Statistical Methods in Medical Research最新文献_第5页

Estimating transmission parameters from stochastic epidemic models using survival analysis techniques. 利用生存分析技术估计随机流行病模型的传播参数。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-15 DOI: 10.1177/09622802261468021

Hein Putter, Chengyuan Lu, Jacco Wallinga

引用次数: 0

A comparison of missing data approaches for linear regression with missing not at random outcome and predictors. 缺失数据的线性回归方法与缺失非随机结果和预测因子的比较。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-15 DOI: 10.1177/09622802261458074

Tetiana Gorbach, Tim P Morris, James R Carpenter

{"title":"A comparison of missing data approaches for linear regression with missing not at random outcome and predictors.","authors":"Tetiana Gorbach, Tim P Morris, James R Carpenter","doi":"10.1177/09622802261458074","DOIUrl":"10.1177/09622802261458074","url":null,"abstract":"While most methods for missing not at random (MNAR) data in regression models address MNAR outcomes assuming fully observed predictors, real-world observational health and longitudinal studies often violate this assumption. This paper compares several approaches for handling MNAR data in linear regression when missingness depends on both partially observed outcomes and predictors. Through extensive simulations, we evaluate complete-case analysis, multiple imputation assuming missing at random, maximum likelihood estimation via the Heckman selection model, uncertainty intervals, multiple imputation under the Heckman selection model, not-at-random fully conditional specification, imputation stacking, and random indicator imputation. None of the methods consistently produced unbiased estimates or nominal coverage across all scenarios. However, not-at-random fully conditional specification was straightforward to implement and yielded coverage close to the nominal level in most scenarios, provided that the sensitivity parameters were specified near their true values. Our results highlight the importance of sensitivity analyses exploring various full-data models and careful parameter specification when addressing MNAR in both outcomes and predictors. We illustrate such sensitivity analyses using Betula study data on the relationship between longitudinal memory change and grey matter volume in aging. The association remained significant across most considered MNAR scenarios, reinforcing existing evidence for this relationship.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1658-1677"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449619","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

Adaptive spline-based weighting functions for blended survival curve extrapolation. 基于自适应样条的混合生存曲线外推加权函数。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-16 DOI: 10.1177/09622802261458072

Hao Chen, Clara Grazian

{"title":"Adaptive spline-based weighting functions for blended survival curve extrapolation.","authors":"Hao Chen, Clara Grazian","doi":"10.1177/09622802261458072","DOIUrl":"10.1177/09622802261458072","url":null,"abstract":"Health technology assessment frequently requires survival predictions well beyond the observed trial follow-up, yet single parametric models fitted to short horizons can accumulate long-term bias. Current guidance also encourages the principled use of external evidence, such as registry summaries or expert anchors, while maintaining fidelity to the trial data. We introduce an adaptive spline-weighted blended extrapolation on the cumulative-hazard scale that unifies these aims. The observation side is fitted on the trial window using a piecewise-exponential Cox-type model with a smooth prior-driven continuation beyond the administrative cutoff, implemented via INLA. The external side is an anchored Gompertz tail identified by a prespecified survival level at a clinically relevant time. To blend the two components, we compare their cumulative hazards, learn a monotone P-spline score over time, and pass it through a logistic link to obtain a data-driven, time-varying weight. A simple \"temperature\" scaling controls the slope of this weight and provides a practical guarantee of non-negative blended hazards on a chosen grid, preserving adaptivity while ensuring feasibility. Across Monte Carlo scenarios spanning multiple tail shapes and censoring levels, the method delivers consistently lower absolute survival error, smaller restricted mean survival time error, and improved stability compared with fixed-schedule blending and single-family parametric models. In a SEER registry study with three-year observation and ten-year extrapolation, the blended curve tracks the Kaplan-Meier estimates within follow-up and transitions smoothly toward the anchored tail, yielding small long-horizon errors across cancer sites and age strata. The framework is modular, interpretable, and easily extended to alternative tails and multiple anchors. An open-source implementation is available in the R package survblendr at https://github.com/haohaostats/survblendr.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1600-1619"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148473027","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 joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme. 混合观测方案下纵向结果的联合模型和渐进多状态模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-03 DOI: 10.1177/09622802261455480

