Biostatistics and Epidemiology最新文献_第5页

A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data 预测新冠肺炎疫情演变的新回归模型：在意大利数据中的应用

Biostatistics and Epidemiology Pub Date : 2020-06-12 DOI: 10.1080/24709360.2021.1978270

D. Sisti, E. Rocchi, S. Peluso, S. Amatori, M. Carletti

{"title":"A new regression model for the forecasting of COVID-19 outbreak evolution: an application to Italian data","authors":"D. Sisti, E. Rocchi, S. Peluso, S. Amatori, M. Carletti","doi":"10.1080/24709360.2021.1978270","DOIUrl":"https://doi.org/10.1080/24709360.2021.1978270","url":null,"abstract":"The novel coronavirus SARS-CoV-2 was first identified in China in December 2019. In just over five months, the virus affected over 4 million people and caused about 300,000 deaths. This study aimed to model new COVID-19 cases in Italian regions using a new curve. A new empirical curve is proposed to model the number of new cases of COVID-19. It resembles a known exponential growth curve, which has a straight line as an exponent, but in the growth curve proposed, the exponent is a logistic curve multiplied for a straight line. This curve shows an initial phase, the expected exponential growth, then rises to the maximum value and finally reaches zero. We characterized the epidemic growth patterns for the entire Italian nation and each of the 20 Italian regions. The estimated growth curve has been used to calculate the expected time of the beginning, the time related to peak, and the end of the epidemics. Our analysis explores the development of the outbreaks in Italy and the impact of the containment measures. Data obtained are useful to forecast future scenarios and the possible end of the epidemic.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"6 1","pages":"48 - 56"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44476898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Regression with incomplete multivariate surrogate responses for a latent covariate 潜在协变量的不完全多元替代响应回归

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1794705

Hua Shen, R. Cook

{"title":"Regression with incomplete multivariate surrogate responses for a latent covariate","authors":"Hua Shen, R. Cook","doi":"10.1080/24709360.2020.1794705","DOIUrl":"https://doi.org/10.1080/24709360.2020.1794705","url":null,"abstract":"ABSTRACT We consider the setting in which a categorical exposure variable of interest can only be measured subject to misclassification via surrogate variables. These surrogate variables may represent the classification of an individual via imperfect diagnostic tests. In such settings, a random number of diagnostic tests may be ordered at the discretion of a treating physician with the decision to order further tests made in a sequential fashion based on the results of preliminary test results. Because the underlying latent status is not ascertainable these cheaper but imperfect surrogate test results are used in lieu of the definitive classification in a model for a long-term outcome. Naive use of a single surrogate or functions of the available surrogates can lead to biased estimators of the association and invalid inference. We propose a likelihood-based approach for modeling the effect of the latent variable in the absence of validation data with estimation based on an expectation–maximization (EM) algorithm. The method yields consistent and efficient estimates and is shown to out-perform several common alternative approaches. The performance of the proposed method is demonstrated in simulation studies and its utility is illustrated by applying the proposed method to the stimulating study on breast cancer.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"247 - 264"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2020.1794705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45075884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Challenges and strategies in analysis of missing data 缺失数据分析的挑战和策略

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2018.1469810

Xiao‐Hua Zhou

引用次数: 10

Weighted Lin and Xu test for two-stage randomization designs 两阶段随机设计的加权Lin和Xu检验

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1734391

S. Vilakati, G. Cortese

引用次数: 0

Clinical data quality: a data life cycle perspective. 临床数据质量:数据生命周期视角。

Biostatistics and Epidemiology Pub Date : 2020-01-01 Epub Date: 2019-02-23 DOI: 10.1080/24709360.2019.1572344

Chunhua Weng

引用次数: 7

Heterogeneous effects of factors on child nutritional status in Bangladesh using linear quantile mixed model 基于线性分位数混合模型的孟加拉国儿童营养状况因素的异质性影响

