Statistics in Medicine最新文献_第10页

A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening. 用于癌症筛查的生物标志物的短期和长期影响检测程序的比较。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70686

Siddharth Roy, Paul S Albert, Anindya Roy, Danping Liu

{"title":"A Comparison of Testing Procedures for Detecting Short- and Long-Term Effects of Biomarkers for Cancer Screening.","authors":"Siddharth Roy, Paul S Albert, Anindya Roy, Danping Liu","doi":"10.1002/sim.70686","DOIUrl":"10.1002/sim.70686","url":null,"abstract":"Longitudinal cancer biomarker studies aim to identify markers useful for long-term associations with cancer risk and early detection of cancer diagnosis. Early detection biomarkers show acute associations, meaning longitudinal trajectory changes sharply just before diagnosis, while long-term risk prediction biomarkers are often represented as changes in slopes or levels. We compare three common approaches to identify longitudinal biomarkers associated with survival outcomes: joint models, conditional models, and Cox models with time-varying covariates. Each of the three methods uses a different modeling framework for the joint density of the biomarkers and survival time. Thus, they have distinct advantages and disadvantages for detecting acute and long-term associations. We investigate the power and Type I error rates for the three approaches under different data-generating settings with regular yearly visits and moderate measurement error to evaluate robustness to assumptions and the power gain from using test statistics that match the data's association structure. The Cox model controlled Type I errors rates and maintained high power to detect associations across the scenarios considered. The conditional and joint models had inflated Type I errors under longitudinal model misspecification and only outperformed the Cox model for power when the correct model is used. However, only the conditional model can effectively disentangle the acute and long-term effects. The Cox model's convex likelihood also provides the fastest convergence. We apply all three approaches to a study of the association between CA-125 and ovarian cancer in the National Cancer Institute Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. The results follow similar patterns to those of our simulations.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70686"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13423587/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148631501","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

Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models. 多源异构分位数线性回归模型的低秩变分校正估计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70678

Huiqiong Li, Lu Luo, Min Wang, Niansheng Tang

{"title":"Low-Rank Variational Correction Estimation for Multi-Source Heterogeneous Quantile Linear Regression Models.","authors":"Huiqiong Li, Lu Luo, Min Wang, Niansheng Tang","doi":"10.1002/sim.70678","DOIUrl":"10.1002/sim.70678","url":null,"abstract":"High-dimensional data arising in genomics, econometrics, and clinical medicine often exhibit substantial heterogeneity across multiple sources. While existing methods address multi-source heterogeneity, they do not adequately accommodate the combined challenges of high dimensionality and between-source heterogeneity. To address this gap, we propose a scalable Bayesian framework for multi-source heterogeneous quantile regression with spike-and-slab priors for simultaneous parameter estimation and feature selection. To overcome computational challenges, we combine mean-field variational inference with Laplace approximation and introduce a novel low-rank variational correction strategy that substantially improves approximation accuracy and adaptability in high-dimensional heterogeneous settings. This low-rank correction effectively captures the underlying dependence structure, leading to more robust and efficient inference. For model assessment and diagnostic analysis, we further develop a Bayesian score test coupled with local influence analysis. Extensive simulation studies and an analysis of The Cancer Genome Atlas (TCGA) data from four cancer cohorts (ESCA, PAAD, PCPG, and READ) demonstrate the computational efficiency, scalability, and practical utility of the proposed method in high-dimensional heterogeneous applications. The proposed low-rank variational correction algorithms are implemented in the R package LRQVB, which is publicly available on CRAN.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70678"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13395643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148580079","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 Local Gaussian-Copula Allocation Model for Bivariate Discrete-Time First-Event Data With Time-Dependent Covariates. 具有时变协变量的二元离散第一事件数据的Bayesian局部高斯- copula分配模型。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70699