Leif Erik Lovblom, Laurent Briollais, Bruce A Perkins, George Tomlinson

{"title":"A joint model for a longitudinal outcome and a progressive multistate model under a mixed observation scheme.","authors":"Leif Erik Lovblom, Laurent Briollais, Bruce A Perkins, George Tomlinson","doi":"10.1177/09622802261455480","DOIUrl":"10.1177/09622802261455480","url":null,"abstract":"Joint models for longitudinal and event-time data can be used to understand the co-development of multiple correlated non-fatal outcomes, such as those encountered when studying diabetic microvascular complications. However, such models were not developed for event-times characterized by a multistate process where the entry time into certain states is interval-censored, which is an important feature of interval cohort study designs. Our aim was to develop a joint model for this setting. Specifically, we formulated a shared random effects joint model with linear mixed-effects and proportional intensities progressive three-state Markov submodels, with interval-censoring of entry into the transient state and exact observation of entry into the absorbing state. Maximum likelihood estimation was used, with exploration of the functional forms and association structures. Bias and confidence interval coverage of the model parameters were assessed using simulation, and performance was compared to existing approaches that assume exact observation of all entry times, which were found to sometimes overestimate the magnitude of association between the longitudinal and multistate outcomes. In an application of the model, the trajectory of a routinely measured diabetic complication (retinopathy) was associated with progression through states of a more difficult to measure complication (neuropathy), suggesting potential efficiencies in screening and monitoring.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1551-1568"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478449/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376943","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 multivariate linear mixed-effects models with varied association structures. 具有不同关联结构的贝叶斯多元线性混合效应模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-06-30 DOI: 10.1177/09622802261455689

Aglina Lika, Dimitris Rizopoulos, Michelle E Kruijshaar, Ans T van der Ploeg, Eleni-Rosalina Andrinopoulou

{"title":"Bayesian multivariate linear mixed-effects models with varied association structures.","authors":"Aglina Lika, Dimitris Rizopoulos, Michelle E Kruijshaar, Ans T van der Ploeg, Eleni-Rosalina Andrinopoulou","doi":"10.1177/09622802261455689","DOIUrl":"10.1177/09622802261455689","url":null,"abstract":"In medicine, multiple continuous outcomes are often repeatedly measured on each subject over time to assess disease severity. Usually, it is of interest to investigate the association between those outcomes, which may be measured at different time points, resulting in unbalanced data. The multivariate linear mixed-effects model (MLMM) is a popular framework for this analysis. It considers the unbalanced nature of the data and accounts for the association of the outcomes via the random effects, often assuming a multivariate normal distribution. However, measuring and understanding the degree of connection between longitudinal outcomes remains challenging. We propose to enhance the MLMM by incorporating various interpretable association structures. Specifically, we consider that multiple longitudinal outcomes are related to the primary outcome through their current value, cumulative effect (total or partial), or both. Our research is motivated by Pompe disease, a rare, inheritable, progressive metabolic myopathy. Clinically, it is important to investigate how patient-reported outcome measures (primary outcomes) are associated with physical outcomes to determine whether improvements in physical outcomes are accompanied by improvements in health-related quality of life and other patient experiences. We found a positive association between them. The proposed models are fitted under the Bayesian framework using Hamiltonian Monte Carlo.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1569-1584"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478448/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148353552","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

Asymptotic online FWER control for dependent test statistics. 相关检验统计量的渐近在线FWER控制。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-02 DOI: 10.1177/09622802261461776

Vincent Jankovic, Lasse Fischer, Werner Brannath

引用次数: 0

Deep learning embedded latent class joint modelling of time-to-event and longitudinal data. 深度学习嵌入了时间到事件和纵向数据的潜在类联合建模。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-10 DOI: 10.1177/09622802261465334

Tristan John Elers Harris, Najmeh Nakhaei Rad, Sphiwe Bonakele Skhosana

{"title":"Deep learning embedded latent class joint modelling of time-to-event and longitudinal data.","authors":"Tristan John Elers Harris, Najmeh Nakhaei Rad, Sphiwe Bonakele Skhosana","doi":"10.1177/09622802261465334","DOIUrl":"10.1177/09622802261465334","url":null,"abstract":"Joint modelling of longitudinal and time-to-event data is a powerful tool for analysing complex medical data. Joint latent class models (JLCMs), in particular, provide additional prognostic insight by clustering subjects into latent subgroups within a mixture of experts framework. Traditionally, the mixing proportions are modelled using a multinomial logistic regression. Recent work in mixture regression has proposed a neural network alternative, implemented through the neural network expectation-maximization algorithm, which offers greater flexibility by capturing nonlinearities that may be missed by logistic regression. In this article, we extend the JLCM by applying the neural network to the modelling the mixing proportions captured in the class-membership submodel. The model estimated using an expectation-maximization-type algorithm. We call this the neural-network JLCM (NN-JLCM). We formally define the method, evaluate its performance through extensive simulations, and apply it to real data. The results show that NN-JLCM improves predictive accuracy and class separation compared with traditional JLCMs, while maintaining competitiveness in terms of overall model fit. The code for the article is available at https://github.com/tharris0924/nn-jlcm_p1.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1746-1784"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478454/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148424305","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 robust neural network with random effects for subject-specific prediction of clustered count data. 具有随机效应的鲁棒神经网络，用于特定主题的聚类计数数据预测。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-06-30 DOI: 10.1177/09622802261459873

