Statistics in Medicine最新文献_第6页

Joint Models of Two Longitudinal Biomarkers and Clustered Survival Data for Cluster-Informed Dynamic Prediction With Application to Periodontitis. 两种纵向生物标志物的联合模型和聚类生存数据用于牙周炎的聚类知情动态预测。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70715

Sean Xinyang Feng, Laurent Briollais, Aya A Mitani

{"title":"Joint Models of Two Longitudinal Biomarkers and Clustered Survival Data for Cluster-Informed Dynamic Prediction With Application to Periodontitis.","authors":"Sean Xinyang Feng, Laurent Briollais, Aya A Mitani","doi":"10.1002/sim.70715","DOIUrl":"10.1002/sim.70715","url":null,"abstract":"Joint modeling of longitudinal data and time-to-event data have been extended to accommodate multilevel data structures. In dental studies, data often exhibit a multilevel hierarchy: each patient has multiple teeth, and one or more biomarkers are measured repeatedly over time for each tooth. In addition to biomarker measurements, the time to tooth loss may vary differently between patients as some patients are more susceptible to tooth loss, conditional on other risk factors. In this paper, we account for intra-patient and intra-tooth correlations in the longitudinal measurement of a continuous biomarker, probing pocket depth (PPD), and a binary biomarker, mobility. We also account for the correlation in time to tooth loss between teeth within the same patient. We jointly model the two longitudinal measurements and the risk of tooth loss using Bayesian estimation. We develop a cluster-informed dynamic prediction framework for the survival outcome, in which the prediction for a given unit is informed not only by its own observed history but also by the observed longitudinal trajectories and event outcomes of other units within the same cluster. We evaluate the predictive performance in terms of discrimination and calibration, accounting for censoring and the multilevel data structure. Our simulation study shows that the proposed joint model produced more accurate estimates and better predictive performance compared to the standard bivariate joint model that ignores the multilevel data structure. We applied our model to electronic periodontal data obtained from the Canadian Armed Forces (CAF).","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70715"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13551296/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148906658","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 Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics. 使用汇总统计在全基因组关联研究中探索多效性关联指南。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70717

Christina Y Feng, Pierre-Emmanuel Sugier, Nan Zou, Benoit Liquet

{"title":"A Guide for Exploring Pleiotropic Associations in Genome-Wide Association Studies Using Summary Statistics.","authors":"Christina Y Feng, Pierre-Emmanuel Sugier, Nan Zou, Benoit Liquet","doi":"10.1002/sim.70717","DOIUrl":"10.1002/sim.70717","url":null,"abstract":"Genome-wide association studies (GWAS) have shown that pleiotropy, whereby a single genetic variant or gene influences multiple traits, is common in complex human diseases. Detecting cross-phenotype associations from GWAS summary statistics remains challenging because of small effect sizes, extensive multiple testing, heterogeneous effects, and possible differences in effect direction across traits. Methods that jointly analyze multiple traits can improve the ability to detect pleiotropic signals while retaining the practical advantages of summary statistic-based analyses. Although a range of statistical approaches has been developed for this purpose, practical guidance on their application, assumptions, and interpretation remains limited. This tutorial reviews several widely used methods for pleiotropy detection from GWAS summary statistics, including ASSET, PLACO, GPA, CPBayes, and GCPBayes, and demonstrates their application using breast and thyroid cancer datasets. We also highlight the importance of accounting for effect heterogeneity, correlation, and biological group structure at the gene and pathway levels in the detection and interpretation of pleiotropic association signals.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70717"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13523090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148841086","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

Multilevel Network Meta-Regression With a Survival Outcome and an Application to Non-Small Cell Lung Cancer. 具有生存结果的多水平网络元回归及其在非小细胞肺癌中的应用。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70728

