Biostatistics and Epidemiology最新文献_第4页

Case fatality risk estimated from routinely collected disease surveillance data: application to COVID–19 根据常规收集的疾病监测数据估计的病死率风险:在COVID-19中的应用

Biostatistics and Epidemiology Pub Date : 2021-01-02 DOI: 10.1080/24709360.2021.1913708

I. Marschner

{"title":"Case fatality risk estimated from routinely collected disease surveillance data: application to COVID–19","authors":"I. Marschner","doi":"10.1080/24709360.2021.1913708","DOIUrl":"https://doi.org/10.1080/24709360.2021.1913708","url":null,"abstract":"Case fatality risk (CFR) is the probability of death among cases of a disease. A crude CFR estimate is the ratio of the number deaths to the number of cases of the disease. This estimate is biased, however, particularly during outbreaks of emerging infectious diseases such as COVID-19, because the death time of recent cases is subject to right censoring. Instead, we propose deconvolution methods applied to routinely collected surveillance data of unlinked case and death counts over time. We begin by considering the death series to be the convolution of the case series and the fatality distribution, which is the subdistribution of the time between diagnosis and death. We then use deconvolution methods to estimate this fatality distribution. This provides a CFR estimate together with information about the distribution of time to death. Importantly, this information is extracted without the need to make strong assumptions used in previous analyses. The methods are applied to COVID-19 surveillance data from a range of countries illustrating substantial CFR differences. Simulations show that crude approaches lead to underestimation, particularly early in an outbreak, and that the proposed approach can rectify this bias. An R package called covidSurv is available for implementing the analyses.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"5 1","pages":"49 - 68"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2021.1913708","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60127878","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: 3

Methods for detecting outlying regions and influence diagnosis in multi-regional clinical trials 多区域临床试验中边缘区域的检测方法及影响诊断

Biostatistics and Epidemiology Pub Date : 2021-01-02 DOI: 10.1080/24709360.2021.1921944

M. Aoki, H. Noma, M. Gosho

{"title":"Methods for detecting outlying regions and influence diagnosis in multi-regional clinical trials","authors":"M. Aoki, H. Noma, M. Gosho","doi":"10.1080/24709360.2021.1921944","DOIUrl":"https://doi.org/10.1080/24709360.2021.1921944","url":null,"abstract":"Due to the globalization of drug development, multi-regional clinical trials (MRCTs) have been increasingly adopted in clinical evaluations. In MRCTs, the primary objective is to demonstrate the efficacy of new drugs in all participating regions, but heterogeneity of various relevant factors across these regions can cause inconsistency of treatment effects. In particular, outlying regions with extreme profiles can influence the overall conclusions of these studies. In this article, we propose quantitative methods to detect these outlying regions and to assess their influences in MRCTs. The approaches are as follows: (1) a method using a dfbeta-like measure, a studentized residual obtained by a leave-one-out cross-validation (LOOCV) scheme; (2) a model-based significance testing method using a mean-shifted model; (3) a method using a relative change measure for the variance estimate of the overall effect estimator; and (4) a method using a relative change measure for the heterogeneity variance estimate in a random-effects model. Parametric bootstrap schemes are proposed to accurately assess the statistical significance and variabilities of the aforementioned influence diagnostic tools. We illustrate the effectiveness of these proposed methods via applications to two MRCTs, the RECORD and RENAAL studies.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"5 1","pages":"30 - 48"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2021.1921944","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45719376","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

Region as a risk factor for asthma prevalence: statistical evidence from administrative data 地区作为哮喘患病率的危险因素：来自行政数据的统计证据

Biostatistics and Epidemiology Pub Date : 2021-01-02 DOI: 10.1080/24709360.2021.1924495

R. Wesonga, Khidir M. Abdelbasit

{"title":"Region as a risk factor for asthma prevalence: statistical evidence from administrative data","authors":"R. Wesonga, Khidir M. Abdelbasit","doi":"10.1080/24709360.2021.1924495","DOIUrl":"https://doi.org/10.1080/24709360.2021.1924495","url":null,"abstract":"Geographical regions may have an influence on asthma exacerbation. No conclusive study has been conducted to fully support or dissipate this assertion. We sought to use a data-driven approach to investigate the possible effect of geographical location on asthma. This study was based on data collected by the Ministry of Health over a 6-year period from 2010 to 2015 and presented in their annual reports. Prevalence rates for 11 regions were computed using the analysis of variance and regression models to determine the proximal nature of the region as a risk factor for asthma. Our results show a statistically significant difference in prevalence rates of asthma among the 11 regions. The asthma prevalence rate among the male population was 18% (OR = 1.18, p = .011) more than for the female population. There was a positive marginal increase in the asthma prevalence over the period. Further, five groups were derived based on asthma prevalence rates and trends. The region has proximal risk factor and significantly associated with asthma prevalence over the period. We recommend the creation of a control mechanism that targets regions with higher prevalence and increasing trends.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"5 1","pages":"19 - 29"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2021.1924495","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42433147","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

