Statistics in Medicine最新文献_第3页

Group Response‐Adaptive Randomization With Delayed and Missing Responses 具有延迟和缺失响应的分组响应自适应随机化

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-17 DOI: 10.1002/sim.10220

Guannan Zhai, Yang Li, Lixin Zhang, Feifang Hu

引用次数: 0

Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra‐Class Correlation Coefficient in Inter‐Rater Reliability Studies 评分者间可靠性研究中的协议类内相关系数的渐近置信区间、样本量公式和比较测试

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-17 DOI: 10.1002/sim.10217

Abderrahmane Bourredjem, Hervé Cardot, Hervé Devilliers

{"title":"Asymptotic Confidence Interval, Sample Size Formulas and Comparison Test for the Agreement Intra‐Class Correlation Coefficient in Inter‐Rater Reliability Studies","authors":"Abderrahmane Bourredjem, Hervé Cardot, Hervé Devilliers","doi":"10.1002/sim.10217","DOIUrl":"https://doi.org/10.1002/sim.10217","url":null,"abstract":"The agreement intra‐class correlation coefficient (ICCa) is a suitable statistical index for inter‐rater reliability studies. With balanced Gaussian data, we prove the explicit form of ICCa asymptotic normality (ASN), valid both with analysis of variance (ANOVA), maximum likelihood (ML), or restricted ML (REML) estimates. An asymptotic confidence interval is then derived and its performances are examined by simulation compared to the most commonly used methods, under small, moderate and large sample size designs. Then, we deduce sample size calculation formulas, for the number of subjects and observers needed, to achieve a desired confidence interval width or an acceptable ICCa value test power and give concrete examples of their use. Finally, we propose a likelihood ratio test (LRT) to compare two ICCa's from two distinct subpopulations of patients (or raters) and study by simulation its first order risk and power properties. These methods are illustrated using data from two inter‐rater reliability studies, one in physiotherapy with 42 patients and 10 raters and the second in neonatology with 80 subjects and 14 raters. In conclusion, we made recommendations to employ the proposed confidence interval for medium to large samples combined with the quantification of the minimal required sample size at the planning step, or the posterior‐power at the analysis step, using simple dedicated formulas. Furthermore, with sufficient sizes, the proposed LRT seems suitable to compare inter‐rater reliability between two patient subpopulations. Used wisely, this proposed methods toolbox can remedy common current issues in inter‐rater reliability studies.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142265866","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

Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome. 具有纵向计数、复发事件和终末事件的家庭数据的三变量联合建模，并应用于林奇综合征。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-15 DOI: 10.1002/sim.10210

Jingwei Lu, Grace Y Yi, Denis Rustand, Patrick Parfrey, Laurent Briollais, Yun-Hee Choi

{"title":"Trivariate Joint Modeling for Family Data with Longitudinal Counts, Recurrent Events and a Terminal Event with Application to Lynch Syndrome.","authors":"Jingwei Lu, Grace Y Yi, Denis Rustand, Patrick Parfrey, Laurent Briollais, Yun-Hee Choi","doi":"10.1002/sim.10210","DOIUrl":"https://doi.org/10.1002/sim.10210","url":null,"abstract":"Trivariate joint modeling for longitudinal count data, recurrent events, and a terminal event for family data has increased interest in medical studies. For example, families with Lynch syndrome (LS) are at high risk of developing colorectal cancer (CRC), where the number of polyps and the frequency of colonoscopy screening visits are highly associated with the risk of CRC among individuals and families. To assess how screening visits influence polyp detection, which in turn influences time to CRC, we propose a clustered trivariate joint model. The proposed model facilitates longitudinal count data that are zero-inflated and over-dispersed and invokes individual-specific and family-specific random effects to account for dependence among individuals and families. We formulate our proposed model as a latent Gaussian model to use the Bayesian estimation approach with the integrated nested Laplace approximation algorithm and evaluate its performance using simulation studies. Our trivariate joint model is applied to a series of 18 families from Newfoundland, with the occurrence of CRC taken as the terminal event, the colonoscopy screening visits as recurrent events, and the number of polyps detected at each visit as zero-inflated count data with overdispersion. We showed that our trivariate model fits better than alternative bivariate models and that the cluster effects should not be ignored when analyzing family data. Finally, the proposed model enables us to quantify heterogeneity across families and individuals in polyp detection and CRC risk, thus helping to identify individuals and families who would benefit from more intensive screening visits.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142295958","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

Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach 解决因风险因素缺失而导致的风险预测模型实施难题：子模型近似法

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-12 DOI: 10.1002/sim.10184

Tianyi Sun, Allison B. McCoy, Alan B. Storrow, Dandan Liu

{"title":"Addressing the implementation challenge of risk prediction model due to missing risk factors: The submodel approximation approach","authors":"Tianyi Sun, Allison B. McCoy, Alan B. Storrow, Dandan Liu","doi":"10.1002/sim.10184","DOIUrl":"https://doi.org/10.1002/sim.10184","url":null,"abstract":"Clinical prediction models have been widely acknowledged as informative tools providing evidence‐based support for clinical decision making. However, prediction models are often underused in clinical practice due to many reasons including missing information upon real‐time risk calculation in electronic health records (EHR) system. Existing literature to address this challenge focuses on statistical comparison of various approaches while overlooking the feasibility of their implementation in EHR. In this article, we propose a novel and feasible submodel approach to address this challenge for prediction models developed using the model approximation (also termed “preconditioning”) method. The proposed submodel coefficients are equivalent to the corresponding original prediction model coefficients plus a correction factor. Comprehensive simulations were conducted to assess the performance of the proposed method and compared with the existing “one‐step‐sweep” approach as well as the imputation approach. In general, the simulation results show the preconditioning‐based submodel approach is robust to various heterogeneity scenarios and is comparable to the imputation‐based approach, while the “one‐step‐sweep” approach is less robust under certain heterogeneity scenarios. The proposed method was applied to facilitate real‐time implementation of a prediction model to identify emergency department patients with acute heart failure who can be safely discharged home.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198928","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

The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies 用于癌症基因组学研究中稳健变量选择的尖峰-扁平量级 LASSO

IF 2 4区医学

Statistics in Medicine Pub Date : 2024-09-11 DOI: 10.1002/sim.10196

Yuwen Liu, Jie Ren, Shuangge Ma, Cen Wu

{"title":"The spike‐and‐slab quantile LASSO for robust variable selection in cancer genomics studies","authors":"Yuwen Liu, Jie Ren, Shuangge Ma, Cen Wu","doi":"10.1002/sim.10196","DOIUrl":"https://doi.org/10.1002/sim.10196","url":null,"abstract":"Data irregularity in cancer genomics studies has been widely observed in the form of outliers and heavy‐tailed distributions in the complex traits. In the past decade, robust variable selection methods have emerged as powerful alternatives to the nonrobust ones to identify important genes associated with heterogeneous disease traits and build superior predictive models. In this study, to keep the remarkable features of the quantile LASSO and fully Bayesian regularized quantile regression while overcoming their disadvantage in the analysis of high‐dimensional genomics data, we propose the spike‐and‐slab quantile LASSO through a fully Bayesian spike‐and‐slab formulation under the robust likelihood by adopting the asymmetric Laplace distribution (ALD). The proposed robust method has inherited the prominent properties of selective shrinkage and self‐adaptivity to the sparsity pattern from the spike‐and‐slab LASSO (Roc̆ková and George, J Am Stat Associat, 2018, 113(521): 431–444). Furthermore, the spike‐and‐slab quantile LASSO has a computational advantage to locate the posterior modes via soft‐thresholding rule guided Expectation‐Maximization (EM) steps in the coordinate descent framework, a phenomenon rarely observed for robust regularization with nondifferentiable loss functions. We have conducted comprehensive simulation studies with a variety of heavy‐tailed errors in both homogeneous and heterogeneous model settings to demonstrate the superiority of the spike‐and‐slab quantile LASSO over its competing methods. The advantage of the proposed method has been further demonstrated in case studies of the lung adenocarcinomas (LUAD) and skin cutaneous melanoma (SKCM) data from The Cancer Genome Atlas (TCGA).","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142198929","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 deep learning with prior assisted feature selection. 集成深度学习与先验辅助特征选择。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-10 Epub Date: 2024-06-23 DOI: 10.1002/sim.10148

