Biometrics最新文献_第6页

Multiply robust estimation of marginal structural models in observational studies subject to covariate-driven observations. 在观测研究中，对受协变因素驱动的观测结果进行边际结构模型的多重稳健估计。

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

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

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":"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":"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

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":"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":"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

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":"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":"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

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":"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":"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

Adjusting for incomplete baseline covariates in randomized controlled trials: a cross-world imputation framework. 调整随机对照试验中不完整的基线协变量：跨世界估算框架。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae094

Yilin Song, James P Hughes, Ting Ye

{"title":"Adjusting for incomplete baseline covariates in randomized controlled trials: a cross-world imputation framework.","authors":"Yilin Song, James P Hughes, Ting Ye","doi":"10.1093/biomtc/ujae094","DOIUrl":"https://doi.org/10.1093/biomtc/ujae094","url":null,"abstract":"In randomized controlled trials, adjusting for baseline covariates is commonly used to improve the precision of treatment effect estimation. However, covariates often have missing values. Recently, Zhao and Ding studied two simple strategies, the single imputation method and missingness-indicator method (MIM), to handle missing covariates and showed that both methods can provide an efficiency gain compared to not adjusting for covariates. To better understand and compare these two strategies, we propose and investigate a novel theoretical imputation framework termed cross-world imputation (CWI). This framework includes both single imputation and MIM as special cases, facilitating the comparison of their efficiency. Through the lens of CWI, we show that MIM implicitly searches for the optimal CWI values and thus achieves optimal efficiency. We also derive conditions under which the single imputation method, by searching for the optimal single imputation values, can achieve the same efficiency as the MIM. We illustrate our findings through simulation studies and a real data analysis based on the Childhood Adenotonsillectomy Trial. We conclude by discussing the practical implications of our findings.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11398886/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142280079","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

Causal inference using multivariate generalized linear mixed-effects models. 利用多变量广义线性混合效应模型进行因果推断。

IF 1.4 4区数学

Biometrics Pub Date : 2024-07-01 DOI: 10.1093/biomtc/ujae100

Yizhen Xu, Ji Soo Kim, Laura K Hummers, Ami A Shah, Scott L Zeger

{"title":"Causal inference using multivariate generalized linear mixed-effects models.","authors":"Yizhen Xu, Ji Soo Kim, Laura K Hummers, Ami A Shah, Scott L Zeger","doi":"10.1093/biomtc/ujae100","DOIUrl":"https://doi.org/10.1093/biomtc/ujae100","url":null,"abstract":"Dynamic prediction of causal effects under different treatment regimens is an essential problem in precision medicine. It is challenging because the actual mechanisms of treatment assignment and effects are unknown in observational studies. We propose a multivariate generalized linear mixed-effects model and a Bayesian g-computation algorithm to calculate the posterior distribution of subgroup-specific intervention benefits of dynamic treatment regimes. Unmeasured time-invariant factors are included as subject-specific random effects in the assumed joint distribution of outcomes, time-varying confounders, and treatment assignments. We identify a sequential ignorability assumption conditional on treatment assignment heterogeneity, that is, analogous to balancing the latent treatment preference due to unmeasured time-invariant factors. We present a simulation study to assess the proposed method's performance. The method is applied to observational clinical data to investigate the efficacy of continuously using mycophenolate in different subgroups of scleroderma patients.","PeriodicalId":8930,"journal":{"name":"Biometrics","volume":"80 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11422711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142340678","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