Comparative analysis of epidemiological models for COVID-19 pandemic predictions COVID-19大流行预测流行病学模型的比较分析

Biostatistics and Epidemiology Pub Date : 2021-01-02 DOI: 10.1080/24709360.2021.1913709

Rajan Gupta, G. Pandey, S. Pal

{"title":"Comparative analysis of epidemiological models for COVID-19 pandemic predictions","authors":"Rajan Gupta, G. Pandey, S. Pal","doi":"10.1080/24709360.2021.1913709","DOIUrl":"https://doi.org/10.1080/24709360.2021.1913709","url":null,"abstract":"Epidemiological modeling is an important problem around the world. This research presents COVID-19 analysis to understand which model works better for different regions. A comparative analysis of three growth curve fitting models (Gompertz, Logistic, and Exponential), two mathematical models (SEIR and IDEA), two forecasting models (Holt's exponential and ARIMA), and four machine/deep learning models (Neural Network, LSTM Networks, GANs, and Random Forest) using three evaluation criteria on ten prominent regions around the world from North America, South America, Europe, and Asia has been presented. The minimum and median values for RMSE were 1.8 and 5372.9; the values for the mean absolute percentage error were 0.005 and 6.63; and the values for AIC were 87.07 and 613.3, respectively, from a total of 125 experiments across 10 regions. The growth curve fitting models worked well where flattening of the cases has started. Based on region's growth curve, a relevant model from the list can be used for predicting the number of infected cases for COVID-19. Some other models used in forecasting the number of cases have been added in the future work section, which can help researchers to forecast the number of cases in different regions of the world.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"5 1","pages":"69 - 91"},"PeriodicalIF":0.0,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2021.1913709","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48622498","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: 8

Estimating the AUC with a Graphical Lasso Method for High-dimensional Biomarkers with LOD. 用带LOD的高维生物标志物的图形套索法估计AUC

Biostatistics and Epidemiology Pub Date : 2021-01-01 Epub Date: 2021-03-17 DOI: 10.1080/24709360.2021.1898731

Jirui Wang, Yunpeng Zhao, Liansheng Larry Tang

引用次数: 0

A note on modeling placement values in the analysis of receiver operating characteristic curves. 对接收机工作特性曲线分析中建模放置值的说明。

Biostatistics and Epidemiology Pub Date : 2021-01-01 Epub Date: 2020-03-22 DOI: 10.1080/24709360.2020.1737794

Zhen Chen, Soutik Ghosal

引用次数: 0

Concordance Measures and Time-Dependent ROC Methods. 一致性测量和时间相关 ROC 方法。

Biostatistics and Epidemiology Pub Date : 2021-01-01 Epub Date: 2021-05-25 DOI: 10.1080/24709360.2021.1926189

Norberto Pantoja-Galicia, Olivia I Okereke, Deborah Blacker, Rebecca A Betensky

{"title":"Concordance Measures and Time-Dependent ROC Methods.","authors":"Norberto Pantoja-Galicia, Olivia I Okereke, Deborah Blacker, Rebecca A Betensky","doi":"10.1080/24709360.2021.1926189","DOIUrl":"10.1080/24709360.2021.1926189","url":null,"abstract":"The receiver operating characteristic (ROC) curve displays sensitivity versus 1-specificity over a set of thresholds. The area under the ROC curve (AUC) is a global scalar summary of this curve. In the context of time-dependent ROC methods, we are interested in global scalar measures that summarize sequences of time-dependent AUCs over time. The concordance probability is a candidate for such purposes. The concordance probability can provide a global assessment of the discrimination ability of a test for an event that occurs at random times and may be right censored. If the test adequately differentiates between subjects who survive longer times and those who survive shorter times, this will assist clinical decisions. In this context the concordance probability may support assessment of precision medicine tools based on prognostic biomarkers models for overall survival. Definitions of time-dependent sensitivity and specificity are reviewed. Some connections between such definitions and concordance measures are also reviewed and we establish new connections via new measures of global concordance. We explore the relationship between such measures and their corresponding time-dependent AUC. To illustrate these concepts, an application in the context of Alzheimer's disease is presented.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":" ","pages":"232-249"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9523576/pdf/nihms-1701256.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40389965","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: 0

