Statistical Methods in Medical Research最新文献_第9页

Smooth transformation models for survival analysis: A tutorial using R. 生存分析的平滑转换模型：使用R的教程。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-27 DOI: 10.1177/09622802251414595

Sandra Siegfried, Bálint Tamási, Torsten Hothorn

{"title":"Smooth transformation models for survival analysis: A tutorial using R.","authors":"Sandra Siegfried, Bálint Tamási, Torsten Hothorn","doi":"10.1177/09622802251414595","DOIUrl":"10.1177/09622802251414595","url":null,"abstract":"Over the last five decades, we have seen strong methodological advances in survival analysis, using parametric methods and, more prominently, methods based on non-/semi-parametric estimation. As the methodological landscape continues to evolve, the task of navigating through the multitude of methods and identifying available software resources is becoming increasingly challenging-especially in more complex scenarios, such as when dealing with interval-censored or clustered survival data, non-proportional hazards, or dependent censoring. This tutorial explores the potential of using the framework of smooth transformation models for survival analysis in the R system for statistical computing. This framework provides a unified maximum-likelihood approach that covers a wide range of survival models, including well-established ones such as the Weibull model and a fully parametric version of the famous Cox proportional hazards model, and various extensions for more complex scenarios. We explore models for non-proportional/crossing hazards, dependent censoring, clustered observations and extensions towards personalized medicine within this framework. Using survival data from a two-arm randomized controlled trial on rectal cancer therapy, we demonstrate how survival analysis tasks can be seamlessly navigated in R within this framework using the implementation provided by the tram package, and few related packages.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"980-997"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13272841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781396","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 sample size determination using robust commensurate priors with interpretable discrepancy weights. 使用具有可解释差异权重的稳健相称先验的贝叶斯样本量确定。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-16 DOI: 10.1177/09622802261432816

Lou E Whitehead, James Ms Wason, Oliver Sailer, Haiyan Zheng

{"title":"Bayesian sample size determination using robust commensurate priors with interpretable discrepancy weights.","authors":"Lou E Whitehead, James Ms Wason, Oliver Sailer, Haiyan Zheng","doi":"10.1177/09622802261432816","DOIUrl":"10.1177/09622802261432816","url":null,"abstract":"Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in clinical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prior knowledge. Many established methods involve the use of a discounting factor, sometimes related to a measure of 'similarity' between historical and the new trials. However, it is often the case that the sample size is highly nonlinear in those discounting factors. This hinders communication with subject-matter experts to elicit sensible values for borrowing strength at the trial design stage. Focussing on a method that can incorporate historical data from multiple sources, we highlight a particular issue of nonmonotonicity and explain why this causes issues with interpretability of discounting factors (hereafter referred to as 'weights'). We propose a solution from which an analytical sample size formula is derived. We then propose a linearization technique such that the sample size changes uniformly over the weights. This leads to interpretable weights (as a percentage of information to borrow/discount) which could facilitate easier elicitation of expert opinion on their values.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1046-1062"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13272840/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147700252","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

Eliminating residual confounding in the stratified estimator via smoothing along with the propensity score. 通过平滑和倾向分数消除分层估计器中的残留混淆。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-03-31 DOI: 10.1177/09622802261432998

Naoto Tsujimoto, Satoshi Hattori

{"title":"Eliminating residual confounding in the stratified estimator via smoothing along with the propensity score.","authors":"Naoto Tsujimoto, Satoshi Hattori","doi":"10.1177/09622802261432998","DOIUrl":"10.1177/09622802261432998","url":null,"abstract":"The stratified estimator by the propensity score is one of the most popular estimator for the average causal effect in the presence of confounding. Despite of its advantages of robustness and simplicity, it has a serious shortcoming of residual confounding even with the correctly specified propensity score. On the other hand, the inverse probability weighting estimator by the propensity score is free from residual confounding and many corresponding variants have been proposed. Wang et al. (2021, Robust estimation of propensity score weights via subclassification) pointed it out that the stratified estimator can be regarded as a special case of the inverse probability weighting estimator with piecewise constant propensity score and proposed a method eliminating residual confounding in the stratified estimator. In this paper, we provide an alternative interpretation of the stratified estimator as the outcome regression with a piecewise constant function over the propensity score. By considering kernel smoothing, an estimator free from residual confounding is proposed based on the stratified estimator, which preserves the robustness of the stratified estimator. We also propose a doubly robust estimator, which does not rely on the inverse probability weighting.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1091-1108"},"PeriodicalIF":1.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582389","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

Linearized maximum rank correlation estimation of doubly truncated data. 双截断数据的线性化最大秩相关估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-02 DOI: 10.1177/09622802261432834

Peijie Wang, Qihao Wang, Jianguo Sun

引用次数: 0

Vaccine efficacy estimands and power considerations. 疫苗效力估计和功率考虑。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-02 DOI: 10.1177/09622802251412833

