Biometrics最新文献_第10页

Visibility graph-based covariance functions for scalable spatial analysis in non-convex partially Euclidean domains. 基于可见性图的协方差函数，用于非凸部分欧几里得域的可扩展空间分析。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae089

Brian Gilbert, Abhirup Datta

{"title":"Visibility graph-based covariance functions for scalable spatial analysis in non-convex partially Euclidean domains.","authors":"Brian Gilbert, Abhirup Datta","doi":"10.1093/biomtc/ujae089","DOIUrl":"https://doi.org/10.1093/biomtc/ujae089","url":null,"abstract":"We present a new method for constructing valid covariance functions of Gaussian processes for spatial analysis in irregular, non-convex domains such as bodies of water. Standard covariance functions based on geodesic distances are not guaranteed to be positive definite on such domains, while existing non-Euclidean approaches fail to respect the partially Euclidean nature of these domains where the geodesic distance agrees with the Euclidean distances for some pairs of points. Using a visibility graph on the domain, we propose a class of covariance functions that preserve Euclidean-based covariances between points that are connected in the domain while incorporating the non-convex geometry of the domain via conditional independence relationships. We show that the proposed method preserves the partially Euclidean nature of the intrinsic geometry on the domain while maintaining validity (positive definiteness) and marginal stationarity of the covariance function over the entire parameter space, properties which are not always fulfilled by existing approaches to construct covariance functions on non-convex domains. We provide useful approximations to improve computational efficiency, resulting in a scalable algorithm. We compare the performance of our method with those of competing state-of-the-art methods using simulation studies on synthetic non-convex domains. The method is applied to data regarding acidity levels in the Chesapeake Bay, showing its potential for ecological monitoring in real-world spatial applications on irregular domains.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153112","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

Estimating the size of a closed population by modeling latent and observed heterogeneity. 通过对潜在和观察到的异质性建模来估算封闭种群的规模。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae017

Francesco Bartolucci, Antonio Forcina

{"title":"Estimating the size of a closed population by modeling latent and observed heterogeneity.","authors":"Francesco Bartolucci, Antonio Forcina","doi":"10.1093/biomtc/ujae017","DOIUrl":"10.1093/biomtc/ujae017","url":null,"abstract":"The paper extends the empirical likelihood (EL) approach of Liu et al. to a new and very flexible family of latent class models for capture-recapture data also allowing for serial dependence on previous capture history, conditionally on latent type and covariates. The EL approach allows to estimate the overall population size directly rather than by adding estimates conditional to covariate configurations. A Fisher-scoring algorithm for maximum likelihood estimation is proposed and a more efficient alternative to the traditional EL approach for estimating the non-parametric component is introduced; this allows us to show that the mapping between the non-parametric distribution of the covariates and the probabilities of being never captured is one-to-one and strictly increasing. Asymptotic results are outlined, and a procedure for constructing profile likelihood confidence intervals for the population size is presented. Two examples based on real data are used to illustrate the proposed approach and a simulation study indicates that, when estimating the overall undercount, the method proposed here is substantially more efficient than the one based on conditional maximum likelihood estimation, especially when the sample size is not sufficiently large.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140304679","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

Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors. 基于线性混合模型，使用加权和自校准预测因子标记异常群集。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae022

Charles E McCulloch, John M Neuhaus, Ross D Boylan

{"title":"Flagging unusual clusters based on linear mixed models using weighted and self-calibrated predictors.","authors":"Charles E McCulloch, John M Neuhaus, Ross D Boylan","doi":"10.1093/biomtc/ujae022","DOIUrl":"10.1093/biomtc/ujae022","url":null,"abstract":"Statistical models incorporating cluster-specific intercepts are commonly used in hierarchical settings, for example, observations clustered within patients or patients clustered within hospitals. Predicted values of these intercepts are often used to identify or \"flag\" extreme or outlying clusters, such as poorly performing hospitals or patients with rapid declines in their health. We consider a variety of flagging rules, assessing different predictors, and using different accuracy measures. Using theoretical calculations and comprehensive numerical evaluation, we show that previously proposed rules based on the 2 most commonly used predictors, the usual best linear unbiased predictor and fixed effects predictor, perform extremely poorly: the incorrect flagging rates are either unacceptably high (approaching 0.5 in the limit) or overly conservative (eg, much <0.05 for reasonable parameter values, leading to very low correct flagging rates). We develop novel methods for flagging extreme clusters that can control the incorrect flagging rates, including very simple-to-use versions that we call \"self-calibrated.\" The new methods have substantially higher correct flagging rates than previously proposed methods for flagging extreme values, while controlling the incorrect flagging rates. We illustrate their application using data on length of stay in pediatric hospitals for children admitted for asthma diagnoses.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140334556","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