Ruofan Jia, Huangdi Yi, Zhaoyang Teng, Sammi Tang, Jiping Wang, Shuangge Ma

{"title":"Multilevel Network Meta-Regression With a Survival Outcome and an Application to Non-Small Cell Lung Cancer.","authors":"Ruofan Jia, Huangdi Yi, Zhaoyang Teng, Sammi Tang, Jiping Wang, Shuangge Ma","doi":"10.1002/sim.70728","DOIUrl":"10.1002/sim.70728","url":null,"abstract":"In biopharmaceutical studies, it is often of interest to compare the effects of two treatments (say, A and B), but data that contain a direct comparison are not available. However, there may be studies that compare them against another competitor (say, C). In this case, an indirect comparison of A versus B through C needs to be conducted. In this article, we consider the scenario where individual participant data (IPD) is available for the comparison of A versus C, but only aggregate-level data (AgD), for example, from a publication, is available for the comparison of B versus C. For such analysis, multilevel network meta-regression (ML-NMR) is advantageous since it can combine evidence from multiple trials with either IPD or AgD and can compare the treatments of interest in any target population. Most of the existing ML-NMR studies have focused on binary and continuous outcomes, while, relatively, research on censored survival outcomes remains limited with perhaps only one study modeling the marginal likelihood for AgD. Here, we aim to extend ML-NMR for time-to-event outcomes. We consider multiple popular parametric survival models and develop Bayesian estimation approaches built on mean and median survival. Extensive simulations show satisfactory performance. We further consider a case study on early-stage non-small cell lung cancer (NSCLC) overall survival. Emulation analyses of the Surveillance, Epidemiology, and End Results (SEER)-Medicare data are conducted. It is found that limited resection (LR) with adjuvant chemotherapy (ACT) prolongs survival compared to LR or lobectomy without ACT.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70728"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13539120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148881314","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

Penalised Spline Estimation of Covariate-Specific Time-Dependent ROC Curves. 协变量特异性随时间变化的ROC曲线的惩罚样条估计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70718

María Xosé Rodríguez-Álvarez, Vanda Inácio

{"title":"Penalised Spline Estimation of Covariate-Specific Time-Dependent ROC Curves.","authors":"María Xosé Rodríguez-Álvarez, Vanda Inácio","doi":"10.1002/sim.70718","DOIUrl":"10.1002/sim.70718","url":null,"abstract":"The identification of biomarkers with high predictive accuracy is a crucial task in medical research, as it can aid clinicians in making early decisions, thereby reducing morbidity and mortality in high-risk populations. Time-dependent receiver operating characteristic (ROC) curves are the main tool used to assess the accuracy of prognostic biomarkers for outcomes that evolve over time. Recognising the need to account for patient heterogeneity when evaluating the accuracy of a prognostic biomarker, we introduce a novel penalised-spline-based estimator of the time-dependent ROC curve that accommodates a possible modifying effect of covariates. We consider flexible models for both the hazard function of the event time given the covariates and biomarker and for the location-scale regression model of the biomarker given covariates, enabling the accommodation of non-proportional hazards and nonlinear effects through penalised splines, thus overcoming limitations of earlier methods. The simulation study demonstrates that our approach successfully recovers the true functional form of the covariate-specific time-dependent ROC curve and the corresponding area under the curve across a variety of scenarios. Comparisons with existing methods further show that our approach performs favourably in multiple settings. Our approach is applied to evaluate the ability of the Global Registry of Acute Coronary Events risk score to predict mortality over different time periods after discharge in patients who have suffered an acute coronary syndrome and to investigate how this ability may vary with the left ventricular ejection fraction. An R package, CondTimeROC, implementing the proposed method is provided.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70718"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546971/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901278","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

Comparing Missing Data Strategies for Generalized Pairwise Comparisons in Randomized Clinical Trials: A Simulation Study. 比较随机临床试验中广义两两比较的缺失数据策略：一项模拟研究。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70737

Ruben P A van Eijk, Ying Lu

{"title":"Comparing Missing Data Strategies for Generalized Pairwise Comparisons in Randomized Clinical Trials: A Simulation Study.","authors":"Ruben P A van Eijk, Ying Lu","doi":"10.1002/sim.70737","DOIUrl":"10.1002/sim.70737","url":null,"abstract":"Generalized pairwise comparisons are increasingly being used in randomized clinical trials. This study evaluates how missing data strategies impact the operating characteristics of endpoints combining overall survival and a longitudinal outcome. A simulation study was conducted to evaluate the impact of censoring and missing longitudinal data on the Type I error, power, and bias of a hierarchical composite endpoint. Seven missing data strategies were identified from the literature, including assigning ties to missing patient pairs, comparing patients at their last common visit, and applying multiple imputation. Conditional longitudinal and survival data were generated based on the natural history of amyotrophic lateral sclerosis. Simulation scenarios varied in censoring rates, extent of missing longitudinal data, differential attrition between treatment arms, and the dependency of missingness on disease severity. All methods were unbiased and maintained nominal Type I error when censoring and missingness were balanced across treatment arms. Under imbalance, however, Type I error increased-reaching up to 0.378 for some strategies. The last common visit strategy was the only approach that consistently preserved nominal error rates (0.025 ± 0.002). Regarding statistical power, all methods exhibited a loss of precision and increased bias under the alternative hypothesis as missingness increased. Multiple imputation partially recovered power and reduced bias but inflated the Type I error rate in scenarios with differential attrition. As generalized pairwise comparisons are increasingly used in pivotal clinical trials-and therefore in regulatory contexts-our findings provide practical guidance for selecting a robust primary analysis strategy and minimizing bias arising from incomplete data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70737"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13584238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148991377","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 Novel Bayesian Distribution-Free Survival Analysis. 一种新的贝叶斯无分布生存分析。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70736

