IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae065

Janie Coulombe, Shu Yang

引用次数: 0

Nonparametric second-order estimation for spatiotemporal point patterns. 时空点模式的非参数二阶估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae071

Decai Liang, Jialing Liu, Ye Shen, Yongtao Guan

引用次数: 0

Joint structure learning and causal effect estimation for categorical graphical models. 分类图形模型的联合结构学习和因果效应估计

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae067

Federico Castelletti, Guido Consonni, Marco L Della Vedova

{"title":"Joint structure learning and causal effect estimation for categorical graphical models.","authors":"Federico Castelletti, Guido Consonni, Marco L Della Vedova","doi":"10.1093/biomtc/ujae067","DOIUrl":"https://doi.org/10.1093/biomtc/ujae067","url":null,"abstract":"The scope of this paper is a multivariate setting involving categorical variables. Following an external manipulation of one variable, the goal is to evaluate the causal effect on an outcome of interest. A typical scenario involves a system of variables representing lifestyle, physical and mental features, symptoms, and risk factors, with the outcome being the presence or absence of a disease. These variables are interconnected in complex ways, allowing the effect of an intervention to propagate through multiple paths. A distinctive feature of our approach is the estimation of causal effects while accounting for uncertainty in both the dependence structure, which we represent through a directed acyclic graph (DAG), and the DAG-model parameters. Specifically, we propose a Markov chain Monte Carlo algorithm that targets the joint posterior over DAGs and parameters, based on an efficient reversible-jump proposal scheme. We validate our method through extensive simulation studies and demonstrate that it outperforms current state-of-the-art procedures in terms of estimation accuracy. Finally, we apply our methodology to analyze a dataset on depression and anxiety in undergraduate students.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787239","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

An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data. 用于分析空间解析转录组学数据的带特征选择的可解释贝叶斯聚类方法。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae066

Huimin Li, Bencong Zhu, Xi Jiang, Lei Guo, Yang Xie, Lin Xu, Qiwei Li

{"title":"An interpretable Bayesian clustering approach with feature selection for analyzing spatially resolved transcriptomics data.","authors":"Huimin Li, Bencong Zhu, Xi Jiang, Lei Guo, Yang Xie, Lin Xu, Qiwei Li","doi":"10.1093/biomtc/ujae066","DOIUrl":"10.1093/biomtc/ujae066","url":null,"abstract":"Recent breakthroughs in spatially resolved transcriptomics (SRT) technologies have enabled comprehensive molecular characterization at the spot or cellular level while preserving spatial information. Cells are the fundamental building blocks of tissues, organized into distinct yet connected components. Although many non-spatial and spatial clustering approaches have been used to partition the entire region into mutually exclusive spatial domains based on the SRT high-dimensional molecular profile, most require an ad hoc selection of less interpretable dimensional-reduction techniques. To overcome this challenge, we propose a zero-inflated negative binomial mixture model to cluster spots or cells based on their molecular profiles. To increase interpretability, we employ a feature selection mechanism to provide a low-dimensional summary of the SRT molecular profile in terms of discriminating genes that shed light on the clustering result. We further incorporate the SRT geospatial profile via a Markov random field prior. We demonstrate how this joint modeling strategy improves clustering accuracy, compared with alternative state-of-the-art approaches, through simulation studies and 3 real data applications.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11285114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141787236","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

The multivariate Bernoulli detector: change point estimation in discrete survival analysis. 多元伯努利检测器：离散生存分析中的变化点估计。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae075

Willem van den Boom, Maria De Iorio, Fang Qian, Alessandra Guglielmi

{"title":"The multivariate Bernoulli detector: change point estimation in discrete survival analysis.","authors":"Willem van den Boom, Maria De Iorio, Fang Qian, Alessandra Guglielmi","doi":"10.1093/biomtc/ujae075","DOIUrl":"https://doi.org/10.1093/biomtc/ujae075","url":null,"abstract":"Time-to-event data are often recorded on a discrete scale with multiple, competing risks as potential causes for the event. In this context, application of continuous survival analysis methods with a single risk suffers from biased estimation. Therefore, we propose the multivariate Bernoulli detector for competing risks with discrete times involving a multivariate change point model on the cause-specific baseline hazards. Through the prior on the number of change points and their location, we impose dependence between change points across risks, as well as allowing for data-driven learning of their number. Then, conditionally on these change points, a multivariate Bernoulli prior is used to infer which risks are involved. Focus of posterior inference is cause-specific hazard rates and dependence across risks. Such dependence is often present due to subject-specific changes across time that affect all risks. Full posterior inference is performed through a tailored local-global Markov chain Monte Carlo (MCMC) algorithm, which exploits a data augmentation trick and MCMC updates from nonconjugate Bayesian nonparametric methods. We illustrate our model in simulations and on ICU data, comparing its performance with existing approaches.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141970575","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

Towards automated animal density estimation with acoustic spatial capture-recapture. 利用声学空间捕获-再捕获技术实现动物密度自动估算。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae081

