Statistics in Medicine最新文献_第9页

Bayesian Model Selection for Derived Responses. 衍生响应的贝叶斯模型选择。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70682

Bijit Roy, Emmanuel Lesaffre

{"title":"Bayesian Model Selection for Derived Responses.","authors":"Bijit Roy, Emmanuel Lesaffre","doi":"10.1002/sim.70682","DOIUrl":"10.1002/sim.70682","url":null,"abstract":"In this article, we adapt a Bayesian model selection criterion for use with derived response variables, motivated by modeling BMI as a function of its components, weight and height. We argue that the best model for the original responses (weight and height) may not be the optimal model for a derived, non-linear quantity like BMI. Existing model selection criteria such as <math><mrow><mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> , <math><mrow><mi>D</mi> <mi>I</mi> <mi>C</mi></mrow> </math> , and <math><mrow><mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> are limited because they measure the out-of-sample predictive accuracy of the original responses. To address this limitation, we propose to adapt the <math><mrow><mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> . The Derived <math><mrow><mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> ( <math><mrow><mi>D</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> ) is specifically designed to measure the out-of-sample predictive accuracy of the derived quantity of interest. This also allows for direct comparison of models based on their performance in predicting the target variable (BMI), regardless of whether they model the original components (height and weight) or the derived variable directly. We illustrate the utility of <math><mrow><mi>D</mi> <mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> by comparing a bivariate growth model for height and weight with a spline smoothing model for BMI, a comparison that cannot be done with traditional criteria like <math><mrow><mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> or <math><mrow><mi>W</mi> <mi>A</mi> <mi>I</mi> <mi>C</mi></mrow> </math> .","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70682"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13415798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148607122","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

Fixed or Random Effects: Analysis of Clustered Data. 固定或随机效应：聚类数据的分析。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70687

Kevin He, Xiangeng Fang, Yubo Shao, Nicholas Hartman, John D Kalbfleisch

{"title":"Fixed or Random Effects: Analysis of Clustered Data.","authors":"Kevin He, Xiangeng Fang, Yubo Shao, Nicholas Hartman, John D Kalbfleisch","doi":"10.1002/sim.70687","DOIUrl":"10.1002/sim.70687","url":null,"abstract":"In analyzing clustered data, random effects (RE) and fixed effects (FE) models are two primary approaches. The RE model assumes that the cluster-specific effects are random and uncorrelated with individual characteristics. If this assumption is violated, the RE approach can lead to biased estimates of the coefficients of individual covariates and cluster effects. In contrast, the FE approach, which models cluster effects as fixed effects, yields unbiased estimates even if the cluster effects are correlated with individual covariates. Numerous studies have compared the FE and RE approaches. However, the practical importance of the biases observed in RE-based methods remains unclear. Some studies have demonstrated significant biases in RE models when the assumption of no correlation is violated, whereas others have found these biases to be negligible. These differences may be due to variations in study design, with some evaluations focusing on settings that may not reflect realistic data scenarios. In this paper, we compare FE and RE models through theoretical evaluation, simulation studies, and real-data applications. Specifically, we derive an approximate formula to quantify the asymptotic bias in the RE model and verify its accuracy through simulations. These results help to explain the different conclusions reached in the existing literature. Our goal is not to propose a new estimator or a fundamentally new inferential framework, but to provide an analytical clarification and practical quantification of the bias incurred by the standard RE estimator under a specific, interpretable dependence model, and to clarify how this bias relates to fixed-effects and correlated random-effects/between-within specifications.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70687"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445157/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679731","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

Generalized Multilevel Multisource Functional Regression With an Application to Alzheimer's Disease. 广义多水平多源函数回归在阿尔茨海默病中的应用。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70688

Xiuli Du, Guorong Yi, Yenan Ren, Ziting Zhang, Hui Mi

{"title":"Generalized Multilevel Multisource Functional Regression With an Application to Alzheimer's Disease.","authors":"Xiuli Du, Guorong Yi, Yenan Ren, Ziting Zhang, Hui Mi","doi":"10.1002/sim.70688","DOIUrl":"10.1002/sim.70688","url":null,"abstract":"With population aging, Alzheimer's disease has gained increasing attention. Advances in medical technology and longitudinal studies such as the Alzheimer's Disease Neuroimaging Initiative (ADNI) have generated diverse follow-up data, which can be viewed as multilevel multisource functional data for understanding disease progression. In this paper, we propose a new estimation method for the multilevel multisource functional principal components. This method first applies the multilevel functional principal component analysis on the univariate multilevel functional data, then obtains the estimation of the principal components of the multilevel multisource functional principal component model based on the relationship between the univariate and the multisource principal component models, thus realizing the multisource-component-based two-stage estimation of the generalized multilevel multisource functional regression model. We also further consider Bayesian estimation based on the two-stage estimation. Numerical simulations and empirical analyses show that both the proposed multisource-component-based two-stage estimation and Bayesian estimation methods work well. Compared to the multisource-component-based two-stage estimation methods, the classification performance of Bayesian estimation is significantly improved.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70688"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13435350/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148670564","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

