Statistical Methods in Medical Research最新文献_第3页

Joint estimation of multiple graphical models for an fMRI study of brain connectivity networks. 脑连接网络fMRI研究中多个图形模型的联合估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-30 DOI: 10.1177/09622802261432804

Lizhe Sun, Xiaojuan Han, Aiying Zhang

引用次数: 0

Covariate hypothesis tests for the cure rate in mixture cure models based on martingale difference correlation. 基于鞅差相关的混合固化模型固成率的协变量假设检验。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-27 DOI: 10.1177/09622802261421453

Blanca E Monroy-Castillo, María Amalia Jácome, Ricardo Cao, Ingrid Van Keilegom

{"title":"Covariate hypothesis tests for the cure rate in mixture cure models based on martingale difference correlation.","authors":"Blanca E Monroy-Castillo, María Amalia Jácome, Ricardo Cao, Ingrid Van Keilegom","doi":"10.1177/09622802261421453","DOIUrl":"https://doi.org/10.1177/09622802261421453","url":null,"abstract":"Cure models are a class of survival models used to analyze time-to-event data that allow the possibility that the event never occurs for a certain percentage <math><mn>1</mn><mo>-</mo><mi>p</mi></math>, of the population. These methods allow for direct modelling of the cure rate and the influence of covariates on this rate. A common goal is to test whether the cure rate depends on a specific covariate or a set of covariates. The availability of methods to test the effect of covariates on the cure rate is limited in the literature. This paper proposes nonparametric hypothesis tests for the effect of covariates on the cure probability based on the martingale difference correlation. Two methods are used to approximate the null distribution of the test statistic: A permutation test and a chi-square test. The methodology is further extended to the case of two covariates using the partial martingale difference correlation. The performance of the proposed tests is evaluated through a simulation study under various scenarios, and the method is applied to a dataset on rheumatoid arthritis patients.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261421453"},"PeriodicalIF":1.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147532760","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

Addressing nonignorable missing data and heterogeneity in prognostic biomarker assessment. 解决预后生物标志物评估中不可忽视的缺失数据和异质性。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-27 DOI: 10.1177/09622802261432996

Xinran Huang, Ruosha Li, Jing Ning, For The Alzheimer's Disease Neuroimaging Initiative

{"title":"Addressing nonignorable missing data and heterogeneity in prognostic biomarker assessment.","authors":"Xinran Huang, Ruosha Li, Jing Ning, For The Alzheimer's Disease Neuroimaging Initiative","doi":"10.1177/09622802261432996","DOIUrl":"10.1177/09622802261432996","url":null,"abstract":"Covariate-specific and time-dependent area-under-curve (AUC) is often used to evaluate the discriminative performance of biomarkers with time-to-event outcomes, particularly when certain covariates influence biomarkers' accuracy. In biomarker research, despite extensive efforts, missing data remain unavoidable, with nonignorable missingness posing significant challenges. This article focuses on estimating the impact of covariates on time-dependent AUC in the presence of nonignorable missing biomarkers. Assuming a parametric model on the missing probability, we leverage instrumental variables to address the identifiable issue and estimate the unknown parameters. We integrate the inverse probability weighting approach into the score equation of a pseudo partial likelihood for estimation and inference. Additionally, we establish the asymptotic properties of the proposed estimators. Through simulation studies, we evaluate finite sample performance of the proposed estimators, and apply the method to data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261432996"},"PeriodicalIF":1.9,"publicationDate":"2026-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13122711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147532724","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

Randomization and allocation procedures for master protocol trials of single-arm studies. 单臂研究主方案试验的随机化和分配程序。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-25 DOI: 10.1177/09622802261425426

Peter Jacko, Günter Heimann, Tom Parke

引用次数: 0

A Bayesian transformation model for informative partly interval-censored data with covariates subject to measurement error. 具有测量误差的协变量的信息部分区间截尾数据的贝叶斯变换模型。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-25 DOI: 10.1177/09622802261432830

Jingjing Jiang, Chunjie Wang

{"title":"A Bayesian transformation model for informative partly interval-censored data with covariates subject to measurement error.","authors":"Jingjing Jiang, Chunjie Wang","doi":"10.1177/09622802261432830","DOIUrl":"https://doi.org/10.1177/09622802261432830","url":null,"abstract":"Linear transformation models are one of the commonly used models for regression analysis of failure time data due to their flexibility. Although the existing literature provides many methods for fitting transformation models with fixed covariates and non-informative censoring, extending these methods to scenarios with covariates subject to measurement error and informative censoring remains challenging. As pointed out in the literature, failure to account for covariate measurement errors or informative censoring may lead to estimation bias or misleading conclusions. Therefore, in this article, we consider a more complicated and general situation where both covariate measurement errors and informative censoring, or more especially informative partly interval censoring, exist. For this problem, we propose a new joint model for regression analysis of such data and present a general Bayesian estimation procedure that can handle both non-informative censoring and informative censoring, using I-splines to approximate unknown functions. To implement this method, we propose a flexible and stable Markov chain Monte Carlo (MCMC) algorithm through a four-stage data augmentation. This method is simple and easy to use. We conduct extensive simulation studies to compare the naive method with the Bayesian method, verifying the effectiveness of the Bayesian method. We also present a practical application to illustrate the proposed method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261432830"},"PeriodicalIF":1.9,"publicationDate":"2026-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147514592","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