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1842048

J. R. Khan, Jahida Gulshan

{"title":"Heterogeneous effects of factors on child nutritional status in Bangladesh using linear quantile mixed model","authors":"J. R. Khan, Jahida Gulshan","doi":"10.1080/24709360.2020.1842048","DOIUrl":"https://doi.org/10.1080/24709360.2020.1842048","url":null,"abstract":"Earlier studies to assess the effects of risk factors on child nutritional status in Bangladesh have used conventional regression models that are inadequate to capture a complete scenario of effects. Therefore, this study aimed to evaluate the heterogeneous effects of factors at different points of conditional height-for-age Z-score (HAZ) distribution accounting for cluster-level variation using linear quantile mixed model (LQMM) and to compare them with a linear mixed model (LMM). In addition, an unconditional quantile model (UQM) was used to measure the effect of factors on the unconditional (marginal) HAZ distribution. A total of 6340 children aged 0–59 months extracted from the 2014 Bangladesh Demographic and Health Survey. Different factors – maternal characteristics (age, occupation, nutritional status, parity, birth interval), parental education, child age, breastfeeding status, and morbidity had significant heterogeneous effects on HAZ distribution. For example, secondary or higher educated parents had substantial differential impacts on the lower tail and upper tail of the child HAZ distribution, which was masked by LMM estimate. Moreover, significant cluster-level variations found across all quantiles of child HAZ. During intervention design, heterogeneous effects of factors and cluster variation ought to consider addressing the undernutrition problem in Bangladesh.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"265 - 281"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2020.1842048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43047827","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Intervention differential effects and regression to the mean in studies where sample selection is based on the initial value of the outcome variable: an evaluation of methods illustrated in weight-management studies 在样本选择基于结果变量初始值的研究中，干预差异效应和回归平均值：对体重管理研究中所示方法的评估

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1719690

Lucy Beggs, R. Briscoe, C. Griffiths, G. Ellison, M. Gilthorpe

{"title":"Intervention differential effects and regression to the mean in studies where sample selection is based on the initial value of the outcome variable: an evaluation of methods illustrated in weight-management studies","authors":"Lucy Beggs, R. Briscoe, C. Griffiths, G. Ellison, M. Gilthorpe","doi":"10.1080/24709360.2020.1719690","DOIUrl":"https://doi.org/10.1080/24709360.2020.1719690","url":null,"abstract":"Background: Intervention differential effects (IDEs) occur where changes in an outcome depend upon the initial values of that outcome. Although methods to identify IDEs are well documented, there remains a lack of understanding about the circumstances under which these methods are robust. One context that has not been explored is the identification of intervention differential effect in studies where sample selection is based on the initial value of the outcome being evaluated. We hypothesise that, in such settings, established methods for detecting IDEs will struggle to discriminate these from regression to the mean. Methods: Using simulated datasets of weight-loss intervention programmes that recruit according to initial body mass index, we explore the reliability of Oldham's method and multilevel modelling (MLM) to detect IDEs. Results: In datasets simulated with no IDE, Oldham's method and MLM yield Type I error rates >90%, confirming that threshold selection/truncation leads to bias due to regression to the mean. Type I error rates return close to 5% for both methods when a control group is introduced. Conclusions: Oldham's method and MLM can robustly detect IDEs in this setting, but only if analyses incorporate a control group for comparison.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"172 - 188"},"PeriodicalIF":0.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2020.1719690","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41500269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Application and extension of a likelihood-ratio test for seasonality in epidemiological data 流行病学资料中季节性似然比检验的应用和推广

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1721965

O. Marrero

引用次数: 1

Evaluating heterogeneity of treatment effects 评价治疗效果的异质性

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2020.1724003

S. Vijan

引用次数: 5

Applying statistical and analytical methods to U.S. Department of Veterans Affairs databases 将统计和分析方法应用于美国退伍军人事务部数据库

Biostatistics and Epidemiology Pub Date : 2020-01-01 DOI: 10.1080/24709360.2019.1708660

T. Kashner

引用次数: 0