Estimation in the generalized linear empirical bayes model using the extended quasi-likelihood

1994 ◽  
Vol 23 (3) ◽  
pp. 661-687 ◽  
Author(s):  
Wang-Shu Lu ◽  
Carl N. Morris
Keyword(s):  
Empirical Bayes ◽  
Bayes Model

scholarly journals Analyzing 2D gel images using a two-component empirical bayes model

2011 ◽  
Vol 12 (1) ◽  
Author(s):  
Feng Li ◽  
Françoise Seillier-Moiseiwitsch
Keyword(s):  
Empirical Bayes ◽  
Bayes Model ◽  
Two Component ◽  
2D Gel

Estimation in an empirical bayes model for longitudinal and cross-sectionally clustered binary data

2000 ◽  
Vol 28 (1) ◽  
pp. 45-63 ◽  
Author(s):  
Andreas I. Sashegyi ◽  
K. Stephen Brown ◽  
Patrick J. Farrell
Keyword(s):  
Binary Data ◽  
Empirical Bayes ◽  
Clustered Binary Data ◽  
Bayes Model

scholarly journals An empirical Bayes model for gene expression and methylation profiles in antiestrogen resistant breast cancer

2010 ◽  
Vol 3 (1) ◽  
Author(s):  
Jaesik Jeong ◽  
Lang Li ◽  
Yunlong Liu ◽  
Kenneth P Nephew ◽  
Tim Hui-Ming Huang ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Empirical Bayes ◽  
Bayes Model

Empirical Bayes Ranking Methods

1989 ◽  
Vol 14 (1) ◽  
pp. 29-46 ◽  
Author(s):  
Nan M. Laird ◽  
Thomas A. Louis
Keyword(s):  
Empirical Bayes ◽  
School Achievement ◽  
Gaussian Model ◽  
Measurement Scale ◽  
Ranking Methods ◽  
Common Value ◽  
Bayes Model ◽  
Ranking Problems ◽  
Using Data ◽  
Summary Of Performance

Ranking problems arise in setting priorities for investigations, in providing a simple summary of performance, in comparing objects in a manner robust to measurement scale, and in a wide variety of other applications. Commonly, rankings are computed from measurements that depend on the true attribute. Using the Gaussian model, we propose and compare methods for using these measurements to estimate the ranks of the underlying attributes and show that those based on an empirical Bayes model produce estimates that differ from ranking observed data. These differences result both from the effect of shrinking posterior means towards a common value by an amount that depends on the precision of individual measurements and from the Bayes processing of the posterior distribution to produce estimates that account for the uncertainty in the distribution of the ranks. We illustrate different ranking methods using data on school achievement reported by Aitkin and Longford (1986) . Mathematical and empirical results highlight the importance of using appropriate ranking methods and identify issues requiring further research.

A Bayes empirical-Bayes model for software reliability

1988 ◽  
Vol 37 (2) ◽  
pp. 248-254 ◽  
Author(s):  
T.A. Mazzuchi ◽  
R. Soyer
Keyword(s):  
Software Reliability ◽  
Empirical Bayes ◽  
Bayes Model

scholarly journals High Efficacy of OnabotulinumtoxinA Treatment in Patients With Comorbid Migraine and Depression: A Meta-analysis

2020 ◽  
Author(s):  
Oreste Affatato ◽  
Thiago C. Moulin ◽  
Claudia Pisanu ◽  
Victoria S. Babasieva ◽  
Marco Russo ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Symptoms ◽  
Depressive Disorder ◽  
Chronic Migraine ◽  
Empirical Bayes ◽  
Meta Analysis ◽  
Inclusion Criteria ◽  
Major Depressive ◽  
Bayes Model ◽  
Vas Scores

Abstract Background: Migraine and depression are highly prevalent and partly overlapping disorders that cause strong limitations in daily life. Patients tend to respond poorly to the therapies available for these diseases. OnabotulinumtoxinA has been proven to be an effective treatment for both migraine and depression. While many studies have addressed the effect of onabotulinumtoxinA in migraine or depression separately, a growing body of evidences suggest benecial effects also for patients comorbid with migraine and depression. The current meta-analysis systematically investigates to what extent onabotulinumtoxinA is efficient in migraineurs with depression.Methods: A systematic literature search was performed based on PubMed, Scopus and Web of Science from the earliest date till October 30th, 2020. Mean, standard deviation (SD) and sample size have been used to evaluate improvement in depressive symptoms and migraine using random-effects empirical Bayes model. Results: Our search retrieved 259 studies, eight of which met inclusion criteria. OnabotulinumtoxinA injections administered to patients with both chronic migraine and major depressive disorder led to mean reduction of -8.94 points (CI: [-10.04, -7.84], p < 0:01) in the BDI scale, of -5.90 points (CI: [-9.92, -1.88], p < 0:01) in the BDI-II scale and of -6.19 points (CI: [-9.52, -2.86], p < 0:01) in the PHQ-9 scale, when evaluating depressive symptoms. In the case of the migraine-related symptoms, we found mean reductions of -4.10 (CI: [-7.31, -0.89], p = 0.01) points in the HIT6 scale, -32.05 (CI: [-55.96, -8.14], p = 0.01) in the MIDAS scale, -1.7 (CI: [-3.27, -0.13], p = 0.03) points in the VAS scale and of -6.27 (CI: [-8.48, -4.07], p < 0:01) migraine episodes per month. Comorbid patients showed slightly better improvements in BDI, HIT6 scores and migraine frequency compared to monomorbid patients. The latter group manifested better results in MIDAS and VAS scores. Conclusion: Treatment with onabotulinumtoxinA leads to a signicant reduction of disease severity of both chronic migraine and major depressive disorder in patients comorbid with both diseases. Comparative analyses suggest an equivalent strong effect in monomorbid and comorbid patients, with benecial effects specically seen for certain migraine features.

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