Statistical Methods in Medical Research最新文献_第10页

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 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub 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":"1078-1090"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","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

A practical review of response-adaptive randomization: Under-explored challenges and potential directions. 反应适应性随机化的实践回顾：未被探索的挑战和潜在的方向。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-16 DOI: 10.1177/09622802261427330

Hao Mei, Xiaolin Xu, Hang Yang, Fan Wang, Yang Li

{"title":"A practical review of response-adaptive randomization: Under-explored challenges and potential directions.","authors":"Hao Mei, Xiaolin Xu, Hang Yang, Fan Wang, Yang Li","doi":"10.1177/09622802261427330","DOIUrl":"https://doi.org/10.1177/09622802261427330","url":null,"abstract":"Response-adaptive randomization (RAR) dynamically adjusts allocation probabilities of sequentially enrolled patients based on accumulating response information. It has gained increasing attention in clinical trials for its ability to enhance statistical efficiency by accelerating estimation of treatment effects, improve ethical allocation by favoring superior interventions, and maintain robust parameter estimation (e.g. via variance or uncertainty reduction) while accounting for adaptive modifications. While both regulatory agencies and the broader clinical research community are making efforts to promote the application of RAR, its real-world implementation remains limited. In this article, we review the application of RAR in clinical practice from 2015 to 2024, identifying key challenges such as managing diverse patient outcome types, incorporating repeated measurements, and addressing missing data. We categorize various RAR methods and propose practical solutions to these challenges, providing insights and practical references for clinical trial practitioners. Additionally, we discuss the limitations of existing RAR methods and outline potential future research directions. Our review aims to bridge the gap between theory and practice, promoting the broader adoption of RAR in clinical trials while advancing the development of RAR theory and methodology.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261427330"},"PeriodicalIF":1.9,"publicationDate":"2026-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700199","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

Improving finite sample performance of causal discovery by exploiting temporal structure. 利用时间结构改进因果发现的有限样本性能。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub Date: 2026-03-31 DOI: 10.1177/09622802261422162

Christine W Bang, Janine Witte, Ronja Foraita, Vanessa Didelez

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 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub 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":"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":"887-911"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13669452/pdf/","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":"OA","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 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub 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":"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":"713-735"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","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

An ensemble approach to tensor learning. 张量学习的集成方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub Date: 2026-03-03 DOI: 10.1177/09622802261424654

Jiaxin He, Jialiang Li

{"title":"An ensemble approach to tensor learning.","authors":"Jiaxin He, Jialiang Li","doi":"10.1177/09622802261424654","DOIUrl":"10.1177/09622802261424654","url":null,"abstract":"Motivated by recent development of tensor regression modeling, we propose a novel tensor ensemble learning (TEL) approach. While CANDECOMP/PARAFAC (CP) decomposition is an efficient technique to reduce the number of parameters in tensor covariate, determining an appropriate CP rank remains uncertain in practice. Furthermore, the structural complexity of tensor often varies across different spatial regions. To explore the intrinsic tensor structure, we design different tensor partition strategies and divide tensor into disjoint blocks to form candidate models. A model ensemble method is then developed to explore the uncertainty in both tensor block structure and CP rank. To assign weights to candidate models and enhance predictive performance, we leverage the predictability, computability, and stability framework for veridical data science. Simulation studies are carried out to assess the performance of TEL under varying levels of tensor complexity. We further apply TEL to two real data applications about glaucoma management using fundus image and cognitive ability prediction in Alzheimer's disease using neuroimaging. Our numerical studies illustrate the superiority of TEL over existing methods and highlight its promise for complex tensor data analysis.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"847-866"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147345362","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

Estimating conditional survival benefit for the allocation of scarce resources. 稀缺资源配置的条件生存效益估计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub Date: 2026-02-17 DOI: 10.1177/09622802261420699

Ilaria Prosepe, Nan van Geloven, Hans de Ferrante, Andries E Braat, Hein Putter

{"title":"Estimating conditional survival benefit for the allocation of scarce resources.","authors":"Ilaria Prosepe, Nan van Geloven, Hans de Ferrante, Andries E Braat, Hein Putter","doi":"10.1177/09622802261420699","DOIUrl":"10.1177/09622802261420699","url":null,"abstract":"Whenever treatment is scarce, the question of how to allocate resources arises. One option is to allocate based on conditional survival benefit, defined as the contrast between an individual's expected survival with and without treatment. Estimating conditional survival benefit from observational data may present the following three challenges: (i) time-dependent confounding, arising when treatments are assigned based on longitudinal health markers which are in turn affected by past assignments; (ii) multiple time scales, namely time since becoming eligible for treatment, relevant for comparability of patients, and calendar time, representing when treatment decisions are made; and (iii) multiple versions of treatment, leading to multiple counterfactual outcomes with treatment. Building on previous work where cross-sections and inverse probability of treatment weighting were combined to estimate survival benefit on the treated population, we propose a strategy to dynamically estimate conditional survival benefit for all patients awaiting scarce treatment at any time treatment becomes available, accounting for multiple versions of treatment. After delineating identifiability assumptions, we show in a simulation study that our proposed method improves the estimation of conditional survival benefit compared to simpler methods. The proposed method is then applied to liver transplant data from the Eurotransplant region.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"812-826"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161485/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214242","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

Parametric and nonparametric propensity score weighting analysis with subgroup covariate balance. 亚组协变量平衡的参数和非参数倾向得分加权分析。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub Date: 2026-01-29 DOI: 10.1177/09622802251415157

Yan Li, Yong-Fang Kuo, Liang Li

{"title":"Parametric and nonparametric propensity score weighting analysis with subgroup covariate balance.","authors":"Yan Li, Yong-Fang Kuo, Liang Li","doi":"10.1177/09622802251415157","DOIUrl":"10.1177/09622802251415157","url":null,"abstract":"Estimating the causal treatment effects by subgroups is important in observational studies when the treatment effect heterogeneity is present. Existing propensity score methods rely on a correctly specified propensity score model. Model misspecification results in biased treatment effect estimation and covariate imbalance. We proposed a method for the propensity score analysis with controlled subgroup balance (G-SBPS) to achieve covariate mean balance in all subgroups. We further incorporated nonparametric kernel regression for the propensity scores and developed a kernelized G-SBPS (kG-SBPS) to improve the subgroup mean balance of covariate transformations in a rich functional class. This extension increased robustness to propensity score model misspecification. Extensive numerical studies showed that G-SBPS and kG-SBPS improve both subgroup covariate balance and subgroup treatment effect estimation, compared to existing approaches. For illustration, we applied G-SBPS and kG-SBPS to a dataset on right heart catheterization to estimate the subgroup average treatment effects on the hospital length of stay and a dataset on diabetes self-management training to estimate the subgroup average treatment effects for the treated on the hospitalization rate.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"736-751"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161500/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146087150","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

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 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub 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":"773-794"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161496/pdf/","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":"OA","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 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-04-01 Epub 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":"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":"695-712"},"PeriodicalIF":2.4,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13161498/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0