Asymptotic validity of Schoenfeld's sample size formula for the Cox proportional hazards model via the Wald test approach. 通过Wald检验方法对Cox比例风险模型的Schoenfeld样本量公式的渐近效度。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-23 DOI: 10.1177/09622802261427024

Se Yoon Lee

{"title":"Asymptotic validity of Schoenfeld's sample size formula for the Cox proportional hazards model via the Wald test approach.","authors":"Se Yoon Lee","doi":"10.1177/09622802261427024","DOIUrl":"https://doi.org/10.1177/09622802261427024","url":null,"abstract":"We revisit the widely used sample size formula for the Cox proportional hazards model, originally proposed by Schoenfeld in 1983. The classical derivation, based on the score test, evaluates the Fisher information under the null hypothesis, overlooking key conditions required for its validity. Using a Wald test framework, we demonstrate that the derivation relies on the risk set proportionality property, wherein the ratio of at-risk counts in the treatment and control arms at observed times matches the randomization ratio. This property typically holds under the null hypothesis or when event rates are low, given a sufficiently large sample size. Our analysis clarifies the asymptotic validity of the formula and shows that violations of this assumption can lead to substantial loss of efficiency, particularly under alternative hypotheses. In contrast, simulation-based approaches remain robust. A retrospective analysis of the ADAURA trial illustrates how simulation-based power analysis could have shortened the study duration compared to the formula-based approach, while still maintaining the type I error rate at the nominal level and preserving the coverage properties of the confidence interval. This work highlights the limitations of the Schoenfeld formula in realistic trial settings and recommends simulation-based methods for planning survival trials, especially when a large treatment effect is expected.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261427024"},"PeriodicalIF":1.9,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147500044","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

Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder. 纵向干预研究中具有顺序治疗依赖混杂因素的中介分析。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-18 DOI: 10.1177/09622802261418211

Mikko Valtanen, Tommi Härkänen, Matti Uusitupa, Jaakko Tuomilehto, Jaana Lindström, Kari Auranen

{"title":"Mediation analysis in longitudinal intervention studies with an ordinal treatment-dependent confounder.","authors":"Mikko Valtanen, Tommi Härkänen, Matti Uusitupa, Jaakko Tuomilehto, Jaana Lindström, Kari Auranen","doi":"10.1177/09622802261418211","DOIUrl":"10.1177/09622802261418211","url":null,"abstract":"In interventional health studies, causal mediation analysis can be employed to investigate mechanisms through which the intervention affects the targeted health outcome. Identifying direct and indirect effects from empirical data become complicated, however, when a confounder of the mediator-outcome association is itself affected by the treatment. Here, we investigate identification of mediational effects under such post-treatment confounding in a setting with a longitudinal mediator, time-to-event outcome and an ordinal treatment-dependent confounder. If the treatment affects the treatment-dependent confounder only in one direction (monotonicity), we show that the mediational effects are identified up to stratum-specific sensitivity parameters and derive their empirical non-parametric expressions. The feasibility of the monotonicity assumption can be assessed using empirical data, based on restrictions on the marginal distributions of counterfactuals of the treatment-dependent confounder. In an empirical analysis, we use data from the Finnish Diabetes Prevention Study to assess the extent to which the effect of a lifestyle intervention on avoiding type 2 diabetes is mediated through weight reduction in a high-risk population, with other health-related changes acting as treatment-dependent confounders. We avoid pitfalls related to post-treatment conditioning by treating the mediator as a functional entity and defining the time-to-event outcome as a restricted disease-free time.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261418211"},"PeriodicalIF":1.9,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147475213","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

Flexible Bayesian modeling of non-equidispersed counts with penalized complexity priors in disease incidence studies. 疾病发病率研究中具有惩罚复杂度先验的非等分散计数的灵活贝叶斯建模。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-15 DOI: 10.1177/09622802261416088

