Statistical Methods in Medical Research最新文献_第7页

Empirical likelihood inference for the area under the receiver operating characteristic (ROC) curve with verification biased data. 具有验证偏倚数据的受试者工作特征（ROC）曲线下面积的经验似然推断。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-17 DOI: 10.1177/09622802261455678

Shirui Wang, Shuangfei Shi, Gengsheng Qin

{"title":"Empirical likelihood inference for the area under the receiver operating characteristic (ROC) curve with verification biased data.","authors":"Shirui Wang, Shuangfei Shi, Gengsheng Qin","doi":"10.1177/09622802261455678","DOIUrl":"10.1177/09622802261455678","url":null,"abstract":"In medical diagnostic studies, the area under the receiver operating characteristic curve (AUC) is a widely used metric that captures a continuous test's overall ability to discriminate between diseased and non-diseased individuals across all possible cutoffs. However, in practice, disease status is sometimes only partially verified, introducing verification bias that undermines the validity of AUC estimation. While numerous methods address bias correction for AUC estimation, approaches that directly construct confidence intervals for the AUC remain limited. This paper proposes two robust methods for constructing bias-corrected confidence intervals for the AUC under the missing-at-random assumption: one based on bootstrap resampling and the other on empirical likelihood. Both approaches accommodate missing disease verification by leveraging the bias-corrected ROC estimators introduced by Alonzo and Pepe. Extensive simulation studies and real-world data analyses demonstrate that our proposed methods yield valid and precise interval estimates for the AUC under various clinically relevant settings.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1428-1445"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148296247","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

Analysis of longitudinal zero-inflated count data using overall marginalized hurdle models. 利用整体边缘障碍模型分析纵向零膨胀计数数据。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-11 DOI: 10.1177/09622802261456081

Jiyun Lee, Eun Jin Jang, Keunbaik Lee

引用次数: 0

Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty. 纵向和复发事件数据的联合分析：带有自回归脆弱性的功能回归方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-23 DOI: 10.1177/09622802261457281

Zifang Kong, Sy Han Chiou, Naim M Maalouf, Yu-Lun Liu

{"title":"Joint analysis of longitudinal and recurrent event data: A functional regression approach with autoregressive frailty.","authors":"Zifang Kong, Sy Han Chiou, Naim M Maalouf, Yu-Lun Liu","doi":"10.1177/09622802261457281","DOIUrl":"10.1177/09622802261457281","url":null,"abstract":"Recurrent health events often involve complex inter-relationships between longitudinal biomarkers and time-to-event outcomes, further complicated by sparse, irregular data collection and time-dependent correlations among events. Traditional statistical methods frequently struggle with these complexities, resulting in biased estimates and suboptimal modeling performance. To address these challenges, we propose the Functional Regression with AutoregressIve fraiLTY (FRAILTY) method, a novel framework designed to jointly model longitudinal measurements and recurrent events, accommodating both scalar and functional covariates while capturing time-dependent correlations among events. The FRAILTY method employs a two-step estimation procedure. First, functional principal component analysis through conditional expectation (PACE) is applied to extract key temporal features from sparse and irregular longitudinal data. Second, the obtained scores are incorporated into a dynamic recurrent frailty model with an autoregressive structure to account for within-subject correlations across recurrent events. Simulation studies demonstrated that the FRAILTY method outperformed existing methods, such as those relying on B-spline basis functions and Bayesian joint modeling, by achieving lower integrated mean squared errors, higher concordance indices, and greater statistical power in detecting functional parameters. Its practical utility was further validated through applications to two datasets: the Systolic Blood Pressure Intervention Trial study and the Multicenter Collaboration to Study Treatment Outcomes in Nephrolithiasis Evaluation cohort.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1463-1479"},"PeriodicalIF":2.4,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13342473/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148309544","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

An evaluation of designs for Phase I/IIa dose-finding studies in Tuberculosis. 结核病I/IIa期剂量研究设计的评价。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-07-01 Epub Date: 2026-06-01 DOI: 10.1177/09622802261430905

Alessandra Serra, Pavel Mozgunov, Patrick Pj Phillips, Thomas Jaki

引用次数: 0

Likelihood-based modeling of covariate-specific time-dependent receiver operating characteristic curves. 协变量特异性时变接收者工作特征曲线的似然建模。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-20 DOI: 10.1177/09622802261445416

