CONFIDENCE INTERVALS ON THE AMONG GROUP VARIANCE COMPONENT IN AN UNBALANCED AND HETEROSCEDASTIC ONE-WAY RANDOM EFFECTS MODEL

2002 ◽  
Vol 20 (1-4) ◽  
Author(s):  
Joachim Härtung ◽  
Doğan Argaç
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Variance Component ◽  
Random Effects Model

Estimators of Random Effects Variance Components in Meta-Analysis

2000 ◽  
Vol 25 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Lynn Friedman
Keyword(s):  
Closed Form ◽  
Random Effects ◽  
Quadratic Forms ◽  
Relative Efficiency ◽  
Variance Component ◽  
Linear Models ◽  
Random Effect ◽  
Linear Functions ◽  
Effect Sizes ◽  
Random Effects Model

In meta-analyses, groups of study effect sizes often do not fit the model of a single population with only sampling, or estimation, variance differentiating the estimates. If the effect sizes in a group of studies are not homogeneous, a random effects model should be calculated, and a variance component for the random effect estimated. This estimate can be made in several ways, but two closed form estimators are in common use. The comparative efficiency of the two is the focus of this report. We show here that these estimators vary in relative efficiency with the actual size of the random effects model variance component. The latter depends on the study effect sizes. The closed form estimators are linear functions of quadratic forms whose moments can be calculated according to a well-known theorem in linear models. We use this theorem to derive the variances of the estimators, and show that one of them is smaller when the random effects model variance is near zero; however, the variance of the other is smaller when the model variance is larger. This leads to conclusions about their relative efficiency.

scholarly journals On Jones et al.’s Method for Assessing Limits of Agreement with the Mean for Multiple Observers

2020 ◽  
Author(s):  
Heidi S. Christensen ◽  
Jens Borgbjerg ◽  
Lars Børty ◽  
Martin Bøgsted
Keyword(s):  
Confidence Intervals ◽  
Random Effects ◽  
Tumour Size ◽  
Measurement Methods ◽  
Data Sets ◽  
Random Effects Model ◽  
Limits Of Agreement ◽  
Continuous Measurements ◽  
Multiple Measurements ◽  
The Mean

Abstract Background To assess the agreement of continuous measurements between a number of observers, Jones et al. introduced limits of agreement with the mean (LOAM) for multiple observers, representing how much an individual observer can deviate from the mean measurement of all observers. Besides the graphical visualisation of LOAM, suggested by Jones et al., it is desirable to supply LOAM with confidence intervals and to extend the method to the case of multiple measurements per observer.Methods We reformulate LOAM under the assumption the measurements follow an additive two-way random effects model. Assuming this model, we provide estimates and confidence intervals for the proposed LOAM. Further, this approach is easily extended to the case of multiple measurements per observer.Results The proposed method is applied on two data sets to illustrate its use. Specifically, we consider agreement between measurements regarding tumour size and aortic diameter. For the latter study, three measurement methods are considered. Conclusions The proposed LOAM and the associated confidence intervals are useful for assessing agreement between continuous measurements.

scholarly journals Prevalence of Headache in Patients With Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-Analysis of 14,275 Patients

2020 ◽  
Vol 11 ◽  
Author(s):  
Md Asiful Islam ◽  
Sayeda Sadia Alam ◽  
Shoumik Kundu ◽  
Tareq Hossan ◽  
Mohammad Amjad Kamal ◽  
...  
Keyword(s):  
Systematic Review ◽  
Quality Assessment ◽  
Confidence Intervals ◽  
Random Effects ◽  
Critical Appraisal ◽  
Meta Analysis ◽  
Clinical Manifestations ◽  
Random Effects Model ◽  
Pooled Prevalence ◽  
Significant Difference

Background: Coronavirus disease 2019 (COVID-19) started to spread globally since December 2019 from Wuhan, China. Headache has been observed as one of the clinical manifestations in COVID-19 patients. We aimed to conduct a comprehensive systematic review and meta-analysis to estimate the overall pooled prevalence of headache in COVID-19 patients.Methods: PubMed, Scopus, ScienceDirect, and Google Scholar databases were searched to identify studies published between December 2019 and March 2020. Adult (≥18 years) COVID-19 patients were considered eligible. We used random-effects model to estimate the pooled prevalence with 95% confidence intervals (CIs). Quality assessment was done using the Joanna Briggs Institute critical appraisal tools. This study is registered with PROSPERO (CRD42020182529).Results: We identified 2,055 studies, of which 86 studies (n = 14,275, 49.4% female) were included in the meta-analysis. Overall, the pooled prevalence of headache in COVID-19 patients was 10.1% [95% CI: 8.76–11.49]. There was no significant difference of headache prevalence in severe or critical vs. non-severe (RR: 1.05, p = 0.78), survived (recovered or discharged) vs. non-survived (RR: 1.36, p = 0.23), and ICU vs. non-ICU (RR: 1.06, p = 0.87) COVID-19 patients. We detected 64.0, 34.9, and 1.1% of the included studies as high, moderate, and low quality, respectively.Conclusions: From the first 4-month data of the outbreak, headache was detected in 10.1% of the adult COVID-19 patients.

Sign in / Sign up

Export Citation Format

Share Document