Investigating the Utility of Fractal Measures as a Diagnostic for Glaucoma. 分形测度在青光眼诊断中的应用研究。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70655

Jonathan W Henderson, Benjamin M Davis, Hannah J Mitchell

{"title":"Investigating the Utility of Fractal Measures as a Diagnostic for Glaucoma.","authors":"Jonathan W Henderson, Benjamin M Davis, Hannah J Mitchell","doi":"10.1002/sim.70655","DOIUrl":"https://doi.org/10.1002/sim.70655","url":null,"abstract":"Recent advances in Adaptive-Optics Confocal Scanning Laser Ophthalmoscopy (AO-cSLO) have enabled non-invasive, in-vivo imaging of retinal cell subsets, at near single-cell resolution. However, challenges such as spatial and temporal variability, limited observation windows, and segmentation errors hinder the effective use of this data for diagnosing neurodegenerative disorders like glaucoma. Common point pattern metrics used in this area, such as density and distance-based measures, are sensitive to noise and incomplete data, reducing their diagnostic utility. Fractal measures, with their scale-invariance and robustness to point pattern degradation, offer a promising alternative. In this study, we apply fractal analysis to RGC point pattern data sets from rodent glaucoma models and controls, comparing fractal dimension, lacunarity, and succolarity with traditional metrics. We find that lacunarity and succolarity provide more accurate and enable earlier detection of glaucoma than traditional metrics, demonstrating greater robustness to noisy and degraded data. These measures demonstrate significant potential for achieving an earlier and more reliable glaucoma diagnosis.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70655"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13482433/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148797175","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 Randomized Basket Trial Design for Dose Optimization Based on Bayesian Model Averaging Using Spike-and-Slab Priors. 基于spike - slab先验贝叶斯模型平均的剂量优化随机篮试验设计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70700

Belay Birlie Yimer, Kentaro Takeda

{"title":"A Randomized Basket Trial Design for Dose Optimization Based on Bayesian Model Averaging Using Spike-and-Slab Priors.","authors":"Belay Birlie Yimer, Kentaro Takeda","doi":"10.1002/sim.70700","DOIUrl":"https://doi.org/10.1002/sim.70700","url":null,"abstract":"The FDA initiated Project Optimus and issued guidance for dose optimization, recommending randomized parallel dose-response cohorts to generate additional data at promising dose levels and implying that different dosages may be needed for different indications. In addition to dose optimization, with recent advancements in precision medicine and cancer biology, the development of cancer treatments has shifted toward the search for agents targeted to specific molecular profiles that may appear in more than one type of cancer. The basket trials are clinical trial designs that enable the simultaneous assessment of a new treatment in multiple indications. Concerning the FDA's dose optimization perspective and the recent trend of basket trials in early-phase clinical trials, this paper proposes a dose-ranging basket trial design based on a Bayesian model-averaging approach considering efficacy and toxicity outcomes, where indications and dose levels define baskets. A key benefit of the proposed approach is that it explicitly accounts for the possible heterogeneity of response rates among baskets. Our simulation study shows that the proposed approach outperforms other methods, offering higher statistical power, better control of Type I error rates, precise optimal dose selection, and sample size savings in various scenarios with heterogeneous treatment effects between baskets.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70700"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13476160/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148762768","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

Designing Efficient Multicenter Trials With a Maximin Strategy. 用最大化策略设计高效的多中心试验。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70677

Math J J M Candel, Gerard J P van Breukelen

{"title":"Designing Efficient Multicenter Trials With a Maximin Strategy.","authors":"Math J J M Candel, Gerard J P van Breukelen","doi":"10.1002/sim.70677","DOIUrl":"10.1002/sim.70677","url":null,"abstract":"For two-treatment multicenter trials with a continuous outcome, designs are presented that minimize the number of subjects required or the research budget, when aiming for a desired power level. These designs optimize both the allocation ratio of participants between treatment and control condition within each center, and the balance between the number of centers and the number of participants per center. Because optimal designs rely on prior knowledge of variance parameters from the analysis model, which are unknown during the design stage, maximin designs are introduced. These designs guarantee a pre-specified power level across plausible ranges of the unknown parameters and maximize power under worst-case parameter values. The present study extends the existing literature on multicenter designs by deriving maximin designs not only for the case where the number of centers and the sample size per center can be optimized, but also when either the number of centers or the sample size per center is fixed due to practical constraints. An empirical example illustrates sample size calculation for such designs and potential budget savings. Additionally, an interactive R Shiny app has been developed to simplify sample size calculations for maximin multicenter trials.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70677"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387372/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536832","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

Improving Sensitivity Analysis By Synthesizing Randomized Clinical Trials With Limited Overlap. 通过综合有限重叠的随机临床试验改进敏感性分析。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70637

