Statistics in Medicine最新文献_第3页

Optimal Subsampling for the Proportional Mean Residual Life Model With Massive Survival Data. 具有大量生存数据的比例平均剩余寿命模型的最优子抽样。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70766

Yiwei Wang, Xiaoran Yang, Kani Chen, Fangfang Bai

引用次数: 0

Generalized Linear Mixed Models for Changepoint Analysis of Biomedical Time Series Data. 生物医学时间序列数据变点分析的广义线性混合模型。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70769

Mark Fiecas, Rebecca Killick, Kathryn Cullen

{"title":"Generalized Linear Mixed Models for Changepoint Analysis of Biomedical Time Series Data.","authors":"Mark Fiecas, Rebecca Killick, Kathryn Cullen","doi":"10.1002/sim.70769","DOIUrl":"10.1002/sim.70769","url":null,"abstract":"Changepoint analysis for time series data identifies the time points for which properties about the data shift. In this paper, we are motivated by two distinct types of biomedical time series data: digital health monitoring and neuroimaging. The neuroimaging study consists of multivariate time series obtained from resting-state functional magnetic resonance imaging (fMRI). The digital health monitoring data consists of binary univariate time series observed within an individual throughout the day across multiple days of activity. The goal of the analysis is to model how physical activity changes within a day over a period of 56 days. The goal in the analysis of that data is to determine the association between dynamic functional connectivity (dFC), which is the interaction between different brain regions and how this interaction changes over time, and severity of non-suicidal self-injury (NSSI). In this paper, we leverage the framework of generalized linear mixed models to incorporate structure that is present in biomedical time series data for changepoint analysis. This structure could be, for example, the repeated measures of the time series data within an individual across multiple days or the interconnected community structure of brain networks. We embed the mixed model in a dynamic programming algorithm for detecting multiple changepoints. We evaluate the performance of our proposed method across several scenarios using simulations. Finally, we show the utility of our proposed method on our two distinct motivating applications.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70769"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661187/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184180","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

Robust Alpha Spending for Unstable Data. 对不稳定数据的稳健Alpha支出。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70760

Ivair R Silva, Judith C Maro

{"title":"Robust Alpha Spending for Unstable Data.","authors":"Ivair R Silva, Judith C Maro","doi":"10.1002/sim.70760","DOIUrl":"10.1002/sim.70760","url":null,"abstract":"Sequential hypothesis testing has been used in randomized clinical trials and in monitoring adverse events after vaccination. Typically, the presumption is that all data that have been previously used in the sequential analysis remain the same, and only new data are added. We call this the \"anchoring\" assumption. In prelicensing randomized trials, enrollment goals are set based on these sequential hypothesis tests so that a trial may stop if a drug proves either too hazardous or, conversely, so beneficial that it would be unethical to deny the treatment to others. However, in monitoring adverse events after vaccination, data are mostly \"secondary use,\" or real-world data. That is, these data are not gathered for research, but are clinical data that have been repurposed for observational vaccine safety surveillance. As such, the counts of exposed persons and adverse events may be subject to revisions over time. These data changes directly challenge the anchoring assumption upon which most sequential hypothesis testing depends. If these data changes are discovered prior to subsequent hypothesis tests, it is unclear how to account for these changes in subsequent tests. We introduce a solution: \"robust alpha spending\" to protect overall type I error probability, statistical power, and surveillance time from low-level data instability present in secondary use data sources used for adverse event monitoring.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70760"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13651786/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138413","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 Bias-Adjusted Approach for Logistic Modeling With Predicted Exposures. 带有预测暴露的Logistic模型的偏差校正方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70740

