Statistics in Medicine最新文献_第10页

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

Surrogate Marker Evaluation: A Tutorial Using R. 代理标记评估：使用R的教程。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70048

Layla Parast

{"title":"Surrogate Marker Evaluation: A Tutorial Using R.","authors":"Layla Parast","doi":"10.1002/sim.70048","DOIUrl":"10.1002/sim.70048","url":null,"abstract":"The practice of using a surrogate marker to replace a primary outcome in clinical studies has become widespread. Typically, the primary outcome requires long-term patient follow-up, is expensive, or is invasive or burdensome for patients to measure, while the surrogate marker is not (or less so). Of course, a surrogate marker must be validated before it should be used to make a decision about the effectiveness of a treatment. There has been a tremendous amount of statistical and clinical research focused on evaluating and validating surrogate markers over the past 35 years. Although there is ongoing debate over the optimal evaluation method, the development of new approaches and insights has greatly enriched the field. In this tutorial, we describe available statistical frameworks for evaluating a surrogate marker and specifically focus on the practical implementation of the proportion of treatment effect explained framework. We consider both uncensored and censored outcomes, parametric and non-parametric estimation, evaluating multiple surrogates, heterogeneity in the utility of the surrogate marker, surrogate evaluation from a prediction perspective, and the surrogate paradox. We include R code to implement these procedures with a follow-along R markdown. We close with a discussion on open problems in this research area, particularly in terms of using the surrogate marker to test for treatment in a future study, which is the ultimate goal of surrogate marker evaluation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70048"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094784","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

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

Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease. 阿尔茨海默病动态风险评估的综合多组学和多变量纵向数据分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70105

Yuanyuan Guo, Haotian Zou, Mohammad Samsul Alam, Sheng Luo

{"title":"Integrative Multi-Omics and Multivariate Longitudinal Data Analysis for Dynamic Risk Estimation in Alzheimer's Disease.","authors":"Yuanyuan Guo, Haotian Zou, Mohammad Samsul Alam, Sheng Luo","doi":"10.1002/sim.70105","DOIUrl":"10.1002/sim.70105","url":null,"abstract":"Alzheimer's disease (AD) is a complex and progressive neurodegenerative disorder, characterized by diverse cognitive and functional impairments that manifest heterogeneously across individuals, domains, and time. The accurate assessment of AD's severity and progression requires integrating a variety of data modalities, including multivariate longitudinal neuropsychological tests and multi-omics datasets such as metabolomics and lipidomics. These data sources provide valuable insights into risk factors associated with dementia onset. However, effectively utilizing omics data in dynamic risk estimation for AD progression is challenging due to issues including high dimensionality, heterogeneity, and complex intercorrelations. To address these challenges, we develop a novel joint-modeling framework that effectively combines multi-omics factor analysis (MOFA) for dimension reduction and feature extraction with a multivariate functional mixed model (MFMM) for modeling longitudinal outcomes. This integrative joint modeling approach enables dynamic evaluation of dementia risk by leveraging both omics and longitudinal data. We validate the efficacy of our integrative model through extensive simulation studies and its practical application to the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70105"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12092054/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094577","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

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70094

Rose H Garrett, Masum Patel, Brian M Feldman, Eleanor M Pullenayegum

{"title":"Why Recommended Visit Intervals Should Be Extracted When Conducting Longitudinal Analyses Using Electronic Health Record Data: Examining Visit Mechanism and Sensitivity to Assessment Not at Random.","authors":"Rose H Garrett, Masum Patel, Brian M Feldman, Eleanor M Pullenayegum","doi":"10.1002/sim.70094","DOIUrl":"10.1002/sim.70094","url":null,"abstract":"Electronic health records (EHRs) provide an efficient approach to generating rich longitudinal datasets. However, since patients visit as needed, the assessment times are typically irregular and may be related to the patient's health. Failing to account for this informative assessment process could result in biased estimates of the disease course. In this paper, we show how estimation of the disease trajectory can be enhanced by leveraging an underutilized piece of information that is often in the patient's EHR: physician-recommended intervals between visits. Specifically, we demonstrate how recommended intervals can be used in characterizing the assessment process and in investigating the sensitivity of the results to assessment not at random (ANAR). We illustrate our proposed approach in a clinic-based cohort study of juvenile dermatomyositis (JDM). In this study, we found that the recommended intervals explained 78% of the variability in the assessment times. Under a specific case of ANAR where we assumed that a worsening in disease led to patients visiting earlier than recommended, the estimated population average disease activity trajectory was shifted downward relative to the trajectory assuming assessment at random. These results demonstrate the crucial role recommended intervals play in improving the rigor of the analysis by allowing us to assess both the plausibility of the AAR assumption and the sensitivity of the results to departures from this assumption. Thus, we advise that studies using irregular longitudinal data should extract recommended visit intervals and follow our procedure for incorporating them into analyses.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70094"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12086963/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094800","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