Kuan Jiang, Wenjie Hu, Xinxing Lai, Shu Yang, Xiao-Hua Zhou

{"title":"Improving Sensitivity Analysis By Synthesizing Randomized Clinical Trials With Limited Overlap.","authors":"Kuan Jiang, Wenjie Hu, Xinxing Lai, Shu Yang, Xiao-Hua Zhou","doi":"10.1002/sim.70637","DOIUrl":"10.1002/sim.70637","url":null,"abstract":"While randomized clinical trials (RCTs) are widely regarded as the gold standard for estimating average treatment effects, their external validity is often constrained by limited sample sizes and restrictive inclusion/exclusion criteria, which may compromise the generalizability of findings to broader real-world populations. Conversely, observational studies typically consist of representative real-world samples but are susceptible to bias due to unmeasured confounders, undermining their internal validity. To address the limitation, sensitivity analysis is often used to estimate bounds for the average treatment effect (ATE) without relying on stringent assumptions of other existing methods. This article introduces a novel synthesis sensitivity analysis estimator that enhances sensitivity analysis in observational studies by incorporating RCT data, even when limited covariate overlap exists between datasets due to differential inclusion/exclusion criteria. We show that the proposed estimator will give a tighter bound when a \"separability\" condition holds for the sensitivity parameter. Theoretical proofs and simulations show that this method provides a tighter bound than the sensitivity analysis using only observational study data. We apply this method to combine observational study data on drug effectiveness comparison with a partially overlapping RCT data, yielding tighter average treatment effect bounds.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70637"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13401964/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148593276","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

Statistical Inference for Covariate-Adaptive Randomization Procedures With Missing Covariates. 缺失协变量的协变量自适应随机化过程的统计推断。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70697

Yuhang Tao, Li-Xin Zhang

引用次数: 0

A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening. 用于癌症筛查的生物标志物的短期和长期影响检测程序的比较。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70686

Siddharth Roy, Paul S Albert, Anindya Roy, Danping Liu

{"title":"A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening.","authors":"Siddharth Roy, Paul S Albert, Anindya Roy, Danping Liu","doi":"10.1002/sim.70686","DOIUrl":"10.1002/sim.70686","url":null,"abstract":"Longitudinal cancer biomarker studies aim to identify markers useful for long-term associations with cancer risk and early detection of cancer diagnosis. Early detection biomarkers show acute associations, meaning longitudinal trajectory changes sharply just before diagnosis, while long-term risk prediction biomarkers are often represented as changes in slopes or levels. We compare three common approaches to identify longitudinal biomarkers associated with survival outcomes: joint models, conditional models, and Cox models with time-varying covariates. Each of the three methods uses a different modeling framework for the joint density of the biomarkers and survival time. Thus, they have distinct advantages and disadvantages for detecting acute and long-term associations. We investigate the power and Type I error rates for the three approaches under different data-generating settings with regular yearly visits and moderate measurement error to evaluate robustness to assumptions and the power gain from using test statistics that match the data's association structure. The Cox model controlled Type I errors rates and maintained high power to detect associations across the scenarios considered. The conditional and joint models had inflated Type I errors under longitudinal model misspecification and only outperformed the Cox model for power when the correct model is used. However, only the conditional model can effectively disentangle the acute and long-term effects. The Cox model's convex likelihood also provides the fastest convergence. We apply all three approaches to a study of the association between CA-125 and ovarian cancer in the National Cancer Institute Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The results follow similar patterns to those of our simulations.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70686"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13423587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631501","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

Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models. 多源异构分位数线性回归模型的低秩变分校正估计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70678

Huiqiong Li, Lu Luo, Min Wang, Niansheng Tang

{"title":"Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.","authors":"Huiqiong Li, Lu Luo, Min Wang, Niansheng Tang","doi":"10.1002/sim.70678","DOIUrl":"10.1002/sim.70678","url":null,"abstract":"High-dimensional data arising in genomics, econometrics, and clinical medicine often exhibit substantial heterogeneity across multiple sources. While existing methods address multi-source heterogeneity, they do not adequately accommodate the combined challenges of high dimensionality and between-source heterogeneity. To address this gap, we propose a scalable Bayesian framework for multi-source heterogeneous quantile regression with spike-and-slab priors for simultaneous parameter estimation and feature selection. To overcome computational challenges, we combine mean-field variational inference with Laplace approximation and introduce a novel low-rank variational correction strategy that substantially improves approximation accuracy and adaptability in high-dimensional heterogeneous settings. This low-rank correction effectively captures the underlying dependence structure, leading to more robust and efficient inference. For model assessment and diagnostic analysis, we further develop a Bayesian score test coupled with local influence analysis. Extensive simulation studies and an analysis of The Cancer Genome Atlas (TCGA) data from four cancer cohorts (ESCA, PAAD, PCPG, and READ) demonstrate the computational efficiency, scalability, and practical utility of the proposed method in high-dimensional heterogeneous applications. The proposed low-rank variational correction algorithms are implemented in the R package LRQVB, which is publicly available on CRAN.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70678"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580079","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