Aryana Arsham, Paul S Albert, Mark P Little

{"title":"A Bias-Adjusted Approach for Logistic Modeling With Predicted Exposures.","authors":"Aryana Arsham, Paul S Albert, Mark P Little","doi":"10.1002/sim.70740","DOIUrl":"10.1002/sim.70740","url":null,"abstract":"Bias-adjusted estimators are proposed for logistic regression parameters under a mixture measurement error model. In observational studies, collecting the true exposure for all units is often infeasible. This has led to the use of prediction models to estimate the true exposure. However, these estimates are error-prone, introducing measurement error. Measurement error in statistical analysis can result in biased parameter estimates and unreliable conclusions. The method developed in this work uses a mixture error model that accounts for both an error-prone predicted exposure and true exposure. The proposed bias-adjusted approach uses bootstrap estimators of the mixture error model parameters, a user-specified prediction model, and a linear regression calibration function. The approach applies to cohort designs under various exposure distributions when measurement error model parameters are unknown. A Wald confidence interval is constructed using the developed bias-adjusted estimators and their bootstrapped standard errors. The proposed method is evaluated in a simulation study and an observational study in which a logistic regression model relates hypertension incidence to glycohemoglobin exposure. Numerical results demonstrate accurate point estimates and coverage probabilities close to the nominal level. We further investigate the effects of selecting an incorrect prediction model on the proposed approach. The results indicate that using an oversimplified predictive model can lead to severe biases. Additionally, we show that even flexible machine learning models may offer lower accuracy and precision than correctly specified parametric models. Diagnostics for assessing model assumptions are provided in the Supporting Information and illustrated for the motivating example.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70740"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149138400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Cancer Heterogeneity Modeling With Omics Data via Network-Based Block-Wise Regulatory Analysis. 基于网络的分组调控分析的组学数据癌症异质性建模。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70779

Rong Li, Shuangge Ma

{"title":"Cancer Heterogeneity Modeling With Omics Data via Network-Based Block-Wise Regulatory Analysis.","authors":"Rong Li, Shuangge Ma","doi":"10.1002/sim.70779","DOIUrl":"https://doi.org/10.1002/sim.70779","url":null,"abstract":"Omics data have been extensively analyzed to quantify the heterogeneity of cancer and other diseases. Recent studies have suggested that interconnections among omics variables, as reflected in network structures, can be more informative than simpler measures. Multiple types of omics variables can be connected via regulatory relationships, and there are studies modeling heterogeneity based on such regulations. Here, we conduct heterogeneity analysis based on both network structures and regulatory relationships. Significantly different from the existing literature, we consider network structures of regulators, as opposed to regulatees-such networks describe more essential biological mechanisms and are less likely to be blurred by the mixture of direct and indirect interconnections. Another significant advancement is that we incorporate (unknown) blocking structures of regulators, which contain information complementary to network structures. A novel penalization approach is developed to simultaneously identify sample groups and estimate block structures, group-specific networks, and regulation relationships. An effective computational algorithm is developed. It is rigorously proved that the proposed approach enjoys consistency in estimation, selection, and block identification. Simulation studies demonstrate its competitiveness against multiple alternatives. In the analysis of TCGA breast cancer data, we consider the (gene expression, protein expression) dual and make sensible findings that differ from those of the alternatives.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70779"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Nonparametric Data-Fusion Approach for Identification and Estimation of Nonignorable Missing Data With Shadow Variable. 带阴影变量的不可忽略缺失数据识别与估计的非参数数据融合方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70747

Wenxiao Zhou, Bo Fu

{"title":"A Nonparametric Data-Fusion Approach for Identification and Estimation of Nonignorable Missing Data With Shadow Variable.","authors":"Wenxiao Zhou, Bo Fu","doi":"10.1002/sim.70747","DOIUrl":"10.1002/sim.70747","url":null,"abstract":"Missing data can pose fundamental challenges to statistical inference, with nonignorable missing or missing not at random (MNAR) presenting the most severe methodological difficulties. Despite substantial advances in MNAR inference methods, several limitations remain. These generally include interpretability issues, non-identifiability, unverifiable parametric assumptions, and reliance on external information for which clear practical guidance is often lacking-particularly in sensitivity and Bayesian methods. In this paper, motivated by a real-world COVID-19 dataset from the Centers for Disease Control and Prevention (CDC), we study an MNAR scenario in the presence of a shadow variable that may itself also be subject to MNAR. A shadow variable is associated with the primary variable that is MNAR, but is conditionally independent of that variable's missingness given the primary variable and other covariates. Under the pattern mixture model framework, we propose a nonparametric inference method that can leverage external data to explicitly address the identification and estimation problems in the primary data. The method requires inference only of an observed data density and an odds of missing function, with the latter forming the core of our approach. We further provide two multiple-imputation-based estimation strategies, enhancing transparency and interpretability. The proposed framework can accommodate both covariate MNAR and outcome MNAR settings, as well as different variable types, and extends the existing shadow variable paradigm for MNAR data by relaxing the common assumption that the shadow variable must be fully observed. We evaluate the proposed method through simulation studies and apply it to CDC COVID-19 surveillance data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70747"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149070160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Adaptive Design for Assessing Treatment Effect Consistency in Bridging Studies. 桥接研究中评估治疗效果一致性的贝叶斯自适应设计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70749

