Biometrics最新文献_第3页

Non-parametric estimators of hazard ratios for comparing two survival curves. 比较两条生存曲线的风险比的非参数估计。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf072

Mihai Giurcanu, Theodore Karrison

引用次数: 0

On the finite-sample and asymptotic error control of a randomization-probability test for response-adaptive clinical trials. 反应适应性临床试验随机概率检验的有限样本和渐近误差控制。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf069

Nina Deliu, Sofia S Villar

引用次数: 0

Leveraging two-phase data for improved prediction of survival outcomes with application to nasopharyngeal cancer. 利用两期数据改善鼻咽癌生存预后预测。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf080

Eun Jeong Oh, Seungjun Ahn, Tristan Tham, Min Qian

{"title":"Leveraging two-phase data for improved prediction of survival outcomes with application to nasopharyngeal cancer.","authors":"Eun Jeong Oh, Seungjun Ahn, Tristan Tham, Min Qian","doi":"10.1093/biomtc/ujaf080","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf080","url":null,"abstract":"Accurate survival predicting models are essential for improving targeted cancer therapies and clinical care among cancer patients. In this article, we investigate and develop a method to improve predictions of survival in cancer by leveraging two-phase data with expert knowledge and prognostic index. Our work is motivated by two-phase data in nasopharyngeal cancer (NPC), where traditional covariates are readily available for all subjects, but the primary viral factor, human papillomavirus (HPV), is substantially missing. To address this challenge, we propose an expert-guided method that incorporates prognostic index based on the observed covariates and clinical importance of key factors. The proposed method makes efficient use of available data, not simply discarding patients with unknown HPV status. We apply the proposed method and evaluate it against other existing approaches through a series of simulation studies and real data example of NPC patients. Under various settings, the proposed method consistently outperforms competing methods in terms of c-index, calibration slope, and integrated Brier score. By efficiently leveraging two-phase data, the model provides a more accurate and reliable predictive ability of survival models.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144494095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rejoinder to the discussion on "Continuous-space occupancy models". 回答关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf058

Wilson J Wright, Mevin B Hooten

引用次数: 0

Discussion on "Continuous-space occupancy models" by Wilson J. Wright and Mevin B. Hooten. Wilson J. Wright和Mevin B. Hooten关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf057

Léa Pautrel, Marie-Pierre Etienne, Olivier Gimenez

引用次数: 0

Discussion on "Continuous-space occupancy models" by Wilson J. Wright and Mevin B. Hooten. Wilson J. Wright和Mevin B. Hooten关于“连续空间占用模式”的讨论。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf056

Jeffrey W Doser, Krishna Pacifici

引用次数: 0

Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data. 高维微生物组组成数据的功率增强双样本均值测试。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf034

Danning Li, Lingzhou Xue, Haoyi Yang, Xiufan Yu

{"title":"Power-enhanced two-sample mean tests for high-dimensional microbiome compositional data.","authors":"Danning Li, Lingzhou Xue, Haoyi Yang, Xiufan Yu","doi":"10.1093/biomtc/ujaf034","DOIUrl":"10.1093/biomtc/ujaf034","url":null,"abstract":"Testing differences in mean vectors is a fundamental task in the analysis of high-dimensional microbiome compositional data. Existing methods may suffer from low power if the underlying signal pattern is in a situation that does not favor the deployed test. In this work, we develop 2-sample power-enhanced mean tests for high-dimensional compositional data based on the combination of $P$-values, which integrates strengths from 2 popular types of tests: the maximum-type test and the quadratic-type test. We provide rigorous theoretical guarantees on the proposed tests, showing accurate Type-I error rate control and enhanced testing power. Our method boosts the testing power toward a broader alternative space, which yields robust performance across a wide range of signal pattern settings. Our methodology and theory also contribute to the literature on power enhancement and Gaussian approximation for high-dimensional hypothesis testing. We demonstrate the performance of our method on both simulated data and real-world microbiome data, showing that our proposed approach improves the testing power substantially compared to existing methods.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11962435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143762714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning. 随做随学（LAGO）试验：通过持续学习优化治疗方法，防止试验失败。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf061

Ante Bing, Donna Spiegelman, Daniel Nevo, Judith J Lok

{"title":"Learn-As-you-GO (LAGO) trials: optimizing treatments and preventing trial failure through ongoing learning.","authors":"Ante Bing, Donna Spiegelman, Daniel Nevo, Judith J Lok","doi":"10.1093/biomtc/ujaf061","DOIUrl":"10.1093/biomtc/ujaf061","url":null,"abstract":"It is well known that changing the intervention package while a trial is ongoing does not lead to valid inference using standard statistical methods. However, it is often necessary to adapt, tailor, or tweak a complex intervention package in public health implementation trials, especially when the intervention package does not have the desired effect. This article presents conditions under which the resulting analyses remain valid even when the intervention package is adapted while a trial is ongoing. Our results on such Learn-As-you-GO (LAGO) trials extend the theory of LAGO for binary outcomes following a logistic regression model to LAGO for continuous outcomes under flexible conditional mean models. Because the mathematical methods for binary outcomes do not apply to continuous outcomes, the theory presented in this paper is entirely new. We derive point and interval estimators of the intervention effects and ensure the validity of hypothesis tests for an overall intervention effect. We develop a confidence set for the optimal intervention package, which achieves a pre-specified mean outcome while minimizing cost, and confidence bands for the mean outcome under all intervention package compositions. This work will be useful for the design and analysis of large-scale intervention trials where the intervention package is adapted, tailored, or tweaked while the trial is ongoing.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12099308/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144126537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","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 and efficient semi-supervised learning for Ising model. 伊辛模型的鲁棒高效半监督学习。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf060

Daiqing Wu, Molei Liu

{"title":"Robust and efficient semi-supervised learning for Ising model.","authors":"Daiqing Wu, Molei Liu","doi":"10.1093/biomtc/ujaf060","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf060","url":null,"abstract":"In biomedical studies, it is often desirable to characterize the interactive mode of multiple disease outcomes beyond their marginal risk. Ising model is one of the most popular choices serving this purpose. Nevertheless, learning efficiency of Ising models can be impeded by the scarcity of accurate disease labels, which is a prominent problem in contemporary studies driven by electronic health records (EHRs). Semi-supervised learning (SSL) leverages the large unlabeled sample with auxiliary EHR features to assist the learning with labeled data only and is a potential solution to this issue. In this paper, we develop a novel SSL method for efficient inference of Ising model. Our method first models the outcomes against the auxiliary features, then uses it to project the score function of the supervised estimator onto the EHR features, and incorporates the unlabeled sample to augment the supervised estimator for variance reduction without introducing bias. For the key step of conditional modeling, we propose strategies that can effectively leverage the auxiliary EHR information while maintaining moderate model complexity. In addition, we introduce approaches including intrinsic efficient updates and ensemble, to overcome the potential misspecification of the conditional model that may cause efficiency loss. Our method is justified by asymptotic theory and shown to outperform existing SSL methods through simulation studies. We also illustrate its utility in a real example about several key phenotypes related to frequent intensive care unit (ICU) admission on MIMIC-III data set.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144075635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiple bias calibration for valid statistical inference under nonignorable nonresponse. 不可忽略非响应下有效统计推断的多偏差校准。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf044

Seonghun Cho, Jae Kwang Kim, Yumou Qiu

引用次数: 0