Ainesh Sewak, Vanda Inácio, Joanne Wuu, Michael Benatar, Torsten Hothorn

{"title":"Likelihood-based modeling of covariate-specific time-dependent receiver operating characteristic curves.","authors":"Ainesh Sewak, Vanda Inácio, Joanne Wuu, Michael Benatar, Torsten Hothorn","doi":"10.1177/09622802261445416","DOIUrl":"10.1177/09622802261445416","url":null,"abstract":"Identifying reliable biomarkers for predicting clinical events in longitudinal studies is important for accurate disease prognosis and for guiding development of new treatments. However, prognostic studies are often observational, making it difficult to account for patient heterogeneity. In amyotrophic lateral sclerosis (ALS), factors such as age, site of onset and genetic status influence both survival and biomarker levels, yet their impact on the prognostic accuracy of biomarkers over time remains unclear. While time-dependent receiver operating characteristic methods have been developed to handle censored time-to-event outcomes, most do not adjust for covariates. To address this, we propose the nonparanormal prognostic biomarker framework, which models the joint distribution of the biomarker and event time while accounting for covariates. This allows estimation of covariate-specific time-dependent receiver operating characteristic curves and related summary measures. We apply the NPB framework to evaluate serum neurofilament light as a prognostic biomarker in ALS, showing that its accuracy varies over time and with patient characteristics. By capturing these covariate-specific effects, the NPB framework supports more targeted risk stratification and can potentially improve the design of clinical trials for new ALS treatments.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1325-1346"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975685","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 double-semiparametric approach for extending mixture cure models with interval-censored data. 带区间截尾数据的混合固化模型扩展的双半参数方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-29 DOI: 10.1177/09622802261442911

Xiaoyu Liu, Zsolt Szabo, Liming Xiang

{"title":"A double-semiparametric approach for extending mixture cure models with interval-censored data.","authors":"Xiaoyu Liu, Zsolt Szabo, Liming Xiang","doi":"10.1177/09622802261442911","DOIUrl":"10.1177/09622802261442911","url":null,"abstract":"Mixture cure models (MCMs) have become a valuable tool for analyzing failure time data in settings where a subset of individuals is considered to be \"cured\" or no longer experience the failure event of interest. Under the MCM framework, the latency component typically postulates a semiparametric survival model for the failure times of uncured individuals, while the incidence component models the probability of being uncured using logistic regression. However, in many practical applications, assuming linear covariate effects or imposing a fully parametric form on the incidence may be unrealistic, given that cure status is inherently unobserved for right-censored individuals. We introduce a more general mixture cure model for interval-censored data by incorporating nonparametric covariate effects in the incidence component. Our method allows semiparametric frameworks for both components of the mixture cure model, offering flexibility in capturing complex relationships between susceptibility and risk factors while preserving interpretability. We develop a spline-based sieve maximum likelihood estimator for both the model parameters and the unknown functions, and establish its desirable asymptotic properties. The finite-sample performance and practical utility of the proposed method are demonstrated through a simulation study and an analysis of data from a cardiac allograft vasculopathy study, respectively.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1281-1294"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781405","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

Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown. 在部分风险人群的地点未知的情况下，量化空气污染对初级保健治疗的呼吸系统疾病的影响。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-24 DOI: 10.1177/09622802261439259

Qiangqiang Zhu, Duncan Lee, Oliver Stoner

{"title":"Quantifying the effects of air pollution on respiratory ill health treated in primary care when the locations of the populations at risk are partially unknown.","authors":"Qiangqiang Zhu, Duncan Lee, Oliver Stoner","doi":"10.1177/09622802261439259","DOIUrl":"10.1177/09622802261439259","url":null,"abstract":"Most air pollution and health studies focus on severe outcomes such as hospitalisations and deaths, overlooking the impact that air pollution may have on non-hospitalised respiratory ill health treated in primary care. This paper presents a new study investigating the effects of NO2, PM10 and PM<math><msub><mrow></mrow><mrow><mn>2.5</mn></mrow></msub></math> on the prescription rates of respiratory medications in Scotland between 2016 and 2020 at a monthly resolution. To enhance the spatial accuracy of the exposure estimates, air pollution predictions at a 1 km<math><msup><mrow></mrow><mn>2</mn></msup></math> resolution are realigned to General Practioner (GP) surgery patient populations by accounting for where patients are likely to live rather than just where the GP surgery is. A Bayesian spatio-temporal conditional autoregressive model is utilised to account for spatial and temporal dependencies in the data, and this paper proposes two novel spatial neighbourhood matrices to better represent the spatial closeness among the patient populations registered at each GP surgery. These matrices improve model performance in capturing spatial correlation compared to standard distance-based approaches, such as using <math><mi>K</mi></math>-nearest neighbours approach. The results of the study suggest that particulate matter pollution has a significant impact on prescription rates for inhaled corticosteroids that are taken to prevent the symptoms of respiratory ill health, while NO2 demonstrates no such association.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1215-1229"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781447","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