Richard A Chechile, Daniel H Barch

{"title":"A Novel Bayesian Distribution-Free Survival Analysis.","authors":"Richard A Chechile, Daniel H Barch","doi":"10.1002/sim.70736","DOIUrl":"10.1002/sim.70736","url":null,"abstract":"A new Bayesian distribution-free medical survivor analysis is presented that strictly avoids using right-tail censored data. The Bayesian procedure is focused on two population parameters called <math> <mrow> <msub><mrow><mi>ϕ</mi></mrow> <mrow><mi>f</mi> <mi>c</mi></mrow> </msub> </mrow> </math> and <math> <mrow> <msub><mrow><mi>Ω</mi></mrow> <mrow><mi>E</mi></mrow> </msub> </mrow> </math> , which are separate metrics of a treatment difference between the two conditions. The <math> <mrow> <msub><mrow><mi>ϕ</mi></mrow> <mrow><mi>f</mi> <mi>c</mi></mrow> </msub> </mrow> </math> parameter is the proportion of failures that are from the Control condition, and the <math> <mrow> <msub><mrow><mi>Ω</mi></mrow> <mrow><mi>E</mi></mrow> </msub> </mrow> </math> parameter is a stochastic difference measure associated with longer survival time before a failure for the patients in the Experimental condition. Point and interval estimates along with a Bayes factor are provided for both parameters. Software is provided for implementing the Bayesian analysis. Monte Carlo simulations are also developed to study the interplay between the stop-testing time used in survival analysis with other variables such as effect size and sample size. The simulations also provide information that enable an approach to survival analysis where there is an early decision about the relative effectiveness of the treatments that is based on the Bayes factor value associated with the <math> <mrow> <msub><mrow><mi>ϕ</mi></mrow> <mrow><mi>f</mi> <mi>c</mi></mrow> </msub> </mrow> </math> statistic. This decision can be made when there is a high proportion of right-tail censored data. In the limit of times so long that there are all failures-thus no right-tail censoring-the <math> <mrow> <msub><mrow><mi>ϕ</mi></mrow> <mrow><mi>f</mi> <mi>c</mi></mrow> </msub> </mrow> </math> metric is not informative, but the <math> <mrow> <msub><mrow><mi>Ω</mi></mrow> <mrow><mi>E</mi></mrow> </msub> </mrow> </math> metric is still a valid measure of the stochastic difference in failure times between the two conditions.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70736"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017367","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

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits. 受检测限约束的连续结果的惩罚累积概率模型。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70723

Shuai Sun, Valeria R Mas, Kellie J Archer

{"title":"Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.","authors":"Shuai Sun, Valeria R Mas, Kellie J Archer","doi":"10.1002/sim.70723","DOIUrl":"10.1002/sim.70723","url":null,"abstract":"Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24 months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70723"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13523091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148841165","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

Regression Not-to-the-Mean: An Oddity of Regression, Illustrated With the Risk of Overdose Deaths. 非均值回归：回归的一种奇怪现象，以过量死亡的风险为例。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70734

Kelly C Kung, Natasha K Martin, Judith J Lok

{"title":"Regression Not-to-the-Mean: An Oddity of Regression, Illustrated With the Risk of Overdose Deaths.","authors":"Kelly C Kung, Natasha K Martin, Judith J Lok","doi":"10.1002/sim.70734","DOIUrl":"10.1002/sim.70734","url":null,"abstract":"Recent works in econometrics have shown that there can be issues with applying a constant treatment effect model in longitudinal settings with staggered treatment and heterogeneous treatment effects. We focus on the issue that the estimated constant treatment effect may be a weighted average, with some negative weights, of treatment effects that are heterogeneous across treatment durations. When this issue arises, the estimated constant treatment effect and estimated heterogeneous treatment effects may result in conflicting results. Through the example of estimating the effect of drug-induced homicide (DIH) prosecutions reported by media on unintentional drug-overdose deaths in the United States, we illustrate how the negative weighting issue can lead to conflicting results in practice. Moreover, although research has shown that the negative weight issue may arise in linear regression models, we show this issue may also arise in logistic regression models. Using a linear link, we estimated a constant treatment effect risk ratio of 0.977 (95% CI: [0.866, 1.101]) and an average risk ratio of 0.728 (range: 0.507-0.979) over different treatment durations. Using a logistic link, we estimated a constant treatment risk ratio effect of 1.064 (95% CI: [0.972, 1.165]) and an average risk ratio of 0.739 (range: 0.538-1.008) over different treatment durations. Under both models, the estimated constant treatment effect is either smaller in magnitude or has a different sign than almost all estimated heterogeneous treatment effects, suggesting a negative weighting issue is present. Our results suggest additional care is needed when applying constant treatment effect models in longitudinal settings.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70734"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13546970/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148901287","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

