Correction to "A Comparison of Methods to Adjust Survival Curves for Confounders". 更正“调整混杂因素生存曲线的方法比较”。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70087

引用次数: 0

Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics. 贝叶斯纵向网络回归及其在脑连接组遗传学中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-04-01 DOI: 10.1002/sim.70069

Chenxi Li, Xinyuan Tian, Simiao Gao, Selena Wang, Gefei Wang, Yi Zhao, Yize Zhao

{"title":"Bayesian Longitudinal Network Regression With Application to Brain Connectome Genetics.","authors":"Chenxi Li, Xinyuan Tian, Simiao Gao, Selena Wang, Gefei Wang, Yi Zhao, Yize Zhao","doi":"10.1002/sim.70069","DOIUrl":"https://doi.org/10.1002/sim.70069","url":null,"abstract":"The increasing availability of large-scale brain imaging genetics studies enables more comprehensive exploration of the genetic underpinnings of brain functional organizations. However, fundamental analytical challenges arise when considering the complex network topology of brain functional connectivity, influenced by genetic contributions and sample relatedness, particularly in longitudinal studies. In this paper, we propose a novel method named Bayesian Longitudinal Network-Variant Regression (BLNR), which models the association between genetic variants and longitudinal brain functional connectivity. BLNR fills the gap in existing longitudinal genome-wide association studies that primarily focus on univariate or multivariate phenotypes. Our approach jointly models the biological architecture of brain functional connectivity and the associated genetic mixed-effect components within a Bayesian framework. By employing plausible prior settings and posterior inference, BLNR enables the identification of significant genetic signals and their associated brain sub-network components, providing robust inference. We demonstrate the superiority of our model through extensive simulations and apply it to the Adolescent Brain Cognitive Development (ABCD) study. This application highlights BLNR's ability to estimate the genetic effects on changes in brain network configurations during neurodevelopment, demonstrating its potential to extend to other similar problems involving sample relatedness and network-variate outcomes.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70069"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049630","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}

引用次数: 0

Joint Latent Class Models: A Tutorial on Practical Applications in Clinical Research. 联合潜在类模型：临床研究中的实际应用教程。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-04-01 DOI: 10.1002/sim.70047

Maéva Kyheng, Génia Babykina, Alain Duhamel

{"title":"Joint Latent Class Models: A Tutorial on Practical Applications in Clinical Research.","authors":"Maéva Kyheng, Génia Babykina, Alain Duhamel","doi":"10.1002/sim.70047","DOIUrl":"https://doi.org/10.1002/sim.70047","url":null,"abstract":"Joint latent class model is a statistical approach allowing to simultaneously account for two outcomes related to disease progression: A longitudinal measure (for example a biomarker) and time-to-event, in the context of a heterogeneous population. Within this approach, the linear mixed model, describing the longitudinal measure, is connected to the survival model, describing the risk of event occurrence, via a model for latent classes, describing an unobserved population heterogeneity; thus, the behavior of the two outcomes is assumed to be specific to each latent class. The theoretical properties of the model are established and the model is implemented in software. However, its complexity makes it difficult to manipulate by clinicians. In this paper, we propose a detailed tutorial for clinicians and applied statisticians on how to specify the model in R software in order to respond to concrete clinical questions, how to explore, manipulate, interpret the provided results. The tutorial is based on a real clinical dataset; for each clinical question the mathematical model specification and the R script for implementation are provided, and the estimation results and goodness-of-fit measures are detailed and interpreted.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70047"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023844/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144014076","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}

引用次数: 0

Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach. 具有许多相关和零膨胀预测因子的预测建模：评估非负绞喉方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-04-01 DOI: 10.1002/sim.70062

Mariella Gregorich, Michael Kammer, Harald Mischak, Georg Heinze

{"title":"Prediction Modeling With Many Correlated and Zero-Inflated Predictors: Assessing the Nonnegative Garrote Approach.","authors":"Mariella Gregorich, Michael Kammer, Harald Mischak, Georg Heinze","doi":"10.1002/sim.70062","DOIUrl":"https://doi.org/10.1002/sim.70062","url":null,"abstract":"Building prediction models from mass-spectrometry data is challenging due to the abundance of correlated features with varying degrees of zero-inflation, leading to a common interest in reducing the features to a concise predictor set with good predictive performance given the experiments' resource-intensive nature. In this study, we established and examined regularized regression approaches designed to address zero-inflated and correlated predictors. In particular, we describe a novel two-stage regularized regression approach (ridge-garrote) explicitly modeling zero-inflated predictors using two component variables, comprising a ridge estimator in the first stage and subsequently applying a nonnegative garrotte estimator in the second stage. We contrasted ridge-garrote with one-stage methods (ridge, lasso) and other two-stage regularized regression approaches (lasso-ridge, ridge-lasso) for zero-inflated predictors. We assessed the predictive performance and predictor selection properties of these methods in a comparative simulation study and a real-data case study with the aim to predict kidney function using peptidomic features derived from mass-spectrometry. In the simulation study, the predictive performance of all assessed approaches was comparable, yet the ridge-garrote approach consistently selected more parsimonious models compared to its competitors in most scenarios. While lasso-ridge achieved higher predictive accuracy than its competitors, it exhibited high variability in the number of selected predictors. Ridge-lasso exhibited slightly superior predictive accuracy than ridge-garrote but at the expense of selecting more noise predictors. Overall, ridge emerged as a favorable option when variable selection is not a primary concern, while ridge-garrote demonstrated notable practical utility in selecting a parsimonious set of predictors, with only minimal compromise in predictive accuracy.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 8-9","pages":"e70062"},"PeriodicalIF":1.8,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12023833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144023778","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}

引用次数: 0