Statistics in Medicine最新文献_第3页

A New Algorithm for Sampling Parameters in a Structured Correlation Matrix With Application to Estimating Optimal Combinations of Muscles to Quantify Progression in Duchenne Muscular Dystrophy. 一种结构化相关矩阵采样参数的新算法及其在估计肌肉最优组合中的应用，以量化杜氏肌营养不良症的进展。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70252

Michael K Kim, Michael J Daniels, William D Rooney, Rebecca J Willcocks, Glenn A Walter, Krista H Vandenborne

{"title":"A New Algorithm for Sampling Parameters in a Structured Correlation Matrix With Application to Estimating Optimal Combinations of Muscles to Quantify Progression in Duchenne Muscular Dystrophy.","authors":"Michael K Kim, Michael J Daniels, William D Rooney, Rebecca J Willcocks, Glenn A Walter, Krista H Vandenborne","doi":"10.1002/sim.70252","DOIUrl":"https://doi.org/10.1002/sim.70252","url":null,"abstract":"The goal of this paper is to estimate an optimal combination of biomarkers for individuals with Duchenne muscular dystrophy (DMD), which provides the most sensitive combinations of biomarkers to assess disease progression (in this case, optimal with respect to standardized response mean (SRM) for 4 muscle biomarkers). The biomarker data is incomplete (missing and irregular) multivariate longitudinal data. We propose a normal model with structured covariance designed for our setting. To sample from the posterior distribution of parameters, we develop a Markov Chain Monte Carlo (MCMC) algorithm to address the positive definiteness constraint on the structured correlation matrix. In particular, we propose a novel approach to compute the support of the parameters in the structured correlation matrix; we modify the approach from [1] on the set of the largest possible submatrices of the correlation matrix, where the correlation parameter is a unique element. For each posterior sample, we compute the optimal weights of our construct. We conduct data analysis and simulation studies to evaluate the algorithm and the frequentist properties of the posteriors of correlations and weights. We found that the lower extremities are the most responsive muscles at the early and late ambulatory disease stages, and the biceps brachii is the most responsive at the nonambulatory disease stage.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70252"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024172","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

Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual- and Cluster-Level Dropout. 在个体和群体水平均退出的聚类随机试验中处理缺失结果数据。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70259

Analissa Avila, Beth A Glenn, Roshan Bastani, Catherine M Crespi

{"title":"Handling Missing Outcome Data in Cluster Randomized Trials With Both Individual- and Cluster-Level Dropout.","authors":"Analissa Avila, Beth A Glenn, Roshan Bastani, Catherine M Crespi","doi":"10.1002/sim.70259","DOIUrl":"https://doi.org/10.1002/sim.70259","url":null,"abstract":"Missing outcome data are common in cluster randomized trials (CRTs), which can complicate inference. Further, the missingness can occur due to dropout of individuals, termed sporadically missing data, or dropout of clusters, termed systematically missing data, and these two types of missingness could have potentially different missing data mechanisms. We aimed to develop a well-performing and practical approach to handle inference in CRTs when outcome data may be both sporadically and systematically missing. To this end, we first examined the performance of several multilevel multiple imputation (MI) methods to handle sporadically and systematically missing CRT outcome data via a simulation study. Specifically, we examined performance under a multilevel covariate-dependent missingness assumption. Our findings indicated that several full conditional specification (FCS) methods designed for missingness in linear mixed models performed well under various scenarios, while an FCS approach using a two-stage estimator often performed poorly. We then developed methods for conducting sensitivity analysis to test the robustness of inferences under different missing at random (MAR) and missing not at random (MNAR) assumptions. The methods allow for different MNAR assumptions for cluster dropout and individual dropout to reflect that they may arise from different missing data mechanisms. We used graphical displays to visualize sensitivity analysis results. Our methods are illustrated using a real data application.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70259"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076008","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

Model-Free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival. 用无模型方法评估被删减的中间结果作为总生存期的替代。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70268