Hangbin Lee, Il Do Ha, Changha Hwang, Youngjo Lee

{"title":"A robust neural network with random effects for subject-specific prediction of clustered count data.","authors":"Hangbin Lee, Il Do Ha, Changha Hwang, Youngjo Lee","doi":"10.1177/09622802261459873","DOIUrl":"10.1177/09622802261459873","url":null,"abstract":"There is a growing interest in subject-specific predictions using neural networks, as large-scale biomedical data often exhibit dependency due to high-cardinality categorical features, which have been largely overlooked by traditional neural network frameworks. This article proposes a novel hierarchical likelihood learning framework that captures both nonlinear overall effects and subject-specific effects by incorporating gamma random effects into Poisson neural networks. The global maximizer of the proposed objective function yields maximum likelihood estimators for fixed parameters and best unbiased predictors for random effects. The proposed framework provides a robust end-to-end algorithm for clustered biomedical count data, in the sense that the corresponding estimating equations remain unbiased even when the random-effects distribution is misspecified. To enhance learning efficiency, we introduce an adjustment procedure for the random effects and variance component. Extensive simulation studies and real data analyses demonstrate the practical effectiveness of the proposed method for clustered biomedical count data. The proposed method achieves competitive predictive performance in terms of mean squared Pearson error and mean deviance across various random-effects distributions and real-world datasets.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1637-1657"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148353484","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 semi-supervised estimation of optimal individualized treatment regimes with survival outcome. 最优个体化治疗方案与生存结果的有效半监督估计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-03 DOI: 10.1177/09622802261460255

Xintong Li, Minghao Chen, Jialiang Li, Yong Zhou

{"title":"Efficient semi-supervised estimation of optimal individualized treatment regimes with survival outcome.","authors":"Xintong Li, Minghao Chen, Jialiang Li, Yong Zhou","doi":"10.1177/09622802261460255","DOIUrl":"10.1177/09622802261460255","url":null,"abstract":"Individualized treatment regimes (ITRs) represent decision-making frameworks that tailor treatment assignments to individual patient characteristics. The value function of an ITR quantifies the expected outcome under a counterfactual scenario in which such a treatment rule is applied. However, estimating optimal ITRs for survival data remains a significant challenge when outcomes are right-censored and only a subset of patients has complete outcome information due to time and cost constraints. To overcome this challenge, we formulate the problem within a semi-supervised learning framework and adopt an induced missingness perspective to model partially observed survival outcomes. We propose an imputation-based semi-supervised approach that is robust and adaptable to various imputation models. Specifically, we employ a flexible single-index kernel smoothing imputation technique to effectively utilize unlabeled data in multidimensional covariate settings. The proposed estimators for the parameters indexing the optimal ITRs are shown to be consistent and asymptotically normal. Moreover, semi-supervised estimation enhances efficiency by reducing asymptotic variance relative to supervised estimation. Numerical experiments on both simulated and real datasets demonstrate the superior performance of our proposed semi-supervised approach.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1620-1636"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148376977","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

Modeling treatment effects on absorbing outcomes in clinical trials: Leveraging longitudinal and ordinal data for efficiency gains. 模拟治疗对临床试验吸收结果的影响：利用纵向和有序数据提高效率。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-08-01 Epub Date: 2026-07-07 DOI: 10.1177/09622802261462925

Chiara Di Gravio, Ran Tao, Yunbi Nam, Eric P Schmidt, Nathan Shapiro, Jonathan S Schildcrout

{"title":"Modeling treatment effects on absorbing outcomes in clinical trials: Leveraging longitudinal and ordinal data for efficiency gains.","authors":"Chiara Di Gravio, Ran Tao, Yunbi Nam, Eric P Schmidt, Nathan Shapiro, Jonathan S Schildcrout","doi":"10.1177/09622802261462925","DOIUrl":"10.1177/09622802261462925","url":null,"abstract":"Randomized clinical trials in critically ill populations often use mortality at the end of follow-up as the primary outcome. When mortality is rare, composite outcomes that capture disease severity over time are frequently employed to improve efficiency. Motivated by the Crystalloid Liberal or Vasopressor Early Resuscitation in Sepsis (CLOVERS) trial-a randomized study comparing two fluid resuscitation strategies in hospitalized patients with sepsis-we evaluate multiple modeling approaches for estimating covariate-adjusted treatment effects on absorbing outcomes such as mortality. While analyses of binary end-of-follow-up outcomes (alive vs. dead) are natural, efficiency can be gained by incorporating low-cost longitudinal and ordinal severity data routinely collected in trials and by imposing reasonable modeling restrictions (constraints). We develop methods that leverage these data within the marginalized model framework, focusing on first-order marginalized transition models, which allows direct estimation of outcome-marginal (population-averaged) treatment effects. We highlight a key property of the marginalized transition model in the presence of absorbing states: the marginal and conditional sub-models correspond to cumulative incidence and discrete hazard models, respectively. Through simulation studies and an application to the CLOVERS data, we compare the validity and efficiency of competing approaches, including standard survival analyses.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1709-1726"},"PeriodicalIF":2.4,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13478445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148397617","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