Mahsa Nadifar, Hossein Baghishani, Thomas Kneib, Afshin Fallah

{"title":"Flexible Bayesian modeling of non-equidispersed counts with penalized complexity priors in disease incidence studies.","authors":"Mahsa Nadifar, Hossein Baghishani, Thomas Kneib, Afshin Fallah","doi":"10.1177/09622802261416088","DOIUrl":"https://doi.org/10.1177/09622802261416088","url":null,"abstract":"Counts in epidemiology often deviate from equidispersion and exhibit spatial, temporal, and nonlinear structure that the Poisson model cannot accommodate. We introduce a gamma-count structured additive regression model that strategically integrates penalized complexity priors in two critical aspects: (i) a principled penalized complexity prior on the dispersion parameter of the gamma-count distribution, which naturally shrinks toward the base Poisson model when the data support equidispersion, and (ii) scale-dependent penalized complexity hyperpriors on the smoothing variances for nonlinear, spatial, and temporal effects. By formulating the model within a latent Gaussian framework, we enable efficient approximate Bayesian inference through integrated nested Laplace approximations. Simulation studies across under-, equi-, and over-dispersed regimes show that the penalized complexity prior for dispersion parameter combined with scale-dependent hyperpriors yields accurate estimation of dispersion and smooth effects, favorable predictive scores, and robust inference. In empirical applications to larynx cancer mortality in Germany, COVID-19 incidence in Georgia (USA), and lung and bronchus cancer in Iowa (USA), the gamma-count structured additive regression model exhibits competitive or enhanced fit relative to Poisson and negative binomial counterparts, while elucidating interpretable nonlinear and spatial structures. This framework delivers robust, spatially resolved estimates of disease burden in the presence of non-equidispersion, thereby facilitating evidence-based resource allocation, epidemiological surveillance, and monitoring of health disparities, contributing to Sustainable Development Goals (SDGs) 3 (Good Health and Well-Being) and 10 (Reduced Inequalities). For geographically targeted analyses, it further supports informed decision-making in urban and community planning, aligning with SDG 11 (Sustainable Cities and Communities).","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261416088"},"PeriodicalIF":1.9,"publicationDate":"2026-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147463828","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

Efficient design of partially nested randomized trials: A maximin approach. 部分嵌套随机试验的有效设计：最大化方法。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-13 DOI: 10.1177/09622802251409388

Math Jjm Candel, Gerard Jp van Breukelen

{"title":"Efficient design of partially nested randomized trials: A maximin approach.","authors":"Math Jjm Candel, Gerard Jp van Breukelen","doi":"10.1177/09622802251409388","DOIUrl":"https://doi.org/10.1177/09622802251409388","url":null,"abstract":"For two-treatment randomized trials with clustering in one of the treatment arms and a continuous outcome, designs are presented that minimize the number of subjects or the amount of research budget, when aiming for a desired power level. These designs optimize the treatment-to-control allocation ratio of study participants but also optimize the choice between the number of clusters (such as therapy groups) versus the number of persons per cluster (therapy group) in the arm with clustering. Optimal designs require prior knowledge of parameters from the analysis model, which are unknown during the design stage. We present maximin designs which address this by ensuring a pre-specified power level for plausible ranges of the unknown parameters, while maximizing the power for worst-case values of these parameters. Maximin designs are also derived when the number of clusters, or the cluster size is fixed due to practical constraints. An empirical example illustrates how to calculate sample sizes for such practical designs and shows how much these maximin designs can reduce the required research budgets compared to designs with equal subject numbers in treatment and control. A user-friendly R Shiny app facilitates these sample size calculations.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251409388"},"PeriodicalIF":1.9,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147445149","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 interval-censored competing risks data with missing causes of failure: A direct likelihood approach. 缺失失败原因的间隔审查竞争风险数据的回归分析：直接似然方法。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-03-04 DOI: 10.1177/09622802261420820

Yichen Lou, Yuqing Ma, Liming Xiang, Jianguo Sun

{"title":"Regression analysis of interval-censored competing risks data with missing causes of failure: A direct likelihood approach.","authors":"Yichen Lou, Yuqing Ma, Liming Xiang, Jianguo Sun","doi":"10.1177/09622802261420820","DOIUrl":"https://doi.org/10.1177/09622802261420820","url":null,"abstract":"Regression analysis of interval-censored competing risks data is often required and plays an important role in many areas. For the situation, in addition to competing risk and interval censoring, another feature that makes the analysis difficult is that the failure cause may be unknown or missing. Most existing methods for addressing these challenges rely on two-stage estimation procedures, which could suffer efficiency loss and high computational cost. To overcome these, we propose a direct likelihood approach based on a mixture model framework. The proposed method accounts for both competing risks and missingness of event types directly in a likelihood function and facilitates estimation through a sieve maximum likelihood estimation, simplifying the estimation procedure and thus enhancing the estimation efficiency. The consistency and asymptotic normality of the resulting estimators are established, and the idea behind the proposed approach can be extended to other competing risks model frameworks. We demonstrate the promising performance of the proposed method in a comprehensive simulation study and illustrate its practical utility with an application to an Alzheimer's disease study.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261420820"},"PeriodicalIF":1.9,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147356558","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