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A spatiotemporal case-crossover model of asthma exacerbation in the City of Houston. 休斯顿市哮喘加重的时空病例交叉模型

IF 1.7

Stat (International Statistical Institute) Pub Date : 2021-12-01 Epub Date: 2021-05-06 DOI: 10.1002/sta4.357

Julia C Schedler, Katherine B Ensor

{"title":"A spatiotemporal case-crossover model of asthma exacerbation in the City of Houston.","authors":"Julia C Schedler, Katherine B Ensor","doi":"10.1002/sta4.357","DOIUrl":"https://doi.org/10.1002/sta4.357","url":null,"abstract":"Case-crossover design is a popular construction for analyzing the impact of a transient effect, such as ambient pollution levels, on an acute outcome, such as an asthma exacerbation. Case-crossover design avoids the need to model individual, time-varying risk factors for cases by using cases as their own 'controls', chosen to be time periods for which individual risk factors can be assumed constant and need not be modelled. Many studies have examined the complex effects of the control period structure on model performance, but these discussions were simplified when case-crossover design was shown to be equivalent to various specifications of Poisson regression when exposure is considered constant across study participants. While reasonable for some applications, there are cases where such an assumption does not apply due to spatial variability in exposure, which may affect parameter estimation. This work presents a spatiotemporal model, which has temporal case-crossover and a geometrically aware spatial random effect based on the Hausdorff distance. The model construction incorporates a residual spatial structure in cases when the constant assumption exposure is not reasonable and when spatial regions are irregular.","PeriodicalId":520780,"journal":{"name":"Stat (International Statistical Institute)","volume":" ","pages":"e357"},"PeriodicalIF":1.7,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/sta4.357","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40615359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Biclustering of medical monitoring data using a nonparametric hierarchical Bayesian model. 使用非参数层次贝叶斯模型的医疗监测数据的双聚类。

IF 1.7

Stat (International Statistical Institute) Pub Date : 2020-01-01 Epub Date: 2020-03-15 DOI: 10.1002/sta4.279

Yan Ren, Siva Sivaganesan, Mekibib Altaye, Raouf S Amin, Rhonda D Szczesniak

{"title":"Biclustering of medical monitoring data using a nonparametric hierarchical Bayesian model.","authors":"Yan Ren, Siva Sivaganesan, Mekibib Altaye, Raouf S Amin, Rhonda D Szczesniak","doi":"10.1002/sta4.279","DOIUrl":"https://doi.org/10.1002/sta4.279","url":null,"abstract":"In longitudinal studies in which a medical device is used to monitor outcome repeatedly and frequently on the same patients over a prespecified duration of time, two clustering goals can arise. One goal is to assess the degree of heterogeneity among patient profiles. A second yet equally important goal unique to such studies is to determine frequency and duration of monitoring sufficient to identify longitudinal changes. Considering these goals jointly would identify clusters of patients who share similar patterns over time and characterize temporal stability within each cluster. We use a biclustering approach, allowing simultaneous clustering of observations at both patient and time levels and using a nonparametric hierarchical Bayesian model. Because clustering units at the time level (i.e., time points) are ordered and hence unexchangeable, we utilize a multivariate Dirichlet process mixture model by specifying a Dirichlet process prior at the patient level whose base measure employs change points at the time level to achieve the desired joint clustering. We consider structured covariance between consecutive time points and assess model performance through simulation studies. We apply the model to data on 24-hr ambulatory blood pressure monitoring and examine the relationship between diastolic blood pressure and pediatric obstructive sleep apnoea.","PeriodicalId":520780,"journal":{"name":"Stat (International Statistical Institute)","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1002/sta4.279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40591543","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3