Yuheng Wang, Juan Ye, Xiaohui Li, David L Borchers

{"title":"Towards automated animal density estimation with acoustic spatial capture-recapture.","authors":"Yuheng Wang, Juan Ye, Xiaohui Li, David L Borchers","doi":"10.1093/biomtc/ujae081","DOIUrl":"https://doi.org/10.1093/biomtc/ujae081","url":null,"abstract":"Passive acoustic monitoring can be an effective way of monitoring wildlife populations that are acoustically active but difficult to survey visually, but identifying target species calls in recordings is non-trivial. Machine learning (ML) techniques can do detection quickly but may miss calls and produce false positives, i.e., misidentify calls from other sources as being from the target species. While abundance estimation methods can address the former issue effectively, methods to deal with false positives are under-investigated. We propose an acoustic spatial capture-recapture (ASCR) method that deals with false positives by treating species identity as a latent variable. Individual-level outputs from ML techniques are treated as random variables whose distributions depend on the latent identity. This gives rise to a mixture model likelihood that we maximize to estimate call density. We compare our method to existing methods by applying it to an ASCR survey of frogs and simulated acoustic surveys of gibbons based on real gibbon acoustic data. Estimates from our method are closer to ASCR applied to the dataset without false positives than those from a widely used false positive \"correction factor\" method. Simulations show our method to have bias close to zero and accurate coverage probabilities and to perform substantially better than ASCR without accounting for false positives.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142079070","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

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae077

Niklas Hagemann, Giampiero Marra, Frank Bretz, Kathrin Möllenhoff

{"title":"Testing for similarity of multivariate mixed outcomes using generalized joint regression models with application to efficacy-toxicity responses.","authors":"Niklas Hagemann, Giampiero Marra, Frank Bretz, Kathrin Möllenhoff","doi":"10.1093/biomtc/ujae077","DOIUrl":"https://doi.org/10.1093/biomtc/ujae077","url":null,"abstract":"A common problem in clinical trials is to test whether the effect of an explanatory variable on a response of interest is similar between two groups, for example, patient or treatment groups. In this regard, similarity is defined as equivalence up to a pre-specified threshold that denotes an acceptable deviation between the two groups. This issue is typically tackled by assessing if the explanatory variable's effect on the response is similar. This assessment is based on, for example, confidence intervals of differences or a suitable distance between two parametric regression models. Typically, these approaches build on the assumption of a univariate continuous or binary outcome variable. However, multivariate outcomes, especially beyond the case of bivariate binary responses, remain underexplored. This paper introduces an approach based on a generalized joint regression framework exploiting the Gaussian copula. Compared to existing methods, our approach accommodates various outcome variable scales, such as continuous, binary, categorical, and ordinal, including mixed outcomes in multi-dimensional spaces. We demonstrate the validity of this approach through a simulation study and an efficacy-toxicity case study, hence highlighting its practical relevance.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142016283","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

Summary statistics knockoffs inference with family-wise error rate control. 利用族智误差率控制进行汇总统计山寨推理。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae082

Catherine Xinrui Yu, Jiaqi Gu, Zhaomeng Chen, Zihuai He

引用次数: 0

Semiparametric inference of effective reproduction number dynamics from wastewater pathogen surveillance data. 从废水病原体监测数据中推断有效繁殖数量动态的半参数。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae074

Isaac H Goldstein, Daniel M Parker, Sunny Jiang, Volodymyr M Minin

{"title":"Semiparametric inference of effective reproduction number dynamics from wastewater pathogen surveillance data.","authors":"Isaac H Goldstein, Daniel M Parker, Sunny Jiang, Volodymyr M Minin","doi":"10.1093/biomtc/ujae074","DOIUrl":"10.1093/biomtc/ujae074","url":null,"abstract":"Concentrations of pathogen genomes measured in wastewater have recently become available as a new data source to use when modeling the spread of infectious diseases. One promising use for this data source is inference of the effective reproduction number, the average number of individuals a newly infected person will infect. We propose a model where new infections arrive according to a time-varying immigration rate which can be interpreted as an average number of secondary infections produced by one infectious individual per unit time. This model allows us to estimate the effective reproduction number from concentrations of pathogen genomes, while avoiding difficulty to verify assumptions about the dynamics of the susceptible population. As a byproduct of our primary goal, we also produce a new model for estimating the effective reproduction number from case data using the same framework. We test this modeling framework in an agent-based simulation study with a realistic data generating mechanism which accounts for the time-varying dynamics of pathogen shedding. Finally, we apply our new model to estimating the effective reproduction number of SARS-CoV-2, the causative agent of COVID-19, in Los Angeles, CA, using pathogen RNA concentrations collected from a large wastewater treatment facility.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141896690","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

PathGPS: discover shared genetic architecture using GWAS summary data. PathGPS：利用 GWAS 摘要数据发现共享遗传结构。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae060

Zijun Gao, Qingyuan Zhao, Trevor Hastie

引用次数: 0

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