Xinyi He, Dan Zhao, Zhaoyang Teng, Hua Liu, Ying Yuan

{"title":"A Bayesian Adaptive Design for Assessing Treatment Effect Consistency in Bridging Studies.","authors":"Xinyi He, Dan Zhao, Zhaoyang Teng, Hua Liu, Ying Yuan","doi":"10.1002/sim.70749","DOIUrl":"10.1002/sim.70749","url":null,"abstract":"Bridging studies serve as an efficient approach to evaluate the applicability of findings from an original study to a new population. We propose a Bayesian adaptive design for bridging studies with time-to-event endpoints, aiming to assess the consistency of treatment effects with those observed in the original study. The design adopts a group sequential framework, using posterior probabilities at interim and final analyses to evaluate the evidence for consistency between the bridging and original studies. If strong evidence of consistency or inconsistency is observed, the trial may be stopped early to conserve sample size. Additionally, a sample size recalculation strategy based on Bayesian predictive probability is incorporated to enhance flexibility. Simulation studies demonstrate that, compared to a commonly used method, the proposed design offers favorable operating characteristics. It maintains control of the empirical type I error rate through prespecified calibration, accounting for interim analyses (IAs) and sample size adaptation, and yields substantial sample size savings when the treatment effect in the bridging study is either highly consistent or clearly inconsistent with that of the original study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70749"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13624694/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081984","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

Sandwich Variance Estimation in Scale Mixture of Skew-Normal Linear Mixed Models. 斜正态线性混合模型尺度混合的夹心方差估计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70753

Keyliane Travassos, Larissa A Matos, Fernanda L Schumacher

引用次数: 0

Design and Analysis Strategies for Pooling in High Throughput Screening: Application to the Search for a New Anti-Microbial. 高通量筛选池的设计与分析策略：在寻找新的抗微生物药物中的应用。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70778

Byran J Smucker, Benjamin Brennan, Emily Rego, Meng Wu, Zhihong Lin, Brian M M Ahmer, Blake R Peterson

引用次数: 0

Clinically Interpretable Time-To-Event Testing in Randomized Clinical Trials: Milestone Survival Combined With Restricted Mean Survival Time. 随机临床试验中临床可解释的事件发生时间检验：里程碑生存与限制平均生存时间相结合。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70787

Siwen He, Ludovic Trinquart

{"title":"Clinically Interpretable Time-To-Event Testing in Randomized Clinical Trials: Milestone Survival Combined With Restricted Mean Survival Time.","authors":"Siwen He, Ludovic Trinquart","doi":"10.1002/sim.70787","DOIUrl":"10.1002/sim.70787","url":null,"abstract":"The log-rank test and hazard ratio (HR) are widely used to compare survival curves in event-driven randomized clinical trials (RCTs). The optimality of the log-rank test and interpretation of the HR rely on the proportional hazards (PH) assumption, which is frequently violated in oncology due to delayed, waning, or crossing hazards. Although combination procedures built from weighted log-rank tests improve robustness to non-PH, hazard-based summaries remain difficult to interpret clinically. We propose combination tests that integrate two clinically interpretable estimands: the between-group difference in milestone survival probability <math><mrow><mi>S</mi> <mrow><mo>(</mo> <mi>τ</mi> <mo>)</mo></mrow> </mrow> </math> and in restricted mean survival time (RMST) <math><mrow><mi>μ</mi> <mrow><mo>(</mo> <mn>0</mn> <mo>,</mo> <mi>τ</mi> <mo>)</mo></mrow> </mrow> </math> . Using Monte Carlo simulations under a milestone-based design (uniform follow-up to <math><mrow><mi>τ</mi></mrow> </math> ) and supplementary event-driven designs, we compared the proposed combinations with the log-rank and MaxCombo tests across diverse survival patterns, sample sizes, and censoring rates. We evaluated Type I error and power. The milestone combination procedures showed rejection probabilities close to the nominal level under the estimand-null configurations examined and showed competitive to superior power in non-PH settings, including transient effects and crossing hazards. Applications to three oncology RCTs illustrate that, when HRs hover near unity while absolute estimands differ, the milestone combinations can detect benefits at clinically meaningful horizons. These findings support aligning design, estimands, and analysis around a prespecified milestone. Careful selection of <math><mrow><mi>τ</mi></mrow> </math> is essential to balance information and interpretability. Milestone-based combination tests provide a viable, patient-centered alternative or complement to HR-centric methods, particularly when non-PH is anticipated.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70787"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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