When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials. 当随机化不是随机化时：在小样本、组序随机临床试验中的分配偏倚。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-04-29 DOI: 10.1177/09622802261442914

Daniel Bodden, Ralf-Dieter Hilgers, Franz König

{"title":"When randomization is not random: Allocation bias in small sample, group sequential randomized clinical trials.","authors":"Daniel Bodden, Ralf-Dieter Hilgers, Franz König","doi":"10.1177/09622802261442914","DOIUrl":"10.1177/09622802261442914","url":null,"abstract":"Even in rare diseases, where the sample size is limited and blinding is less frequently implemented, randomized controlled trials are considered the gold standard to prove efficacy. Randomization is used to mitigate bias and regulatory guidance recommend the investigation of the impact of bias on the test decision. We quantified how allocation bias affects the test decision in small-sample two-arm group sequential trials under a biasing policy based on the Blackwell-Hodges convergence strategy. Type I error and power were evaluated under Lan-DeMets spending (Pocock-, O'Brien-Fleming-, Wang-Tsiatis-type functions), with and without futility (non-binding, binding), varying interim timing, number of looks and stage-wise restarting of randomization. Allocation bias inflated type I error most for more restrictive randomization procedures, especially permuted blocks with small block sizes. Spending more alpha at interim reduced inflation. Non-binding futility reduced type I error, while binding increased type I error inflation for more aggressive stopping boundaries. Stage-wise restarting modestly reduced inflation for most procedures. Overall, group sequential choices had secondary effect and did not rescue a predictable randomization scheme. When allocation bias cannot be ruled out (e.g. open-label trials), we recommend less restrictive randomization procedures (e.g. big stick design) or, if using permuted blocks, large block sizes.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1295-1309"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781436","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

Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs. 使用基于神经网络的建模和复杂的调查设计在美国人群中筛查糖尿病。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-07 DOI: 10.1177/09622802261442893

Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela

{"title":"Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs.","authors":"Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela","doi":"10.1177/09622802261442893","DOIUrl":"10.1177/09622802261442893","url":null,"abstract":"Complex survey designs are widely used in medical cohort studies. Developing risk score models that adequately account for the sampling design is essential to minimize selection bias and obtain representative population estimates. This work addresses three complementary objectives. First, we propose a general predictive framework for regression and classification tasks that utilizes neural networks to incorporate survey weights into the model estimation process. Second, we introduce a procedure for quantifying prediction uncertainty based on conformal inference, adapted to the characteristics of complex survey data. Third, we demonstrate the application of the proposed methodology in a case study assessing the risk of diabetes mellitus in the US population, using the NHANES 2011-2014 cohort. The empirical results show that models of varying complexity, each using different sets of predictors, achieve different trade-offs between predictive performance and economic cost while maintaining generalizability at the population level. Although the case study focuses on diabetes, the proposed framework is directly applicable to the development of clinical prediction models for other diseases and complex survey datasets. All software and data used in this study are publicly available on GitHub.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1257-1280"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842888","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 quantile cure model with partially functional covariate effects. 具有部分泛函协变量效应的分位数治愈模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-06-01 Epub Date: 2026-05-23 DOI: 10.1177/09622802261445414

Chyong-Mei Chen, Yingwei Peng

{"title":"A quantile cure model with partially functional covariate effects.","authors":"Chyong-Mei Chen, Yingwei Peng","doi":"10.1177/09622802261445414","DOIUrl":"10.1177/09622802261445414","url":null,"abstract":"Quantile regressions offer several attractive features, including the ability to allow covariate effects to vary at different quantile levels and to effectively handle heteroscedasticity in data, which makes it a viable alternative for analyzing data with continuous outcomes in recent years. It has been used in modeling survival data with and without a cured fraction. In this work, we propose novel estimating equation approaches to estimate a mixture cure model where the latency survival time is modeled using a quantile regression. Our proposed estimation methods provide double robustness, meaning that a misspecification in one part of the mixture cure model will not affect the estimation in the other part. The methods relax the restrictive global log-linear assumption that is typically found in existing quantile regressions, and they allow for both quantile-varying and quantile-invariant effects in the regression when the log-linear assumption holds within a certain range of quantile levels. We established the asymptotic properties of the proposed estimators, and our simulation studies demonstrated their double robustness and efficiency gains. An application of the proposed model and methods to data from a lung cancer study revealed that uncured patients with adenocarcinoma have significantly longer quantiles in the survival time than uncured patients with squamous cell carcinoma, which had not been reported in previous analyses of the data due to the limitations of the existing methods.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1310-1324"},"PeriodicalIF":2.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13283496/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148006753","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