Statistical Methods in Medical Research最新文献_第6页

Inference about the ratio of age-standardized rates between two overlapping populations. 关于两个重叠人群年龄标准化比率的推断。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-06-30 DOI: 10.1177/09622802261459842

Jiangshan Zhang, Jiming Jiang, Mandi Yu

{"title":"Inference about the ratio of age-standardized rates between two overlapping populations.","authors":"Jiangshan Zhang, Jiming Jiang, Mandi Yu","doi":"10.1177/09622802261459842","DOIUrl":"10.1177/09622802261459842","url":null,"abstract":"We develop a robust bias-corrected method of inference about the ratio of age-standardized rates (RASR) for comparing the age-standardized rate (ASR) between a subpopulation and the whole population. Unlike previous methods, the proposed approach does not rely on the proportional age-distribution (PAD) assumption, which is often unrealistic in many situations. Like an existing approach, the method corrects for bias resulting from sampling errors when using sample-based population estimates, instead of census-based populations, as the denominators for estimating ASRs. This broadens the applicability of the proposed method in studying cancer risk factors beyond the basic demographic characteristics. The robust bias-corrected estimator of the RASR and the associated variance estimator and confidence intervals are derived. We show empirically that the proposed RASR estimator performs significantly better than the existing estimator, which relies on the PAD assumption, especially when the latter assumption fails. Specifically, the proposed RASR estimator significantly reduces the bias without increasing the variance. On the other hand, when the PAD assumption holds, our RASR estimator performs similarly to the existing estimator. The proposed method has also shown highly desirable performance when at-risk population estimates used for calculating ASRs are subject to sampling errors. We also show empirically that the proposed variance estimator performs satisfactorily. A real-data application is discussed.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1678-1692"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148353476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Regression analysis of misclassified current status data with potentially unknown test accuracy. 回归分析错误分类的当前状态数据与潜在未知的测试精度。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-01 DOI: 10.1177/09622802261456076

Zhixin Chen, Lianming Wang

{"title":"Regression analysis of misclassified current status data with potentially unknown test accuracy.","authors":"Zhixin Chen, Lianming Wang","doi":"10.1177/09622802261456076","DOIUrl":"10.1177/09622802261456076","url":null,"abstract":"Current status data are frequently encountered in many real life cross-sectional epidemiological, demographic, and medical studies, where each subject is examined only once, and the failure time of interest is never exactly observed but known to be either smaller or larger than the examination time for each subject by evaluating the failure status. Consequently, current status data are a mixture of left-censored and right-censored observations for the failure times of all subjects with or without covariates. In some real life studies, the test or diagnosis that is used to determine the failure status may be error-prone, and this leads to misclassified failure status for some or all subjects. The resulting data are referred to as misclassified current status data in the literature. In this paper, we study regression analysis of misclassified current status data and propose a novel estimation approach under the proportional odds model. Specifically, monotone splines are adopted to approximate the baseline odds function, and an efficient expectation-maximization algorithm is developed based on a data augmentation involving exponential and Poisson latent variables. An extension of the proposed method is also developed to account for the unknown test accuracy. The proposed method is shown to have excellent estimation performance in our simulation studies and is illustrated by an application to uterine fibroid data.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1585-1599"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148362503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 bounded hazard ratio Cox model for the effect of time to treatment on mortality. 治疗时间对死亡率影响的有界风险比Cox模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-17 DOI: 10.1177/09622802261458075

Krithika Suresh, Wenyu Zhang, John D Rice

{"title":"A bounded hazard ratio Cox model for the effect of time to treatment on mortality.","authors":"Krithika Suresh, Wenyu Zhang, John D Rice","doi":"10.1177/09622802261458075","DOIUrl":"10.1177/09622802261458075","url":null,"abstract":"In resource-limited or time-sensitive care settings, there is interest in assessing the impact of time to treatment (TTT) on mortality. Traditional Cox proportional hazards models, which specify the effect of TTT as an unrestricted term in the log hazard ratio, can produce counterintuitive results where the survival probability may not decrease monotonically with longer delays. Moreover, hazard ratios from such models quantify the effect of TTT conditional on surviving until treatment, rather than the effect of delayed treatment at baseline. We propose a class of bounded hazard ratio (BHR) Cox models that constrain the hazard ratio for TTT to attenuate towards the null with increasing treatment delay, such that hazard for death after treatment cannot exceed the hazard without treatment. Estimation can be performed using direct optimization of the partial log-likelihood or with an iterative linearized estimation procedure for large sample sizes. From BHR models, the estimated hazard ratio curve describes how treatment benefit diminishes with delay. Additionally, we propose a survival probability difference that provides an absolute measure for comparing survival under different treatment delays. We evaluate model performance in simulations and apply the method to examine treatment delays for colon cancer with data from the National Cancer Database.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1518-1533"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 new information integration framework for complex models with applications to real-world data. 一个新的信息集成框架，用于具有实际数据应用程序的复杂模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-05-23 DOI: 10.1177/09622802261445563