Moving from two- to multi-way interactions among binary risk factors on the additive scale. 在可加性尺度上，二元危险因素之间从双向相互作用转向双向相互作用。

Biostatistics and Epidemiology Pub Date : 2020-12-03 DOI: 10.1080/24709360.2020.1850171

Michail Katsoulis, Manuel Gomes, Christina Bamia

{"title":"Moving from two- to multi-way interactions among binary risk factors on the additive scale.","authors":"Michail Katsoulis, Manuel Gomes, Christina Bamia","doi":"10.1080/24709360.2020.1850171","DOIUrl":"10.1080/24709360.2020.1850171","url":null,"abstract":"Many studies have focused on investigating deviations from additive interaction of two dichotomous risk factors on a binary outcome. There is, however, a gap in the literature with respect to interactions on the additive scale of >2 risk factors. In this paper, we present an approach for examining deviations from additive interaction among three or more binary exposures. The relative excess risk due to interaction (RERI) is used as measure of additive interaction. First, we concentrate on three risk factors - we propose to decompose the total RERI to: the RERI owned to the joint presence of all three risk factors and the RERI of any two risk factors, given that the third is absent. We then extend this approach, to >3 binary risk factors. For illustration, we use a sample from data from the Greek EPIC cohort and we investigate the association with overall mortality of Mediterranean diet, body mass index , and smoking. Our formulae enable better interpretability of any evidence for deviations from additivity owned to more than two risk factors and provide simple ways of communicating such results from a public health perspective by attributing any excess relative risk to specific combinations of these factors. Abbreviations: BMI: Body Mass Index; ERR: excess relative risk; EPIC: European Prospective Investigation into Cancer and nutrition; MD: Mediterranean diet; RERI: relative excess risk due to interaction; RR: relative risk; TotRERI: total relative excess risk due to interaction.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"4 1","pages":"282-293"},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8098792/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38930293","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: 0

Number needed to test: quantifying risk stratification provided by diagnostic tests and risk predictions 需要测试的数量:通过诊断测试和风险预测提供的量化风险分层

Biostatistics and Epidemiology Pub Date : 2020-08-07 DOI: 10.1080/24709360.2020.1796176

H. Katki, R. Dey, P. Saha-Chaudhuri

{"title":"Number needed to test: quantifying risk stratification provided by diagnostic tests and risk predictions","authors":"H. Katki, R. Dey, P. Saha-Chaudhuri","doi":"10.1080/24709360.2020.1796176","DOIUrl":"https://doi.org/10.1080/24709360.2020.1796176","url":null,"abstract":"Risk stratification is the ability of a test or model to separate those at high vs. low risk of disease. There is no risk stratification metric that is in terms of the number of people requiring testing, which would help with considering the benefits, harms, and costs associated with the test and interventions. We introduce the expected number needed to test (NNtest) to identify one more disease case than by randomly selecting people for disease ascertainment. We show that NNtest measures risk stratification, allowing us to decompose NNtest into components that contrast the increase in risk upon testing positive (‘concern’) versus the decrease in risk upon testing negative (‘reassurance’). A graph of the reciprocals of concern vs. reassurance have linear contours of constant NNtest, visualizing the relative importance and tradeoff of each component to better understand the properties of risk thresholds with equal NNtest. We apply NNtest to the controversy over the risk threshold for who should get testing for BRCA1/2 mutations that cause high risks of breast and ovarian cancers. We show that risk thresholds between 0.78% and 5% optimize NNtest. At these thresholds, people will require risk-model evaluation to find one more mutation-carrier. However, these thresholds of equal NNtest provide very different concern and reassurance, with 0.78% providing much more reassurance (and thus much less concern) than 5%. Given that genetic testing costs are declining rapidly, the greater reassurance provided by the 0.78% threshold might be deemed more important than the greater concern provided by the 5% threshold.","PeriodicalId":37240,"journal":{"name":"Biostatistics and Epidemiology","volume":"5 1","pages":"134 - 148"},"PeriodicalIF":0.0,"publicationDate":"2020-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/24709360.2020.1796176","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42483253","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

Contrast-specific propensity scores 对比特定倾向得分

Biostatistics and Epidemiology Pub Date : 2020-07-02 DOI: 10.1080/24709360.2021.1936421

Shasha Han, D. Rubin

引用次数: 4