Event-Driven Type Design for Clinical Trials With Recurrent Events. 复发事件临床试验的事件驱动型设计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70742

Jingwen Zhang, Satoshi Hattori

{"title":"Event-Driven Type Design for Clinical Trials With Recurrent Events.","authors":"Jingwen Zhang, Satoshi Hattori","doi":"10.1002/sim.70742","DOIUrl":"10.1002/sim.70742","url":null,"abstract":"It is common practice in randomized clinical trials with the standard survival outcome to follow patients until a prespecified number of events have been observed, a type of trial known as the event-driven trial. The event-driven design ensures that the target power for a specified Type I error rate is achieved to detect the target hazard ratio, regardless of the specification of other quantities. To understand the treatment effect for chronic conditions, the analysis of recurrent events has gained popularity in randomized controlled trials, particularly large-scale confirmatory trials. In the absence of within-subject correlation among multiple events, a similar event-driven design can be employed for recurrent event outcomes. On the other hand, in the presence of within-subject correlation, one needs to model the correlation among recurrent events in evaluating power and setting the sample size. However, information useful in modeling the within-subject correlation is limited at the design stage. Failing to properly account for correlation may lead to underpowered studies. We propose an event-driven type design for recurrent event outcomes. Our method ensures the target power for the target treatment effect, regardless of the specification of other quantities, by monitoring the robust variance under the marginal rates/means model in a blinded manner. We investigate the operating characteristics of the proposed monitoring procedure in simulation studies. The results of simulation studies showed that the proposed blinded monitoring procedure controlled the power well so that the test possessed the target power and did not lead to serious inflation of the Type I error rate. Furthermore, we illustrate the proposed method using a real clinical trial dataset.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70742"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13575311/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971353","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

Deconfounded-Debiased Estimation and Inference for High-Dimensional Mediation Analysis With Pervasive Hidden Confounders. 具有普遍隐藏混杂因素的高维中介分析的去基础去偏估计与推理。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-09-01 DOI: 10.1002/sim.70741

Zhaoyang Li, Lulu Pan, Yongfu Yu, Guoyou Qin, Bo Fu

{"title":"Deconfounded-Debiased Estimation and Inference for High-Dimensional Mediation Analysis With Pervasive Hidden Confounders.","authors":"Zhaoyang Li, Lulu Pan, Yongfu Yu, Guoyou Qin, Bo Fu","doi":"10.1002/sim.70741","DOIUrl":"10.1002/sim.70741","url":null,"abstract":"Mediation analysis is a powerful tool for elucidating the causal mechanisms by which exposures influence outcomes through mediators. However, conventional approaches often yield biased estimates of mediation effects in scenarios involving high-dimensional exposures and high-dimensional mediators, alongside a small number of pervasive hidden confounders that simultaneously affect the exposures, mediators, and outcome or the exposures and outcome. To address these challenges, we propose a deconfounded-debiased method for estimation and inference in high-dimensional mediation analysis based on the difference-in-coefficients strategy. This approach effectively corrects biases arising from both pervasive hidden confounders and high-dimensionality without requiring prior knowledge of hidden confounders or a sparse precision matrix assumption. We establish the asymptotic normality of the proposed estimators for both direct and indirect effects, and develop hypothesis testing procedures that asymptotically achieve validity and power lower bounds. Simulation experiments demonstrate our method's superior finite-sample performance in both estimation and inference, especially in the presence of pervasive hidden confounding. We also apply the proposed method to real data from the Alzheimer's Disease Neuroimaging Initiative to identify serum metabolites that influence Alzheimer's disease progression through DNA methylation pathways.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 20-22","pages":"e70741"},"PeriodicalIF":2.1,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13577804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148977856","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