Cook, R. D., S. Weisberg: Residuals and influence in regression. Chapman and Hall, New York — London 1982. VIII, 229 pp., £ 12,‐

Läuter, H.

doi:10.1002/bimj.4710270110

Cited by 12 publications

(5 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The validity of the assumptions of the normal linear model was verified by diagnostic plot inspection, Shapiro–Wilk test of normality of residuals, and Koenker’s studentized version of Breusch–Pagan test of homoscedasticity. Regression influential diagnostics 8 was conducted to evaluate sensitivity of the results on leverage and outlying observations. The final model was used to calculate cross-validated (leave-one-out) predicted mortalities including the 95% prediction intervals, which were then compared with the observed mortalities.…”

Section: Methodsmentioning

confidence: 99%

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Hradský

Komárek

2021

European Journal of Public Health

View full text Add to dashboard Cite

Background The numbers of coronavirus disease 2019 (COVID-19) deaths per million people differ widely across countries. Often, the causal effects of interventions taken by authorities are unjustifiably concluded based on the comparison of pure mortalities in countries where interventions consisting different strategies have been taken. Moreover, the possible effects of other factors are only rarely considered. Methods We used data from open databases (European Centre for Disease Prevention and Control, World Bank Open Data, The BCG World Atlas) and publications to develop a model that could largely explain the differences in cumulative mortality between countries using non-interventional (mostly socio-demographic) factors. Results Statistically significant associations with the logarithmic COVID-19 mortality were found with the following: proportion of people aged 80 years and above, population density, proportion of urban population, gross domestic product, number of hospital beds per population, average temperature in March and incidence of tuberculosis. The final model could explain 67% of the variability. This finding could also be interpreted as follows: less than a third of the variability in logarithmic mortality differences could be modified by diverse non-pharmaceutical interventions ranging from case isolation to comprehensive measures, constituting case isolation, social distancing of the entire population and closure of schools and borders. Conclusions In particular countries, the number of people who will die from COVID-19 is largely given by factors that cannot be drastically changed as an immediate reaction to the pandemic and authorities should focus on modifiable variables, e.g. the number of hospital beds.

show abstract

Section: Methodsmentioning

confidence: 99%

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Hradský

Komárek

2021

European Journal of Public Health

View full text Add to dashboard Cite

show abstract

“…Analysis of Cook’s distance ( Figure 5 ) marked 8 data points ( 37 , 38 , 43 – 48 ) as the most influential based on the 4/n cut-off of Cook’s distance ( 49 ), with the most prominent estimate, obtained in the study of Kim et al ( 47 ). Six data points represent the largest estimates from the original data set, five of them were extracted from USA studies, based on records review surveillance design, that, as was mentioned above, is associated with the largest prevalence estimates compared with other methodologies.…”

Section: Resultsmentioning

confidence: 89%

The global prevalence of autism spectrum disorder: A three-level meta-analysis

et al. 2023

View full text Add to dashboard Cite

Autism spectrum disorder (ASD) is one the most disabling developmental disorders, imposing an extremely high economic burden. Obtaining as accurate prevalence estimates as possible is crucial to guide governments in planning policies for identification and intervention for individuals with ASD and their relatives. The precision of prevalence estimates can be heightened by summative analyses of the data collected around the world. To that end, we conducted a three-level mixed-effects meta-analysis. A systematic search of the Web of Science, PubMed, EMBASE, and PsycINFO databases from 2000 up to 13 July 2020 was performed, and reference lists of previous reviews and existing databases of prevalence studies were screened. Overall, 79 studies were included in the analysis of ASD and 59–in the analysis of previously existing relevant diagnoses: 30 for Autistic Disorder (AD), 15 for Asperger Syndrome (AS), and 14 for Atypical Autism (AA) and Pervasive Developmental Disorder - Not Otherwise Specified (PDD-NOS); these research reports covered the period from 1994 to 2019. Pooled prevalence estimates were 0.72% (95% CI = 0.61–0.85) for ASD, 0.25% (95% CI = 0.18–0.33) for AD, 0.13% (95% CI = 0.07–0.20) for AS, and 0.18% (95% CI = 0.10–0.28) for the combined group of AA and PDD-NOS. Estimates were higher (1) for the studies that used records-review surveillance rather than other designs; (2) in North America compared with other geographical regions; and (3) in high-income compared with lower-income countries. The highest prevalence estimates were registered in the USA. There was an increase in autism prevalence estimates over time. The prevalence was also significantly higher for children aged between 6 and 12 years compared to children under the age of 5 and over the age of 13 years.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019131525, identifier CRD42019131525.

show abstract

“…Moreover, we calculated the Cook's distance for each data point to check if bivariate outliers were present (Cook, 1977). Data points with a Cook's distance < 1 were considered as bivariate outliers (Cook & Weisberg, 1982). All fNIRS preprocessing steps were done using Matlab ® R2014a (MathWorks, Natick, MA), and all statistical analyses were performed using SPSS 22.0 software (IBM Corporation, Armonk, NY, USA).…”

Section: Discussionmentioning

confidence: 99%

Negative mood is associated with decreased prefrontal cortex functioning during working memory in young adults

2021

View full text Add to dashboard Cite

According to the prefrontal-subcortical model of emotion regulation, the prefrontal cortex (PFC) regulates the limbic system, especially the amygdala, and puts it under control in face of negative stress (Davidson, 2002;Ochsner & Gross, 2005;Ochsner et al., 2012). When the PFC is dysfunctional, the amygdala may become out of control (e.g., overactive), leading to the emergence of affective disorders. Over the past few decades, this neural model, proposed to explain affective disorders, has received abundant empirical support (Dougherty & Rauch, 1997;Drevets, 2001;Karl et al., 2006). For example, some single-photon emission computed tomography (SPECT) and positron emission tomography (PET) studies have reported glucose hypometabolism in the frontal lobes of patients with mood disorders and a relationship between the severity of depression and frontal hypometabolism (

show abstract

Cook, R. D., S. Weisberg: Residuals and influence in regression. Chapman and Hall, New York — London 1982. VIII, 229 pp., £ 12,‐

Cited by 12 publications

References 0 publications

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

Demographic and public health characteristics explain large part of variability in COVID-19 mortality across countries

The global prevalence of autism spectrum disorder: A three-level meta-analysis

Negative mood is associated with decreased prefrontal cortex functioning during working memory in young adults

Contact Info

Product

Resources

About