Hirofumi Michimae

{"title":"A Bayesian Local Gaussian-Copula Allocation Model for Bivariate Discrete-Time First-Event Data With Time-Dependent Covariates.","authors":"Hirofumi Michimae","doi":"10.1002/sim.70699","DOIUrl":"10.1002/sim.70699","url":null,"abstract":"Bivariate first-event outcomes arise in longitudinal biomedical studies when two clinically related events are assessed on a shared visit schedule. We propose a Bayesian local Gaussian-copula allocation model for bivariate discrete-time first-event data with baseline and time-dependent covariates. Two outcome-specific working discrete-time decrement models define interval-specific working decrement probabilities and thereby generate marginal survival coordinates on the common interval grid. Among subjects jointly at risk at the start of an interval, a Gaussian copula is used locally to allocate probability mass across four terminal outcomes: no event, event 1 only, event 2 only, and same-interval co-occurrence. The Gaussian copula accommodates negative local dependence, independence, and positive local dependence. We consider constant, unstructured interval-specific, and hierarchical shrinkage specifications for the dependence parameter, and compare them with frequentist and Bayesian odds-ratio benchmarks on the observed-probability scale. Because the native copula-model coefficients act on the working decrement scale, observed-scale covariate effects are summarized by Kullback-Leibler projection. In simulations, the Gaussian-copula models were competitive with odds-ratio benchmarks for observed-scale covariate-effect estimation; the hierarchical model provided a regularized alternative to unstructured interval-specific dependence. We illustrate the method using National Health and Aging Trends Study data on first onset of mobility help and self-care help. The application gave broadly similar observed-scale covariate-effect conclusions across the odds-ratio, constant Gaussian-copula, and hierarchical Gaussian-copula models.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70699"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13451534/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148689870","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 Optimal Adaptive Clinical Trial Design for Sequentially Integrated Therapies. 顺序综合治疗的贝叶斯最优适应性临床试验设计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70684

Yining Li, Jiaying Guo, Samer Gawrieh, Wei Shen, Yong Zang, Wanzhu Tu

{"title":"A Bayesian Optimal Adaptive Clinical Trial Design for Sequentially Integrated Therapies.","authors":"Yining Li, Jiaying Guo, Samer Gawrieh, Wei Shen, Yong Zang, Wanzhu Tu","doi":"10.1002/sim.70684","DOIUrl":"10.1002/sim.70684","url":null,"abstract":"Clinical syndromes or diseases with complex etiology often require multiple treatments, each addressing one specific aspect of the disease. Such treatments are typically administered sequentially in an integrated fashion. For example, in alcohol-associated hepatitis (AH), an acute liver disease caused by excessive alcohol drinking, successful treatment requires therapies that reduce liver inflammation during the acute phase, followed by interventions that address the underlying alcohol use disorder (AUD) to achieve optimal outcomes. Selecting an optimal treatment combination, however, presents great challenges in study design due to the involvement of multiple drug combinations and treatment phases. In this paper, we describe a new trial design based on a Bayesian model that connects the outcomes from the acute and subsequent treatment phases. We refer to this design as the Bayesian Optimal Adaptive Design for Sequentially Integrated Therapies (BIT) design. BIT incorporates multiple interim analyses with adaptive stopping rules for both futility and superiority, enhancing efficiency while controlling the family-wise type I error rate and maximizing statistical power. We illustrate the use of BIT design in a clinical trial comparing the efficacy of integrated therapy for severe AH, with sample size determination and design parameter optimization. Simulation studies show that the design possesses excellent operating characteristics, including experiment-wise type I error rate control. Although the design's development is motivated by studies of AH treatment, the design framework is broadly applicable to other complex diseases requiring sequentially delivered therapies. R codes are provided for BIT implementation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70684"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13422262/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148621019","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

Estimating Optimal Site-Specific Individualized Treatment Rules With Limited Data Sharing. 在有限数据共享条件下估计最优的特定部位个性化治疗规则。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70696

Nan Qiao, Jingxiao Zhang, Zishu Zhan

{"title":"Estimating Optimal Site-Specific Individualized Treatment Rules With Limited Data Sharing.","authors":"Nan Qiao, Jingxiao Zhang, Zishu Zhan","doi":"10.1002/sim.70696","DOIUrl":"10.1002/sim.70696","url":null,"abstract":"Estimation of optimal individualized treatment rules (ITRs) tailored to patient characteristics is a crucial component of precision medicine. When such problems arise in multicenter randomized trials with known treatment assignment probabilities, the optimal ITR may vary across sites because of site-level heterogeneity, and directly pooling data across centers can lead to biased ITR estimation. Meanwhile, privacy constraints often prevent investigators from accessing individual-level data from all centers. We therefore propose Heterogeneous Distributed Learning (HD-learning) to estimate optimal site-specific ITRs using only summary-level data from each site in a single communication round. The method uses a distributed mixed-effects model to accommodate common fixed effects and site-specific random effects. We further develop Shrinkage HD-learning (SHD-learning) for moderate- or high-dimensional covariates. Under suitable conditions, we establish asymptotic properties of the proposed estimators. Simulations show that our methods are lossless relative to the corresponding pooled estimator and outperform methods that ignore heterogeneity. We illustrate the proposed methods using the acute upper respiratory tract infections (AURTIs) study and the opioid withdrawal study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70696"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13470841/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148724678","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

