Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping [0.03%]
基于贝叶斯疾病制图的多结果空间差异边界检测方法研究
Leiwen Gao,Sudipto Banerjee,Beate Ritz
Leiwen Gao
Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To...
Julia Fukuyama,Kris Sankaran,Laura Symul
Julia Fukuyama
Topic modeling is a popular method used to describe biological count data. With topic models, the user must specify the number of topics $K$. Since there is no definitive way to choose $K$ and since a true value might not exist, we develop ...
Estimation of sparse functional quantile regression with measurement error: a SIMEX approach [0.03%]
带有测量误差的稀疏函数分位数回归的估计：一种SIMEX方法
Carmen D Tekwe,Mengli Zhang,Raymond J Carroll et al.
Carmen D Tekwe et al.
Quantile regression is a semiparametric method for modeling associations between variables. It is most helpful when the covariates have complex relationships with the location, scale, and shape of the outcome distribution. Despite the metho...
A flexible parametric accelerated failure time model and the extension to time-dependent acceleration factors [0.03%]
一种灵活的参数加速失败时间模型及其对时间依赖性加速因子的扩展
Michael J Crowther,Patrick Royston,Mark Clements
Michael J Crowther
Accelerated failure time (AFT) models are used widely in medical research, though to a much lesser extent than proportional hazards models. In an AFT model, the effect of covariates act to accelerate or decelerate the time to event of inter...
A Bayesian MultiLayer Record Linkage Procedure to Analyze Post-Acute Care Recovery of Patients with Traumatic Brain Injury [0.03%]
一种贝叶斯多层记录链接程序 分析创伤性脑损伤患者的术后恢复状况
Mingyang Shan,Kali S Thomas,Roee Gutman
Mingyang Shan
Understanding associations between injury severity and postacute care recovery for patients with traumatic brain injury (TBI) is crucial to improving care. Estimating these associations requires information on patients' injury, demographics...
Automated splitting into batches for observational biomedical studies with sequential processing [0.03%]
序贯处理观察性生物医学研究的自动分批算法
Bram Burger,Marc Vaudel,Harald Barsnes
Bram Burger
Experimental design usually focuses on the setting where treatments and/or other aspects of interest can be manipulated. However, in observational biomedical studies with sequential processing, the set of available samples is often fixed, a...
Andrew C Titman,Hein Putter
Andrew C Titman
Multi-state models for event history analysis most commonly assume the process is Markov. This article considers tests of the Markov assumption that are applicable to general multi-state models. Two approaches using existing methodology are...
Semiparametric marginal regression for clustered competing risks data with missing cause of failure [0.03%]
具有缺失失败原因的聚类竞争风险数据的半参数边值回归分析方法研究
Wenxian Zhou,Giorgos Bakoyannis,Ying Zhang et al.
Wenxian Zhou et al.
Clustered competing risks data are commonly encountered in multicenter studies. The analysis of such data is often complicated due to informative cluster size (ICS), a situation where the outcomes under study are associated with the size of...
Curtis Tatsuoka,Weicong Chen,Xiaoyi Lu
Curtis Tatsuoka
A Bayesian framework for group testing under dilution effects has been developed, using lattice-based models. This work has particular relevance given the pressing public health need to enhance testing capacity for coronavirus disease 2019 ...
Extending prediction models for use in a new target population with failure time outcomes [0.03%]
预测模型在新的目标人群中的应用与失效时间结局的关系研究
Jon A Steingrimsson
Jon A Steingrimsson
Prediction models are often built and evaluated using data from a population that differs from the target population where model-derived predictions are intended to be used in. In this article, we present methods for evaluating model perfor...