Biometrics最新文献_第8页

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

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

Inference with approximate local false discovery rates. 近似局部错误发现率的推理。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf035

Rajesh Karmakar, Ruth Heller, Saharon Rosset

{"title":"Inference with approximate local false discovery rates.","authors":"Rajesh Karmakar, Ruth Heller, Saharon Rosset","doi":"10.1093/biomtc/ujaf035","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf035","url":null,"abstract":"Efron's 2-group model is widely used in large-scale multiple testing. This model assumes that test statistics are drawn independently from a mixture of a null and a non-null distribution. The marginal local false discovery rate (locFDR) is the probability that the hypothesis is null given its test statistic. The procedure that rejects null hypotheses with marginal locFDRs below a fixed threshold maximizes power (the expected number of non-nulls rejected) while controlling the marginal false discovery rate in this model. However, in realistic settings the test statistics are dependent, and taking the dependence into account can boost power. Unfortunately, the resulting calculations are typically exponential in the number of hypotheses, which is impractical. Instead, we propose using $textrm {locFDR}_N$, which is the probability that the hypothesis is null given the test statistics in its $N$-neighborhood. We prove that rejecting for small $textrm {locFDR}_N$ is optimal in the restricted class where the decision for each hypothesis is only guided by its $N$-neighborhood, and that power increases with $N$. The computational complexity of computing the $mathrm{ locFDR}_N$s increases with $N$, so the analyst should choose the largest $N$-neighborhood that is still computationally feasible. We show through extensive simulations that our proposed procedure can be substantially more powerful than alternative practical approaches, even with small $N$-neighborhoods. We demonstrate the utility of our method in a genome-wide association study of height.","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":"143976983","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

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

Nonparametric assessment of regimen response curve estimators. 方案反应曲线估计器的非参数评估。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf066

Cuong T Pham, Benjamin R Baer, Ashkan Ertefaie

{"title":"Nonparametric assessment of regimen response curve estimators.","authors":"Cuong T Pham, Benjamin R Baer, Ashkan Ertefaie","doi":"10.1093/biomtc/ujaf066","DOIUrl":"10.1093/biomtc/ujaf066","url":null,"abstract":"In the framework of dynamic marginal structural models, regimen-response curve is a function that describes the relation between the mean outcome and the parameters in the class of decision rules. The modeling choice of the regimen-response curve is crucial in constructing an optimal regime, as a misspecified model can lead to a biased estimate with questionable causal interpretability. However, the existing literature lacks methods to evaluate and compare different working models. To address this problem, we will leverage risk to assess the \"goodness-of-fit\" of an imposed working model. We consider the counterfactual risk as our target parameter and derive inverse probability weighting and canonical gradients to map it to the observed data. We provide asymptotic properties of the resulting risk estimators, considering both fixed and data-dependent target parameters. We will show that the inverse probability weighting estimator can be efficient and asymptotic linear when the weight functions are estimated using a sieve-based estimator. The proposed method is implemented on the LS1 study to estimate a regimen-response curve for patients with Parkinson's disease.","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/PMC12123697/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144186382","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

A semicompeting risks model with an application to UK Biobank data to identify risk factors for diabetes onset and progression. 半竞争风险模型与英国生物银行数据的应用，以确定糖尿病发病和进展的危险因素。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf003

Md Tuhin Sheikh, Hongyu Zhao

{"title":"A semicompeting risks model with an application to UK Biobank data to identify risk factors for diabetes onset and progression.","authors":"Md Tuhin Sheikh, Hongyu Zhao","doi":"10.1093/biomtc/ujaf003","DOIUrl":"10.1093/biomtc/ujaf003","url":null,"abstract":"Type 2 diabetes (T2D) is a major health concern worldwide with multiple disease stages, including onset, progression to complications, and death. Understanding the roles of genetic and nongenetic factors at different disease stages is crucial for gaining insights into disease etiology, possible prevention, and treatment strategies. The UK Biobank (UKB) is a valuable resource for studying complex diseases, including T2D, with comprehensive data from half a million volunteer participants. However, the UKB data present some unique challenges due to their semicompeting risks structure, involving 2 nonterminal events (T2D and complications) and one terminal event (death). In this paper, we propose a new shared gamma frailty-based semicompeting risks model within the Bayesian framework to account for subsequent nonterminal and terminal events and enable appropriate analysis. We further propose incorporating prevalent cases, that is, individuals with diabetes at enrollment, to gain more insights into the progression to complications and complications to death. To integrate prevalent cases, we introduce a power prior approach that leads to improved model fit and more efficient estimates. Simulation results demonstrate the efficacy of our modeling framework. We apply our method to identify the impacts of various risk factors at different stages of T2D development.","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/PMC12104815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144141207","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

A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data. 具有潜在随机过程的贝叶斯联合纵向生存模型。

IF 1.7 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf052

Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Lindsey N Potter, David W Wetter, Cho Y Lam, Jeremy M G Taylor

{"title":"A Bayesian joint longitudinal-survival model with a latent stochastic process for intensive longitudinal data.","authors":"Madeline R Abbott, Walter H Dempsey, Inbal Nahum-Shani, Lindsey N Potter, David W Wetter, Cho Y Lam, Jeremy M G Taylor","doi":"10.1093/biomtc/ujaf052","DOIUrl":"10.1093/biomtc/ujaf052","url":null,"abstract":"The availability of mobile health (mHealth) technology has enabled increased collection of intensive longitudinal data (ILD). ILD have potential to capture rapid fluctuations in outcomes that may be associated with changes in the risk of an event. However, existing methods for jointly modeling longitudinal and event-time outcomes are not well-equipped to handle ILD due to the high computational cost. We propose a joint longitudinal and time-to-event model suitable for analyzing ILD. In this model, we summarize a multivariate longitudinal outcome as a smaller number of time-varying latent factors. These latent factors, which are modeled using an Ornstein-Uhlenbeck stochastic process, capture the risk of a time-to-event outcome in a parametric hazard model. We take a Bayesian approach to fit our joint model and conduct simulations to assess its performance. We use it to analyze data from an mHealth study of smoking cessation. We summarize the longitudinal self-reported intensity of 9 emotions as the psychological states of positive and negative affect. These time-varying latent states capture the risk of the first smoking lapse after attempted quit. Understanding factors associated with smoking lapse is of keen interest to smoking cessation researchers.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"81 2","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12050977/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143967240","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

Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint. 以事件发生时间为终点的横截面阶梯楔形群随机试验的功率计算。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf074

Mary Ryan Baumann, Denise Esserman, Monica Taljaard, Fan Li

{"title":"Power calculation for cross-sectional stepped wedge cluster randomized trials with a time-to-event endpoint.","authors":"Mary Ryan Baumann, Denise Esserman, Monica Taljaard, Fan Li","doi":"10.1093/biomtc/ujaf074","DOIUrl":"10.1093/biomtc/ujaf074","url":null,"abstract":"Stepped wedge cluster randomized trials (SW-CRTs) are a form of randomized trial whereby clusters are progressively transitioned from control to intervention, with the timing of transition randomized for each cluster. An important task at the design stage is to ensure that the planned trial has sufficient power. While methods for determining power have been well-developed for SW-CRTs with continuous and binary outcomes, limited methods for power calculation are available for SW-CRTs with censored time-to-event outcomes. In this article, we propose a stratified marginal Cox model to analyze cross-sectional SW-CRTs and then derive an explicit expression of the robust sandwich variance to facilitate power calculations without the need for computationally intensive simulations. Power formulas based on both the Wald and robust score tests are developed, assuming constant within-period and between-period correlation parameters, and are further validated via simulation under different finite-sample scenarios. Finally, we illustrate our methods in the context of a SW-CRT testing the effect of a new electronic reminder system on time to catheter removal in hospital settings. We also offer an R Shiny application to facilitate sample size and power calculations using our proposed 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/PMC12188223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144483096","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

Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach. 揭示COVID-19心力衰竭患者的死亡率模式和医院影响：一种新的多层次logistic聚类加权建模方法

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf046

Luca Caldera, Chiara Masci, Andrea Cappozzo, Marco Forlani, Barbara Antonelli, Olivia Leoni, Francesca Ieva

{"title":"Uncovering mortality patterns and hospital effects in COVID-19 heart failure patients: a novel multilevel logistic cluster-weighted modeling approach.","authors":"Luca Caldera, Chiara Masci, Andrea Cappozzo, Marco Forlani, Barbara Antonelli, Olivia Leoni, Francesca Ieva","doi":"10.1093/biomtc/ujaf046","DOIUrl":"https://doi.org/10.1093/biomtc/ujaf046","url":null,"abstract":"Evaluating hospital performance and its relationship to patients' characteristics is of utmost importance to ensure timely, effective, and optimal treatment. This is particularly relevant in areas and situations where the healthcare system must deal with an unexpected surge in hospitalizations, such as heart failure patients in the Lombardy Region of Italy during the COVID-19 pandemic. Motivated by this issue, the paper introduces a novel multilevel logistic cluster-weighted model for predicting 45-day mortality following hospitalization due to COVID-19. The methodology flexibly accommodates dependence patterns among continuous and dichotomous variables; effectively accounting for group-specific effects in distinct subgroups showing different attributes. A tailored classification expectation-maximization algorithm is developed for parameter estimation, and extensive simulation studies are conducted to evaluate its performance against competing models. The novel approach is applied to administrative data from the Lombardy Region, with the aim of profiling heart failure patients hospitalized for COVID-19 and investigating the hospital-level impact on their overall mortality. A scenario analysis demonstrates the model's efficacy in managing multiple sources of heterogeneity, thereby yielding promising results in aiding healthcare providers and policymakers in the identification of patient-specific treatment pathways.","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":"143960546","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

A semiparametric quantile regression rank score test for zero-inflated data. 零膨胀数据的半参数分位数回归秩得分检验。

IF 1.4 4区数学

Biometrics Pub Date : 2025-04-02 DOI: 10.1093/biomtc/ujaf050

Zirui Wang, Wodan Ling, Tianying Wang

{"title":"A semiparametric quantile regression rank score test for zero-inflated data.","authors":"Zirui Wang, Wodan Ling, Tianying Wang","doi":"10.1093/biomtc/ujaf050","DOIUrl":"10.1093/biomtc/ujaf050","url":null,"abstract":"Zero-inflated data commonly arise in various fields, including economics, healthcare, and environmental sciences, where measurements frequently include an excess of zeros due to structural or sampling mechanisms. Traditional approaches, such as Zero-Inflated Poisson and Zero-Inflated Negative Binomial models, have been widely used to handle excess zeros in count data, but they rely on strong parametric assumptions that may not hold in complex real-world applications. In this paper, we propose a zero-inflated quantile single-index rank-score-based test (ZIQ-SIR) to detect associations between zero-inflated outcomes and covariates, particularly when nonlinear relationships are present. ZIQ-SIR offers a flexible, semi-parametric approach that accounts for the zero-inflated nature of the data and avoids the restrictive assumptions of traditional parametric models. Through simulations, we show that ZIQ-SIR outperforms existing methods by achieving higher power and better Type I error control, owing to its flexibility in modeling zero-inflated and overdispersed data. We apply our method to the real-world dataset: microbiome abundance from the Columbian Gut study. In this application, ZIQ-SIR identifies more significant associations than alternative approaches, while maintaining accurate type I error control.","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/PMC12050976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962446","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