Jia Liang, Jason Falve, Shuo Chen, Rozalina McCoy, Chixiang Chen

{"title":"A new information integration framework for complex models with applications to real-world data.","authors":"Jia Liang, Jason Falve, Shuo Chen, Rozalina McCoy, Chixiang Chen","doi":"10.1177/09622802261445563","DOIUrl":"10.1177/09622802261445563","url":null,"abstract":"Over the past few decades, various advanced methods have been developed to facilitate information integration. These methods leverage summary statistics (e.g. point estimates) from multiple sites or studies, which can be readily extracted from existing publications or efficiently shared via correspondence, without requiring the sharing of raw individual-level data. Despite these advancements, existing methods may not be directly applicable to the model that allows certain covariate effects to vary with the values of another covariate. This model is widely used in biomedical research, particularly in scenarios where assuming linear effects across all covariates is inappropriate. This article addresses this gap by introducing a new information integration framework. This new framework (a) enables computationally efficient integration of information from a different external model type (e.g. generalized linear models), (b) does not assume homogeneous data distributions across sites or studies, and (c) supports variable selection. Extensive simulations validate the proposed method, demonstrating substantial variance reduction with minimal estimation bias in various cases. Finally, we apply this method to two distinct datasets to identify (a) risk factors influencing blood pressure using data from the UK Biobank and ARIC studies, and (b) risk factors associated with prolonged hospital length of stay among older fracture patients with ADRD using Medicare claims.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1366-1381"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13540554/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006724","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Maximum likelihood estimation in proportional odds regression model based on interval-censored event-time data. 基于间隔截尾事件时间数据的比例概率回归模型的最大似然估计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-18 DOI: 10.1177/09622802261461775

Zhong Guan

引用次数: 0

An effective method for modeling highly correlated interaction models with applications in Alzheimer's disease analysis. 一种建立高相关相互作用模型的有效方法及其在阿尔茨海默病分析中的应用。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-05 DOI: 10.1177/09622802261457286

Shun Yu, Yujie Gai, Yuehan Yang

{"title":"An effective method for modeling highly correlated interaction models with applications in Alzheimer's disease analysis.","authors":"Shun Yu, Yujie Gai, Yuehan Yang","doi":"10.1177/09622802261457286","DOIUrl":"10.1177/09622802261457286","url":null,"abstract":"Interactions and correlations among features are essential in biology, as well as in other fields. This article introduces a novel approach for linear interaction models characterized by complex correlation structures. By integrating local linear approximation and Laplacian smoothing penalty with <math><msub><mi>l</mi><mn>1</mn></msub></math> or <math><msub><mi>l</mi><mn>1</mn></msub></math> and <math><msub><mi>l</mi><mn>2</mn></msub></math> penalties, our methods effectively estimate and predict highly correlated interaction models. Theoretical analysis confirms that both methods converge to an oracle solution within two iterations, demonstrating a rapid convergence rate. In simulation studies, our proposed methods outperform existing techniques in terms of prediction accuracy, estimation precision, and variable selection. When applied to protein microarray data for Alzheimer's disease analysis, they reveal substantial main and interaction effects with notably lower prediction errors. This highlights the potential of our methods as powerful tools for analyzing linear interaction models with intricate correlations, applicable across a wide range of biological research and other fields.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1480-1492"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148164516","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints. 具有持续有效性和实施终点的混合型集群随机试验设计方法的比较。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-23 DOI: 10.1177/09622802261457277

Melody A Owen, Fan Li, Ruyi Liu, Donna Spiegelman

{"title":"A comparison of methods for designing hybrid type 2 cluster-randomized trials with continuous effectiveness and implementation endpoints.","authors":"Melody A Owen, Fan Li, Ruyi Liu, Donna Spiegelman","doi":"10.1177/09622802261457277","DOIUrl":"10.1177/09622802261457277","url":null,"abstract":"Hybrid type 2 studies are gaining popularity for their ability to assess both implementation and health outcomes as co-primary endpoints. Often conducted as cluster-randomized trials (CRTs), five design methods can validly power these studies: p-value adjustment methods, combined outcomes approach, single weighted 1-DF test, disjunctive 2-DF test, and conjunctive test. We compared these methods theoretically and numerically. Theoretical comparisons of power equations allowed us to identify when one method had more or less power than another globally. We showed that p-value adjustment methods are always less powerful than both the combined outcomes approach and the single 1-DF test, and identified conditions where the disjunctive 2-DF test is less powerful than the single 1-DF test. To further investigate when power advantages shift, we conducted a large-scale numerical study using our novel crt2power R package, which calculates power or sample size for CRTs with two continuous co-primary endpoints using these methods. Across 45,000 input scenarios, we found specific patterns: when treatment effects are unequal, the disjunctive 2-DF test tends to be most powerful; when treatment effects are equal, the single 1-DF test tends to dominate. Together, these comparisons offer practical guidance for powering hybrid type 2 studies.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1493-1517"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13342486/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Bayesian adaptive design for clinical trials with potential subgroup effects. 具有潜在亚组效应的临床试验贝叶斯自适应设计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-01 DOI: 10.1177/09622802261449368