Feifei Wang, Ke Jia, Yang Li

{"title":"Integrative deep learning with prior assisted feature selection.","authors":"Feifei Wang, Ke Jia, Yang Li","doi":"10.1002/sim.10148","DOIUrl":"10.1002/sim.10148","url":null,"abstract":"Integrative analysis has emerged as a prominent tool in biomedical research, offering a solution to the \"small <math> <semantics><mrow><mi>n</mi></mrow> <annotation>$$ n $$</annotation></semantics> </math> and large <math> <semantics><mrow><mi>p</mi></mrow> <annotation>$$ p $$</annotation></semantics> </math> \" challenge. Leveraging the powerful capabilities of deep learning in extracting complex relationship between genes and diseases, our objective in this study is to incorporate deep learning into the framework of integrative analysis. Recognizing the redundancy within candidate features, we introduce a dedicated feature selection layer in the proposed integrative deep learning method. To further improve the performance of feature selection, the rich previous researches are utilized by an ensemble learning method to identify \"prior information\". This leads to the proposed prior assisted integrative deep learning (PANDA) method. We demonstrate the superiority of the PANDA method through a series of simulation studies, showing its clear advantages over competing approaches in both feature selection and outcome prediction. Finally, a skin cutaneous melanoma (SKCM) dataset is extensively analyzed by the PANDA method to show its practical application.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141459451","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 survival analysis with INLA. 利用 INLA 进行贝叶斯生存分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-10 Epub Date: 2024-06-23 DOI: 10.1002/sim.10160

Danilo Alvares, Janet van Niekerk, Elias Teixeira Krainski, Håvard Rue, Denis Rustand

引用次数: 0

Erratum to "Joint modeling for censored predictors due to detection limits with applications to metabolites data". 对 "检测限导致的删减预测因子联合建模在代谢物数据中的应用 "的勘误。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-10 Epub Date: 2024-07-29 DOI: 10.1002/sim.10190

引用次数: 0

Renewable risk assessment of heterogeneous streaming time-to-event cohorts. 异质流时间到事件队列的可再生风险评估。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-10 Epub Date: 2024-06-19 DOI: 10.1002/sim.10146

Jie Ding, Jialiang Li, Xiaoguang Wang

引用次数: 0

Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure. 异质 Cox 模型中的探索性亚组识别：一个相对简单的程序

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2024-09-10 Epub Date: 2024-07-01 DOI: 10.1002/sim.10163

Larry F León, Thomas Jemielita, Zifang Guo, Rachel Marceau West, Keaven M Anderson

{"title":"Exploratory subgroup identification in the heterogeneous Cox model: A relatively simple procedure.","authors":"Larry F León, Thomas Jemielita, Zifang Guo, Rachel Marceau West, Keaven M Anderson","doi":"10.1002/sim.10163","DOIUrl":"10.1002/sim.10163","url":null,"abstract":"For survival analysis applications we propose a novel procedure for identifying subgroups with large treatment effects, with focus on subgroups where treatment is potentially detrimental. The approach, termed forest search, is relatively simple and flexible. All-possible subgroups are screened and selected based on hazard ratio thresholds indicative of harm with assessment according to the standard Cox model. By reversing the role of treatment one can seek to identify substantial benefit. We apply a splitting consistency criteria to identify a subgroup considered \"maximally consistent with harm.\" The type-1 error and power for subgroup identification can be quickly approximated by numerical integration. To aid inference we describe a bootstrap bias-corrected Cox model estimator with variance estimated by a Jacknife approximation. We provide a detailed evaluation of operating characteristics in simulations and compare to virtual twins and generalized random forests where we find the proposal to have favorable performance. In particular, in our simulation setting, we find the proposed approach favorably controls the type-1 error for falsely identifying heterogeneity with higher power and classification accuracy for substantial heterogeneous effects. Two real data applications are provided for publicly available datasets from a clinical trial in oncology, and HIV.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":null,"pages":null},"PeriodicalIF":1.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141477460","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