Non-Parametric Mediation Analysis of Non-Markov Illness-Death Model. 非马尔可夫疾病-死亡模型的非参数中介分析。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70685

Li-Sheng Zhuang, Jih-Chang Yu, Yen-Tsung Huang

{"title":"Non-Parametric Mediation Analysis of Non-Markov Illness-Death Model.","authors":"Li-Sheng Zhuang, Jih-Chang Yu, Yen-Tsung Huang","doi":"10.1002/sim.70685","DOIUrl":"10.1002/sim.70685","url":null,"abstract":"The illness-death model is widely used to characterize disease progression over time. Previous work focuses either on estimation without causal interpretation or on causal interpretation under a strong Markov assumption where the terminal event depends on the status but not the timing of the intermediate event. To bridge the research gap, we propose a new definition of counterfactual hazard that relaxes the Markov assumption by considering the entire history of the intermediate event. We derive an identification formula that involves an integral with respect to the probability density function of the intermediate event time. Direct and indirect effects refer to the influence of an exposure on the terminal event not mediated by, and mediated through, the intermediate event, respectively. We propose non-parametric kernel estimators for the two effects and study their asymptotic properties. We conduct numerical simulations to examine the proposed estimators' finite-sample performance. Applying the method to a hepatitis study where the Markov assumption is violated, we show that the effect of hepatitis C on mortality is not mediated through septicemia during the first 15 years of follow-up.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70685"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13428012/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148649038","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

Joint Modeling of Longitudinal Data and Potentially Informative Visiting Process to Dynamically Predict Future Visit Times. 纵向数据和潜在信息访问过程联合建模动态预测未来访问时间。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70702

Achilleas Stamoulopoulos, Christos Thomadakis, Giota Touloumi

{"title":"Joint Modeling of Longitudinal Data and Potentially Informative Visiting Process to Dynamically Predict Future Visit Times.","authors":"Achilleas Stamoulopoulos, Christos Thomadakis, Giota Touloumi","doi":"10.1002/sim.70702","DOIUrl":"10.1002/sim.70702","url":null,"abstract":"Frequent visits in cohort studies are important as they enable, among others, the assessment of disease progression and treatment effectiveness. In HIV studies, extended intervals between visits have been shown to be associated with adverse outcomes (e.g., antiretroviral therapy interruption). Prediction of the next visit time can be a useful tool in identifying those more prone to (temporarily) disengage from care. Since visit times are to a large extent individually driven, patient characteristics such as longitudinal markers history and past visit patterns have to be taken into account when dealing with the prediction of future visit times. The topic of dynamic prediction has been developed greatly, especially within the framework of joint models of longitudinal and survival data. While joint models have been extended to account for an informative visiting process, little attention has been given in the dynamic prediction of the next visit time. In this study we propose a model for the joint analysis of a longitudinal continuous marker and the visiting process. A gap time mechanism is employed for the visiting process, while a linear mixed model is used for the marker. The two submodels are linked via correlated normally distributed random effects. The resulting form of the marginal likelihood requires only uni-dimensional integration, irrespective of the dimension of the random effects in the longitudinal model, a property that holds with multiple continuous markers. The model is used to derive dynamic predictions for the time to the next visit, conditioning on patient's previous gap times and marker history. The performance of the proposed methodology is assessed through simulation. Finally, we apply the proposed model to data of people living with HIV from the Athens Multicenter AIDS Cohort Study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70702"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13454517/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148702152","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

Modeling Heterogeneity in Health Risk Behaviors With a Dynamic Mixture Model Informed by Textual Occupational Data. 基于文本职业数据的健康风险行为异质性动态混合模型建模