Xuekui Zhang, Qianyun Zhao, Cong Chen, Belaid Moa, Shelley Gao

{"title":"Bayesian adaptive design for clinical trials with potential subgroup effects.","authors":"Xuekui Zhang, Qianyun Zhao, Cong Chen, Belaid Moa, Shelley Gao","doi":"10.1177/09622802261449368","DOIUrl":"10.1177/09622802261449368","url":null,"abstract":"Adaptive clinical trial designs increasingly aim to improve efficiency while accommodating subgroup heterogeneity, yet most existing methods fix assumptions about drug efficacy and subgroup effects. We propose a Bayesian adaptive design that explicitly models and learns from uncertainty in both components. A hierarchical mixture prior represents uncertainty about overall treatment efficacy and the magnitude of a biomarker-defined subgroup effect. Interim data are used to update these hyperparameters into posterior distributions, enabling a decision-theoretic framework that adaptively selects the optimal testing strategy among three options: continuing with the overall population, focusing on the subgroup, or conducting a joint test of both. When joint testing is chosen, the posterior information further determines the optimal allocation of Type I error between populations by selecting an evidence-based <math><mi>α</mi></math>-splitting parameter that maximizes expected power under error-rate constraints. The resulting optimization is solved efficiently using GPU-accelerated quasi-Monte Carlo integration and smooth search procedures. Simulation studies across a range of subgroup prevalences and effect sizes demonstrate that the proposed design maintains nominal error control, achieves superior power and decision accuracy, and adapts appropriately to prior misspecification. By unifying posterior learning and adaptive <math><mi>α</mi></math>-allocation within a principled Bayesian framework, this design provides a transparent and computationally practical tool for confirmatory clinical trials with uncertain subgroup effects, supporting precision-medicine decision-making and regulatory reproducibility.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1395-1407"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13342478/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148138965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Handling missing data, skewness, and outliers in medical research: A robust factor analysis approach using the canonical fundamental skew-t distribution. 处理医学研究中的缺失数据、偏度和异常值：使用典型基本偏t分布的稳健因子分析方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-05-19 DOI: 10.1177/09622802261449552

Wan-Lun Wang, Luis M Castro, Tsung-I Lin

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Handling missing data, skewness, and outliers in medical research: A robust factor analysis approach using the canonical fundamental skew-<ns0:math><ns0:mi>t</ns0:mi></ns0:math> distribution.","authors":"Wan-Lun Wang, Luis M Castro, Tsung-I Lin","doi":"10.1177/09622802261449552","DOIUrl":"10.1177/09622802261449552","url":null,"abstract":"Addressing incomplete and non-normally distributed multivariate data poses significant challenges in medical research, particularly when the interest is in discovering underlying data structures. This article introduces a robust factor analysis framework for handling missing data by employing the canonical fundamental skew-<math><mi>t</mi></math> factor analysis (CFUSTFA) model, which incorporates the canonical fundamental skew-<math><mi>t</mi></math> distribution into the latent factors and error terms. This versatile framework accounts for skewness, heavy tails, and missing data, thereby enhancing the model's ability to capture complex structures commonly observed in biomedical datasets. For parameter estimation under the missing at random mechanism, we develop a computationally efficient alternating expectation-conditional maximization algorithm within the maximum likelihood framework. This approach facilitates the simultaneous imputation of missing values and the extraction of low-dimensional factor representations. Standard errors for parameter estimates are also derived using a general information matrix-based approach. The proposed methodology is validated through simulations and applied to a hepatitis C virus laboratory dataset exhibiting skewness, excess kurtosis, and missingness. Our findings highlight the capability of the CFUSTFA model to robustly capture complex, incomplete, and asymmetric biomedical data, offering enhanced inference and interpretability compared with existing factor analysis approaches.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1408-1427"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975608","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Bayesian phase I/II platform design with survival efficacy endpoint for dose optimization. 贝叶斯I/II期平台设计，用于剂量优化的生存疗效终点。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-05-14 DOI: 10.1177/09622802261449367

Xian Shi, Jiangyan Zhao, Jin Xu, Rongji Mu

引用次数: 0