COMIC: A Bayesian Dose Optimization Design for Drug Combination in Multiple Indications With Application to CAR-T Therapies. COMIC：用于CAR-T治疗的多适应症药物联合的贝叶斯剂量优化设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70107

Kai Chen, Kentaro Takeda, Ying Yuan

{"title":"COMIC: A Bayesian Dose Optimization Design for Drug Combination in Multiple Indications With Application to CAR-T Therapies.","authors":"Kai Chen, Kentaro Takeda, Ying Yuan","doi":"10.1002/sim.70107","DOIUrl":"10.1002/sim.70107","url":null,"abstract":"Project Optimus, initiated by the US Food and Drug Administration (FDA), seeks to shift the focus of dose finding and selection from the maximum tolerated dose to the optimal dose that offers the most favorable risk-benefit balance. However, applying this paradigm shift to drug combination trials presents challenges, particularly due to limited sample sizes and a large two-dimensional dose exploration space. These challenges are amplified when trials involve multiple indications. To address this, we developed a two-stage Bayesian dose optimization design, called COMIC (Combination Optimization in Multiple IndiCations), to efficiently identify Optimal Biological Dose Combinations (OBDC) for multiple indications. The COMIC design follows a two-stage strategy: First, optimizing the dose for one indication based on a utility function that measures the risk-benefit tradeoff, and then using that data to inform and accelerate dose optimization for additional indications. This approach significantly reduces the required sample size. Additionally, we incorporate a pharmacodynamic endpoint (e.g., receptor occupancy) to prioritize which component of the combination should be escalated, further enhancing the efficiency of dose optimization. Simulation studies demonstrate the strong performance and robustness of the COMIC design across various scenarios. We illustrate the method using a CAR-T therapy trial.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70107"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12207600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094650","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

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

Sample Size and Power Calculations With Win Measures Based on Hierarchical Endpoints. 基于分层端点的Win测量的样本量和功率计算。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-05-01 DOI: 10.1002/sim.70096

Huiman Barnhart, Yuliya Lokhnygina, Roland Matsouaka, Susan Halabi, David Yanez, Robert J Mentz, Frank Rockhold

{"title":"Sample Size and Power Calculations With Win Measures Based on Hierarchical Endpoints.","authors":"Huiman Barnhart, Yuliya Lokhnygina, Roland Matsouaka, Susan Halabi, David Yanez, Robert J Mentz, Frank Rockhold","doi":"10.1002/sim.70096","DOIUrl":"https://doi.org/10.1002/sim.70096","url":null,"abstract":"Win measures, such as win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR), have become popular approaches for the analysis of hierarchical endpoints in clinical studies. Sample size and power calculations with win measures based on hierarchical endpoints are often based on simulation studies that can be cumbersome. Existing sample size and power formulas require investigators to specify clinically significant and meaningful magnitudes of win measures and probability of ties that are difficult to elicit based on prior published literature or preliminary data. In this paper, we provide sample size and power calculation formulas for the four win measures. To facilitate the formula-based sample size or power calculations, we provide formulas to compute overall win measures and overall probability of ties needed by using the specification of marginal win measures and marginal probability of ties that are readily available from clinical investigators or literature. The latter formulas provide a novel way to specify a meaningful and justifiable magnitude of win measures and the magnitude of probability of ties. Therefore, they can be readily used to evaluate the powers based on the number of multiple endpoints, the ordering, and types of endpoints. Our extensive simulation studies show that the power estimations based on these formulas are often like the simulated powers for any type of correlated hierarchical endpoints except for scenarios with very high correlations between endpoints. We illustrate the usefulness of our formulas by using data from three trials with different types of hierarchical endpoints.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 10-12","pages":"e70096"},"PeriodicalIF":1.8,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144094780","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

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