Xuan Wang, Tianxi Cai, Lu Tian, Layla Parast

{"title":"Model-Free Approach to Evaluate a Censored Intermediate Outcome as a Surrogate for Overall Survival.","authors":"Xuan Wang, Tianxi Cai, Lu Tian, Layla Parast","doi":"10.1002/sim.70268","DOIUrl":"https://doi.org/10.1002/sim.70268","url":null,"abstract":"Clinical trials or studies oftentimes require long-term and/or costly follow-up of participants to evaluate a novel treatment/drug/vaccine. There has been increasing interest in the past few decades in using short-term surrogate outcomes as a replacement for the primary outcome, that is, in using the surrogate outcome, which can potentially be observed sooner, to make inferences about the treatment effect on the long-term primary outcome. Very few of the available statistical methods to evaluate a surrogate are applicable to settings where both the surrogate and the primary outcome are time-to-event outcomes subject to censoring. Methods that can handle this setting tend to require parametric assumptions or be limited to assessing only the restricted mean survival time. In this paper, we propose a nonparametric approach to evaluate a censored surrogate outcome, such as time to progression, when the primary outcome is also a censored time-to-event outcome, such as time to death, and the treatment effect of interest is the difference in overall survival. Specifically, we define the proportion of the treatment effect on the primary outcome that is explained (PTE) by the censored surrogate outcome in this context, and estimate this proportion by defining and deriving an optimal transformation of the surrogate information. Our approach provides the added advantage of relaxed assumptions to guarantee that the true PTE is within <math> <semantics><mrow><mo>(</mo> <mn>0</mn> <mo>,</mo> <mn>1</mn> <mo>)</mo></mrow> <annotation>$$ left(0,1right) $$</annotation></semantics> </math> , along with being model-free. The finite sample performance of our estimators is illustrated via extensive simulation studies and a real data application examining progression-free survival as a surrogate for overall survival for patients with metastatic colorectal cancer.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70268"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087323","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

Minimum Area Confidence Set Optimality for Simultaneous Confidence Bands for Percentiles With Applications to Drug Shelf-Life Estimation. 百分位数同时置信带的最小面积置信集最优性在药物保质期估计中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70184

Lingjiao Wang, Yang Han, Wei Liu, Frank Bretz

{"title":"Minimum Area Confidence Set Optimality for Simultaneous Confidence Bands for Percentiles With Applications to Drug Shelf-Life Estimation.","authors":"Lingjiao Wang, Yang Han, Wei Liu, Frank Bretz","doi":"10.1002/sim.70184","DOIUrl":"10.1002/sim.70184","url":null,"abstract":"One important property of any drug product is its stability over time. A key objective in drug stability studies is to estimate the shelf-life of a drug, involving a suitable definition of the true shelf-life and the construction of an appropriate estimate of the true shelf-life. Simultaneous confidence bands (SCBs) for percentiles in linear regression are valuable tools for determining the shelf-life in drug stability studies. In this paper, we propose a novel criterion, the Minimum Area Confidence Set (MACS) criterion, for finding the optimal SCB for percentile regression lines. This criterion focuses on the area of the constrained regions for the newly proposed pivotal quantities, which are generated from the confidence set for the unknown parameters of an SCB. We employ the new pivotal quantities to construct exact SCBs over any finite covariate intervals and use the MACS criterion to compare several SCBs of different forms. The optimal SCB under the MACS criterion can be used to construct the interval estimate of the true shelf-life. Furthermore, a new computationally efficient method is proposed for calculating the critical constants of exact SCBs for percentile regression lines. A real data example on drug stability is provided for illustration.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70184"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12418920/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145024181","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 Stepped Wedge Cluster Randomized Trials With a Baseline Measurement of the Outcome. 设计具有基线测量结果的楔形阶梯群随机试验。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70273