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70691

Lorenzo Schiavon, Mattia Stival, Angela Andreella, Stefano Campostrini

{"title":"Modeling Heterogeneity in Health Risk Behaviors With a Dynamic Mixture Model Informed by Textual Occupational Data.","authors":"Lorenzo Schiavon, Mattia Stival, Angela Andreella, Stefano Campostrini","doi":"10.1002/sim.70691","DOIUrl":"10.1002/sim.70691","url":null,"abstract":"Health risk behaviors, including smoking, poor nutrition, alcohol misuse, and physical inactivity (SNAP), are leading contributors to chronic disease burden and healthcare costs worldwide. Their prevalence is shaped not only by individual demographic characteristics but also by contextual factors such as socioeconomic and occupational environments. We develop a Bayesian topic-informed dynamic mixture model to analyze SNAP behaviors using data from the Italian health and behavioral surveillance system PASSI. Free-text occupational descriptions are integrated through structural topic modeling to define latent occupational groups that inform the mixture weights of a multivariate ordered probit model. Covariate effects are allowed to vary across occupational clusters and evolve over time. To enhance interpretability and facilitate variable selection, we incorporate non-local spike-and-slab priors on regression coefficients. We further develop a tailored online learning strategy based on sequential Monte Carlo, enabling efficient updating of inference as new surveillance data become available. The proposed approach provides a flexible and interpretable framework for monitoring how occupational contexts modulate the impact of socio-demographic factors on multiple health risk behaviors over time, supporting targeted public health interventions. More broadly, the methodology is applicable to other longitudinal health surveillance and clinical settings where complex contextual information is available in textual form.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70691"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13457518/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148707639","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

Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies. 孟德尔随机化研究中使用阴性对照结果检测选择偏倚。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70703

Apostolos Gkatzionis, George Davey Smith, Kate Tilling

{"title":"Using Negative Control Outcomes to Detect Selection Bias in Mendelian Randomization Studies.","authors":"Apostolos Gkatzionis, George Davey Smith, Kate Tilling","doi":"10.1002/sim.70703","DOIUrl":"10.1002/sim.70703","url":null,"abstract":"Mendelian randomization is currently mainly implemented through the use of genetic variants as instrumental variables to inestigate the causal effect of an exposure on an outcome of interest. Mendelian randomization studies are robust to confounding bias and reverse causation, but they remain susceptible to selection bias; for example, this can happen if the exposure or outcome are associated with selection into the study sample. Negative controls are sometimes used to detect biases (typically due to confounding) in observational studies. Here, we focus specifically on Mendelian randomization analyses and discuss under what conditions a variable can be used as a negative control outcome to detect selection mechanisms that could bias Mendelian randomization estimates. We show that the main requirement is that the negative control outcome relates to confounders of the exposure and outcome. Counter-intuitively, the effect of the negative control on selection is of secondary concern; for example, a variable that does not affect selection can be a valid negative control for an outcome that does. We also investigate under what conditions age and sex can be used as negative control outcomes in Mendelian randomization analyses. In a real-data application, we investigate the pairwise causal relationships between 19 traits, utilizing data from the UK Biobank. Treating biological sex and natural hair color as negative control outcomes, we identify selection bias in analyses involving commonly used traits such as alcohol consumption, body mass index and educational attainment.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70703"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13470660/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148722195","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

Inverse-Intensity-Weighted Generalized Estimating Equations With Irregularly Measured Longitudinal Data and Informative Dropout. 具有不规则测量纵向数据和信息缺失的逆强度加权广义估计方程。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-08-01 DOI: 10.1002/sim.70674

George Stefan, Eleanor Pullenayegum

{"title":"Inverse-Intensity-Weighted Generalized Estimating Equations With Irregularly Measured Longitudinal Data and Informative Dropout.","authors":"George Stefan, Eleanor Pullenayegum","doi":"10.1002/sim.70674","DOIUrl":"10.1002/sim.70674","url":null,"abstract":"Longitudinal data are commonly encountered in biomedical research, including randomized trials and retrospective cohort studies. Subjects are typically followed over a period of time and may be scheduled for follow-up at predetermined time points. However, subjects may miss their appointments or return at non-specified times, leading to irregularity in the visit process. IIW-GEEs have been developed as one method to account for this irregularity, whereby estimates from a visit intensity model are used as weights in a GEE model with an independent correlation structure. We show that currently available methods can be biased for situations in which the health outcome of interest may influence a subject's dropout from the study. We have extended the IIW-GEE framework to adjust for informative dropout and have demonstrated via simulation studies that this bias can be significantly reduced. We have illustrated this method using the STAR*D clinical trial data, and observed that the disease trajectory was generally overestimated when informative dropout was not accounted for.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 18-19","pages":"e70674"},"PeriodicalIF":2.1,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13387368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148536897","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