Discussion on "Bayesian meta-analysis of penetrance for cancer risk" by Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani, and Swati Biswas. Thanthirige Lakshika M. Ruberu、Danielle Braun、Giovanni Parmigiani 和 Swati Biswas 关于 "癌症风险渗透的贝叶斯元分析 "的讨论。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae039

Sudipto Banerjee

引用次数: 0

Robustness of response-adaptive randomization. 反应自适应随机化的稳健性。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae049

Xiaoqing Ye, Feifang Hu, Wei Ma

{"title":"Robustness of response-adaptive randomization.","authors":"Xiaoqing Ye, Feifang Hu, Wei Ma","doi":"10.1093/biomtc/ujae049","DOIUrl":"https://doi.org/10.1093/biomtc/ujae049","url":null,"abstract":"Doubly adaptive biased coin design (DBCD), a response-adaptive randomization scheme, aims to skew subject assignment probabilities based on accrued responses for ethical considerations. Recent years have seen substantial advances in understanding DBCD's theoretical properties, assuming correct model specification for the responses. However, concerns have been raised about the impact of model misspecification on its design and analysis. In this paper, we assess the robustness to both design model misspecification and analysis model misspecification under DBCD. On one hand, we confirm that the consistency and asymptotic normality of the allocation proportions can be preserved, even when the responses follow a distribution other than the one imposed by the design model during the implementation of DBCD. On the other hand, we extensively investigate three commonly used linear regression models for estimating and inferring the treatment effect, namely difference-in-means, analysis of covariance (ANCOVA) I, and ANCOVA II. By allowing these regression models to be arbitrarily misspecified, thereby not reflecting the true data generating process, we derive the consistency and asymptotic normality of the treatment effect estimators evaluated from the three models. The asymptotic properties show that the ANCOVA II model, which takes covariate-by-treatment interaction terms into account, yields the most efficient estimator. These results can provide theoretical support for using DBCD in scenarios involving model misspecification, thereby promoting the widespread application of this randomization procedure.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141178772","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 Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease. 学习阿尔茨海默病临床前阶段生物标记物轨迹和变化点的贝叶斯半参数模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae048

Kunbo Wang, William Hua, MeiCheng Wang, Yanxun Xu

{"title":"A Bayesian semi-parametric model for learning biomarker trajectories and changepoints in the preclinical phase of Alzheimer's disease.","authors":"Kunbo Wang, William Hua, MeiCheng Wang, Yanxun Xu","doi":"10.1093/biomtc/ujae048","DOIUrl":"10.1093/biomtc/ujae048","url":null,"abstract":"It has become consensus that mild cognitive impairment (MCI), one of the early symptoms onset of Alzheimer's disease (AD), may appear 10 or more years after the emergence of neuropathological abnormalities. Therefore, understanding the progression of AD biomarkers and uncovering when brain alterations begin in the preclinical stage, while patients are still cognitively normal, are crucial for effective early detection and therapeutic development. In this paper, we develop a Bayesian semiparametric framework that jointly models the longitudinal trajectory of the AD biomarker with a changepoint relative to the occurrence of symptoms onset, which is subject to left truncation and right censoring, in a heterogeneous population. Furthermore, unlike most existing methods assuming that everyone in the considered population will eventually develop the disease, our approach accounts for the possibility that some individuals may never experience MCI or AD, even after a long follow-up time. We evaluate the proposed model through simulation studies and demonstrate its clinical utility by examining an important AD biomarker, ptau181, using a dataset from the Biomarkers of Cognitive Decline Among Normal Individuals (BIOCARD) study.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110494/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141074619","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

Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes. 采用非参数方法估计零膨胀计数结果移动健康中的因果偏移效应。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae054

Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty

{"title":"Incorporating nonparametric methods for estimating causal excursion effects in mobile health with zero-inflated count outcomes.","authors":"Xueqing Liu, Tianchen Qian, Lauren Bell, Bibhas Chakraborty","doi":"10.1093/biomtc/ujae054","DOIUrl":"https://doi.org/10.1093/biomtc/ujae054","url":null,"abstract":"In mobile health, tailoring interventions for real-time delivery is of paramount importance. Micro-randomized trials have emerged as the \"gold-standard\" methodology for developing such interventions. Analyzing data from these trials provides insights into the efficacy of interventions and the potential moderation by specific covariates. The \"causal excursion effect,\" a novel class of causal estimand, addresses these inquiries. Yet, existing research mainly focuses on continuous or binary data, leaving count data largely unexplored. The current work is motivated by the Drink Less micro-randomized trial from the UK, which focuses on a zero-inflated proximal outcome, i.e., the number of screen views in the subsequent hour following the intervention decision point. To be specific, we revisit the concept of causal excursion effect, specifically for zero-inflated count outcomes, and introduce novel estimation approaches that incorporate nonparametric techniques. Bidirectional asymptotics are established for the proposed estimators. Simulation studies are conducted to evaluate the performance of the proposed methods. As an illustration, we also implement these methods to the Drink Less trial data.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141260409","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 Bayesian convolutional neural network-based generalized linear model. 基于贝叶斯卷积神经网络的广义线性模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae057

Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park

{"title":"A Bayesian convolutional neural network-based generalized linear model.","authors":"Yeseul Jeon, Won Chang, Seonghyun Jeong, Sanghoon Han, Jaewoo Park","doi":"10.1093/biomtc/ujae057","DOIUrl":"https://doi.org/10.1093/biomtc/ujae057","url":null,"abstract":"Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141417569","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

Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders. 通过对高维暴露和混杂因素进行中介分析，剖析定位的 GWAS 和 eQTL。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae050

Qi Zhang, Zhikai Yang, Jinliang Yang

{"title":"Dissecting the colocalized GWAS and eQTLs with mediation analysis for high-dimensional exposures and confounders.","authors":"Qi Zhang, Zhikai Yang, Jinliang Yang","doi":"10.1093/biomtc/ujae050","DOIUrl":"https://doi.org/10.1093/biomtc/ujae050","url":null,"abstract":"To leverage the advancements in genome-wide association studies (GWAS) and quantitative trait loci (QTL) mapping for traits and molecular phenotypes to gain mechanistic understanding of the genetic regulation, biological researchers often investigate the expression QTLs (eQTLs) that colocalize with QTL or GWAS peaks. Our research is inspired by 2 such studies. One aims to identify the causal single nucleotide polymorphisms that are responsible for the phenotypic variation and whose effects can be explained by their impacts at the transcriptomic level in maize. The other study in mouse focuses on uncovering the cis-driver genes that induce phenotypic changes by regulating trans-regulated genes. Both studies can be formulated as mediation problems with potentially high-dimensional exposures, confounders, and mediators that seek to estimate the overall indirect effect (IE) for each exposure. In this paper, we propose MedDiC, a novel procedure to estimate the overall IE based on difference-in-coefficients approach. Our simulation studies find that MedDiC offers valid inference for the IE with higher power, shorter confidence intervals, and faster computing time than competing methods. We apply MedDiC to the 2 aforementioned motivating datasets and find that MedDiC yields reproducible outputs across the analysis of closely related traits, with results supported by external biological evidence. The code and additional information are available on our GitHub page (https://github.com/QiZhangStat/MedDiC).","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 2","pages":""},"PeriodicalIF":1.9,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141155115","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

Deep partially linear cox model for current status data. 针对现状数据的深度部分线性 Cox 模型。

IF 1.9 4区数学

Biometrics Pub Date : 2024-03-27 DOI: 10.1093/biomtc/ujae024

Qiang Wu, Xingwei Tong, Xingqiu Zhao

引用次数: 0