Kendra Davis-Plourde, Keith Goldfeld, Heather Allore, Monica Taljaard, Fan Li

{"title":"Designing Stepped Wedge Cluster Randomized Trials With a Baseline Measurement of the Outcome.","authors":"Kendra Davis-Plourde, Keith Goldfeld, Heather Allore, Monica Taljaard, Fan Li","doi":"10.1002/sim.70273","DOIUrl":"https://doi.org/10.1002/sim.70273","url":null,"abstract":"Stepped wedge cluster randomized trials (SW-CRTs) are a type of uni-directional crossover designs and are increasingly common in prevention and implementation research. Although sample size formulas have been developed to support the planning of SW-CRTs, almost no prior methods incorporated the baseline measurement of the outcome-a common feature in many randomized trials and, increasingly, in cross-sectional SW-CRTs. In this article, we systematically investigate the possibility of addressing a baseline outcome measurement in designing cross-sectional SW-CRTs. We provide three linear mixed modeling approaches to adjust for the baseline outcome and derive the corresponding variance formula of the treatment effect estimator under each. The derived formulas reveal the efficiency implications of including a baseline outcome measurement, and provide a natural vehicle for the efficiency comparisons across adjustment approaches to generate practical recommendations. We validate the power and sample size methods under each baseline adjustment approach using simulations and provide an illustrative sample size calculation with a baseline outcome using the context of a real SW-CRT.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70273"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145131897","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

Logistic Mixed-Effects Model Analysis With Pseudo-Observations for Estimating Risk Ratios in Clustered Binary Data Analysis. 聚类二值数据分析中估计风险比的伪观测Logistic混合效应模型分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70280

Hisashi Noma, Masahiko Gosho

{"title":"Logistic Mixed-Effects Model Analysis With Pseudo-Observations for Estimating Risk Ratios in Clustered Binary Data Analysis.","authors":"Hisashi Noma, Masahiko Gosho","doi":"10.1002/sim.70280","DOIUrl":"10.1002/sim.70280","url":null,"abstract":"Logistic mixed-effects model has been a standard multivariate analysis method for analyzing clustered binary outcome data, for example, longitudinal studies, clustered randomized trials, and multicenter/regional studies. However, the resultant odds ratio estimator cannot be directly interpreted as an effect measure, and it is only interpreted as an approximation of the risk ratio estimator when the frequency of events is small. In this article, we propose a new statistical analysis method that enables providing a risk ratio estimator in the multilevel statistical model framework. The valid risk ratio estimation is realized via augmenting pseudo-observations to the original dataset and then analyzing the modified dataset by the logistic mixed-effects model. The resultant estimators of fixed effect coefficients are theoretically shown to be consistent estimators of the risk ratios. Also, the standard errors and confidence intervals of the risk ratios can be calculated by the bootstrap method. All of the computations are simply implementable by using the R package \"glmmrr.\" We illustrate the effectiveness of the proposed method via applications to a cluster-randomized trial of the maternal and child health handbook and a longitudinal study of respiratory disease. Also, we provide simulation-based evidence for the accuracy and precision of estimation of risk ratios by the proposed method.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70280"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12454234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125907","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

Collapsible Kernel Machine Regression for Exposomic Analyses. 暴露分析的可折叠核机回归。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70258

Glen McGee, Brent A Coull, Ander Wilson

{"title":"Collapsible Kernel Machine Regression for Exposomic Analyses.","authors":"Glen McGee, Brent A Coull, Ander Wilson","doi":"10.1002/sim.70258","DOIUrl":"10.1002/sim.70258","url":null,"abstract":"An important goal of environmental epidemiology is to quantify the complex health effects posed by a wide array of environmental exposures. In studies of a small number of exposures, flexible models like Bayesian kernel machine regression (BKMR) are appealing because they allow for non-linear and non-additive associations among exposures. However, this flexibility comes at the cost of low power and difficult interpretation, particularly in exposomic analyses when the number of exposures is large. We propose a flexible framework that allows for the separate selection of additive and non-additive effects, unifying additive models and kernel machine regression. The proposed approach yields increased power and simpler interpretation when there is little evidence of interaction. Further, it allows users to specify separate priors for additive and non-additive effect s, and allows for statistical inference on non-additive interactions. We extend the approach to a class of multiple index models, in which the special case of kernel machine-distributed lag models is nested. We apply the method to motivating data from a subcohort of the Human Early Life Exposome (HELIX) study containing 65 mixture components grouped into 13 distinct exposure classes.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70258"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12436083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070614","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 Location-Scale Joint Model for Studying the Link Between the Time-Dependent Subject-Specific Variability of Blood Pressure and Competing Events. 研究时间依赖性受试者特异性血压变异性与竞赛项目之间联系的地点尺度联合模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70244