Andrea Callegaro, Nathan W Bean

引用次数: 0

A Bayesian likely responder approach for the analysis of randomized controlled trials. 随机对照试验分析的贝叶斯可能应答者方法。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-08 DOI: 10.1177/09622802261427026

Annan Deng, Carole Siegel, Hyung G Park

{"title":"A Bayesian likely responder approach for the analysis of randomized controlled trials.","authors":"Annan Deng, Carole Siegel, Hyung G Park","doi":"10.1177/09622802261427026","DOIUrl":"10.1177/09622802261427026","url":null,"abstract":"An important goal of precision medicine is to personalize medical treatment by identifying individuals who are most likely to benefit from a specific treatment. The likely responder (LR) framework, which identifies a subpopulation where treatment response is expected to exceed a certain clinical threshold, plays a role in this effort. However, the LR framework, and more generally, data-driven subgroup analyses, often fail to account for uncertainty in the estimation of model-based data-driven subgrouping. We propose a simple two-stage approach that integrates subgroup identification with subsequent subgroup-specific inference on treatment effects. We incorporate model estimation uncertainty from the first stage into subgroup-specific treatment effect estimation in the second stage, by utilizing Bayesian posterior distributions from the first stage. We evaluate our method through simulations, demonstrating that the proposed Bayesian two-stage model produces better calibrated confidence intervals than naïve approaches. We apply our method to an international COVID-19 treatment trial, which shows substantial variation in treatment effects across data-driven subgroups.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"998-1010"},"PeriodicalIF":1.9,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634151","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

Model-based clustering of multiple images incorporating covariates. 包含协变量的多幅图像的基于模型的聚类。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-08 DOI: 10.1177/09622802251393631

Ying Cui, Jeong Hoon Jang, Robert G Mannino, Amita K Manatunga

{"title":"Model-based clustering of multiple images incorporating covariates.","authors":"Ying Cui, Jeong Hoon Jang, Robert G Mannino, Amita K Manatunga","doi":"10.1177/09622802251393631","DOIUrl":"10.1177/09622802251393631","url":null,"abstract":"In this paper, we develop a novel method for clustering multiple images while adjusting for the effect of available covariates on cluster membership. The key strategy is to represent each image as two-dimensional functional data and formulate a functional latent class mixed model, which fully leverages the structural information of images while effectively addressing their high dimensionality and accounting for covariate effects. We apply the proposed method to color intensity matrices extracted from patient-sourced smartphone fingernail photos to identify distinct subgroups, while adjusting for the effect of image metadata, which may act as an effect modifier on cluster membership. Information on these subgroups can assist public health officials in low-resource settings by enabling rapid and non-invasive identification of high-risk subpopulations for anemia, thereby facilitating the timely delivery of targeted interventions. The results suggest that the three clusters identified by the proposed method correspond to varying levels of anemia risk, with 0%, 79%, and 86% of subjects in each cluster classified as anemic. These findings demonstrate the utility of the proposed method and highlight the potential of a smartphone application leveraging fingernail images for non-invasive and cost-effective anemia screening.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"945-961"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147634231","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 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 : 2026-05-01 Epub 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":"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":"1032-1045"},"PeriodicalIF":1.9,"publicationDate":"2026-05-01","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

Implementing empirical likelihood within the causal inference framework to study causal effects of air pollution on reproductive development. 在因果推理框架内实施经验似然，研究空气污染对生殖发育的因果影响。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-04-08 DOI: 10.1177/09622802261435966

Sima Sharghi, Kevin E Stoll, Sally W Thurston, Emily Barrett, Brent Johnson

引用次数: 0

Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates. 具有时变协变量的区间截尾数据线性变换模型的惩罚估计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-05-01 Epub Date: 2026-03-31 DOI: 10.1177/09622802261433000

Minggen Lu, Yahui Zhang, Chin-Shang Li, Guogen Shan

{"title":"Penalized estimation of linear transformation models for interval-censored data with time-dependent covariates.","authors":"Minggen Lu, Yahui Zhang, Chin-Shang Li, Guogen Shan","doi":"10.1177/09622802261433000","DOIUrl":"10.1177/09622802261433000","url":null,"abstract":"We investigate efficient estimation strategies for partially linear transformation models with time-dependent covariates under interval censoring. The unknown monotone function is approximated using a monotone <math><mi>B</mi></math>-spline basis to enable flexible semiparametric modeling, and we develop a computationally efficient nested hybrid EM algorithm that integrates Newton's method with isotonic regression. To support large-sample inference, we propose a straightforward variance-covariance estimation procedure for the regression parameters and introduce a score test to assess the adequacy of the proportional hazards (PH) specification within the broader class of transformation models. The numerical performance of the penalized estimators is examined extensively and compared with both the time-invariant covariate model by Lu et al. and the semiparametric transformation model by Zeng et al. Finally, the proposed methodology is applied to data from the National Alzheimer's Coordinating Center (NACC) to demonstrate its practical utility in a real-world clinical setting.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1063-1077"},"PeriodicalIF":2.4,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13507786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147594714","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