Bayesian Methods for Meta-Analysis

2020 ◽  
pp. 91-128
Author(s):  
Christopher H. Schmid ◽  
Bradley P. Carlin ◽  
Nicky J. Welton
Keyword(s):  
Bayesian Methods ◽  
Meta Analysis

Bayesian Methods in Meta-Analysis

2012 ◽  
pp. 116-121
Author(s):  
Elizabeth Stojanovski ◽  
Kerrie Mengersen
Keyword(s):  
Bayesian Methods ◽  
Meta Analysis

scholarly journals Adopting a Meta-Generative Way of Thinking in the Field of Education via the Use of Bayesian Methods: A Multimethod Approach in a Post-Truth and COVID-19 Era1

2020 ◽  
pp. 4-19 ◽  
Author(s):  
Prathiba Natesan Batley ◽  
Peter Boedeker ◽  
Anthony J. Onwuegbuzie
Keyword(s):  
Bayesian Methods ◽  
Null Hypothesis ◽  
Quantitative Research ◽  
Meta Analysis ◽  
Research Synthesis ◽  
Research Data ◽  
Policy Implications ◽  
Significance Testing ◽  
Null Hypothesis Significance Testing ◽  
Multimethod Approach

In this editorial, we introduce the multimethod concept of thinking meta-generatively, which we define as directly integrating findings from the extant literature during the data collection, analysis, and interpretation phases of primary studies. We demonstrate that meta-generative thinking goes further than do other research synthesis techniques (e.g., meta-analysis) because it involves meta-synthesis not only across studies but also within studies—thereby representing a multimethod approach. We describe how meta-generative thinking can be maximized/optimized with respect to quantitative research data/findings via the use of Bayesian methodology that has been shown to be superior to the inherently flawed null hypothesis significance testing. We contend that Bayesian meta-generative thinking is essential, given the potential for divisiveness and far-reaching sociopolitical, educational, and health policy implications of findings that lack generativity in a post-truth and COVID-19 era.

scholarly journals Meta-analysis of Indonesian Octopus laqueus Kaneko & Kubodera 2005 (Cephalopoda: Octopodidae) using Mt-DNA COI as Genetic Marker

2021 ◽  
Vol 24 (1) ◽  
pp. 7-14
Author(s):  
Nenik Kholilah ◽  
Norma Afiati ◽  
Subagiyo Subagiyo ◽  
Retno Hartati
Keyword(s):  
Phylogenetic Tree ◽  
Genetic Distance ◽  
Dna Barcoding ◽  
Genetic Marker ◽  
Genetic Markers ◽  
Bayesian Methods ◽  
Genetic Difference ◽  
Meta Analysis ◽  
Pairwise Alignment ◽  
Mt Dna

O. laqueus was first discovered not long ago in 2005 in the Ryuku Islands, Japan. Its geographical distribution and molecular identification are therefore still rarely. Nucleotide sequences based on mt-DNA COI for O. laqueus that have been uploaded in the GenBank until before this study was carried out were only six sequences. Since DNA barcoding of mt-DNA COI has some advantageous characteristics, this study aimed to analyse the genetic difference of Indonesian O. laqueus to the data available in the GenBank. Samples were collected in 2019 - 2020 from Karimunjawa (n=16) and Bangka-Belitung (n=2). The mt-DNA COI was extracted using 10% chelex methods, PCR amplified using Folmer’s primer and sequenced in Sanger methods. Pairwise alignment and genetic distance were carried out in MEGA-X, whereas the phylogenetic tree was reconstructed using Bayesian methods. BLAST identification resulted in 685 bp with a range of 92,07-99,24  percentages of identity. The genetic mean pair-wise distances within-clade were 0,002 and 0,006, whilst the distance between the clade was 0.0883. Combining the suggestion with the ITF current, it is concluded that O. laqueus taken from Karimunjawa raised from the same species as those in Malaysia (MN711655) and Japan (AB302176). Specimens from Bangka-Belitung were suggested came from different species, as they were separated into the second clade by 8.83%. One single sample from Japan (AB430543) which laid outside the two clades by 11.63%-11.38% was also suggested to represent a different species. Overall, this study opens to various further studies on O. laqueus using other loci of genetic markers.

scholarly journals Bayesian Approaches for Confirmatory Trials in Rare Diseases: Opportunities and Challenges

2021 ◽  
Vol 18 (3) ◽  
pp. 1022
Author(s):  
Moreno Ursino ◽  
Nigel Stallard
Keyword(s):  
Rare Diseases ◽  
Bayesian Methods ◽  
Meta Analysis ◽  
Phase Iii ◽  
Design Stage ◽  
Bayesian Approaches ◽  
Phase Iii Trial ◽  
Available Information ◽  
Confirmatory Trials ◽  
Robust Evidence

The aim of this narrative review is to introduce the reader to Bayesian methods that, in our opinion, appear to be the most important in the context of rare diseases. A disease is defined as rare depending on the prevalence of the affected patients in the considered population, for example, about 1 in 1500 people in U.S.; about 1 in 2500 people in Japan; and fewer than 1 in 2000 people in Europe. There are between 6000 and 8000 rare diseases and the main issue in drug development is linked to the challenge of achieving robust evidence from clinical trials in small populations. A better use of all available information can help the development process and Bayesian statistics can provide a solid framework at the design stage, during the conduct of the trial, and at the analysis stage. The focus of this manuscript is to provide a review of Bayesian methods for sample size computation or reassessment during phase II or phase III trial, for response adaptive randomization and of for meta-analysis in rare disease. Challenges regarding prior distribution choice, computational burden and dissemination are also discussed.

scholarly journals The Bayesian New Statistics (Public)

2017 ◽  
Author(s):  
John K. Kruschke ◽  
Torrin Liddell
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Methods ◽  
Meta Analysis ◽  
Bayesian Approaches ◽  
Randomized Controlled ◽  
Credible Intervals ◽  
Conceptual Distinction ◽  
The One ◽  
New Statistics ◽  
Better Than

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty, on the other hand. Among frequentists in psychology a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming, 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Bayesian Methods in Meta-Analysis

2010 ◽  
pp. 116-121
Author(s):  
Elizabeth Stojanovski ◽  
Kerrie L. Mengersen
Keyword(s):  
Bayesian Methods ◽  
Meta Analysis

scholarly journals The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective

2016 ◽  
Author(s):  
John K. Kruschke ◽  
Torrin Liddell
Keyword(s):  
Hypothesis Testing ◽  
Bayesian Methods ◽  
Power Analysis ◽  
Meta Analysis ◽  
Bayesian Approaches ◽  
Credible Intervals ◽  
Conceptual Distinction ◽  
The One ◽  
New Statistics ◽  
Better Than

In the practice of data analysis, there is a conceptual distinction between hypothesis testing, on the one hand, and estimation with quantified uncertainty, on the other hand. Among frequentists in psychology a shift of emphasis from hypothesis testing to estimation has been dubbed "the New Statistics" (Cumming, 2014). A second conceptual distinction is between frequentist methods and Bayesian methods. Our main goal in this article is to explain how Bayesian methods achieve the goals of the New Statistics better than frequentist methods. The article reviews frequentist and Bayesian approaches to hypothesis testing and to estimation with confidence or credible intervals. The article also describes Bayesian approaches to meta-analysis, randomized controlled trials, and power analysis.

Sign in / Sign up

Export Citation Format

Share Document