Léonie Courcoul, Christophe Tzourio, Mark Woodward, Antoine Barbieri, Hélène Jacqmin-Gadda

{"title":"A Location-Scale Joint Model for Studying the Link Between the Time-Dependent Subject-Specific Variability of Blood Pressure and Competing Events.","authors":"Léonie Courcoul, Christophe Tzourio, Mark Woodward, Antoine Barbieri, Hélène Jacqmin-Gadda","doi":"10.1002/sim.70244","DOIUrl":"10.1002/sim.70244","url":null,"abstract":"Given the high incidence of cardio and cerebrovascular diseases (CVD), and their association with morbidity and mortality, their prevention is a major public health issue. A high level of blood pressure is a well-known risk factor for these events, and an increasing number of studies suggest that blood pressure variability may also be an independent risk factor. However, these studies suffer from significant methodological weaknesses. In this work, we propose a new location-scale joint model for the repeated measures of a marker and competing events. This joint model combines a mixed model including a subject-specific and time-dependent residual variance modeled through random effects, and cause-specific proportional intensity models for the competing events. The risk of events may depend simultaneously on the current value of the variance, as well as, the current value and the current slope of the marker trajectory. The model is estimated by maximizing the likelihood function using the Marquardt-Levenberg algorithm. The estimation procedure is implemented in an R-package and is validated through a simulation study. This model is applied to study the association between blood pressure variability and the risk of CVD and death from other causes. Using data from a large clinical trial on the secondary prevention of stroke, we find that the current individual variability of blood pressure is associated with the risk of CVD and death. Moreover, the comparison with a model without heterogeneous variance shows the importance of taking into account this variability in the goodness-of-fit and for dynamic predictions.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70244"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12412725/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145001470","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 Bayesian Multi-Factorial Design and Analysis for Estimating Combined Effects of Multiple Interventions in a Pragmatic Clinical Trial to Improve Dementia Care. 贝叶斯多因子设计和分析评估多种干预措施在改善痴呆护理的实用临床试验中的联合效应。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70264

Keith S Goldfeld, Corita R Grudzen, Manish N Shah, Abraham A Brody, Joshua Chodosh, Rebecca Anthopolos

引用次数: 0

Accounting for Misclassification of Binary Outcomes in External Control Arm Studies for Unanchored Indirect Comparisons: Simulations and Applied Example. 用于非锚定间接比较的外部控制臂研究中二元结果的错误分类：模拟和应用实例。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70236

Mikail Nourredine, Antoine Gavoille, Côme Lepage, Behrouz Kassai-Koupai, Michel Cucherat, Fabien Subtil

{"title":"Accounting for Misclassification of Binary Outcomes in External Control Arm Studies for Unanchored Indirect Comparisons: Simulations and Applied Example.","authors":"Mikail Nourredine, Antoine Gavoille, Côme Lepage, Behrouz Kassai-Koupai, Michel Cucherat, Fabien Subtil","doi":"10.1002/sim.70236","DOIUrl":"10.1002/sim.70236","url":null,"abstract":"Single-arm control trials are increasingly proposed as a potential approach for treatment evaluation. However, the limitations of this design restrict its methodological acceptability. Regulatory agencies have raised concerns about this approach, although it is sometimes required in applications based solely on such studies. Consequently, the need for accurate indirect treatment comparisons has become critical, especially when constructing external control arms using routinely collected data as outcome measurements may differ from those recorded in the single-arm trial leading to potential misclassification of outcomes. This study aimed to quantify the bias from ignoring misclassification of a binary outcome within unanchored indirect comparisons, through simulations, and to propose a likelihood-based method to correct this bias (i.e., the outcome-corrected model). Simulations demonstrated that ignoring misclassification results in significant bias and poor coverage probabilities. In contrast, the outcome-corrected model reduced bias, improved 95% confidence interval coverage probability and root mean square error in various scenarios. The methodology was applied to two hepatocellular carcinoma trials illustrating a practical application. The findings underscore the importance of addressing outcome misclassification in indirect comparisons. The proposed correction method may improve reliability in unanchored indirect treatment comparisons.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70236"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12422847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145034218","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