What is a Bayesian meta-analysis?
What is a Bayesian meta-analysis?
In a Bayesian analysis, initial uncertainty is expressed through a prior distribution about the quantities of interest. In the context of a meta-analysis, the prior distribution will describe uncertainty regarding the particular effect measure being analysed, such as the odds ratio or the mean difference.
What is hierarchical meta-analysis?
Meta-analytic data have a natural hierarchical structure to them, where individuals are nested within studies, and have both within-and between-study variation to model.
Are hierarchical models Bayesian?
Bayesian hierarchical modelling is a statistical model written in multiple levels (hierarchical form) that estimates the parameters of the posterior distribution using the Bayesian method. Hierarchical modeling is used when information is available on several different levels of observational units.
What is a meta-analysis model?
In a random-effects meta-analysis model, the effect sizes in the studies that actually were performed are assumed to represent a random sample from a particular distribution of these effect sizes (hence the term random effects).
What is Bayesian modeling?
A Bayesian model is a statistical model where you use probability to represent all uncertainty within the model, both the uncertainty regarding the output but also the uncertainty regarding the input (aka parameters) to the model.
How do I run a network meta-analysis?
Table 1
- Define the review question and eligibility criteria. – Question should benefit from network meta- analysis.
- Search for and select studies.
- Abstract data and assess risk of bias.
- Synthesize evidence qualitatively.
- Synthesize evidence quantitatively.
- Interpret results and draw conclusions.
- Report findings.
What is DerSimonian and Laird method?
A variation on the inverse-variance method is to incorporate an assumption that the different studies are estimating different, yet related, intervention effects. This produces a random-effects meta-analysis, and the simplest version is known as the DerSimonian and Laird method (DerSimonian 1986).
How does Bayesian analysis work?
In Bayesian analysis, a parameter is summarized by an entire distribution of values instead of one fixed value as in classical frequentist analysis. Moreover, all statistical tests about model parameters can be expressed as probability statements based on the estimated posterior distribution.
What type of study is a meta-analysis?
Meta-analysis is a quantitative, formal, epidemiological study design used to systematically assess the results of previous research to derive conclusions about that body of research. Typically, but not necessarily, the study is based on randomized, controlled clinical trials.
Can Bayesian Meta-analysis be used for hierarchical modeling?
We’ll pick up from the previous section on hierarchical modeling with Bayesian meta-analysis, which lends itself naturally to a hierarchical formulation, with each study an “exchangeable” unit. Let’s first go through a quick illustration of a Bayesian meta-analysis.
Is there a Bayesian model for network meta-analysis of multiple diagnostic tests?
A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests Biostatistics. 2018 Jan 1;19(1):87-102.doi: 10.1093/biostatistics/kxx025. Authors Xiaoye Ma 1 , Qinshu Lian 1 , Haitao Chu 1 , Joseph G Ibrahim 2 , Yong Chen 3
Why use Bayesian research methods?
Bayesian methods allow to directly model the uncertainty in our estimate of τ 2 τ 2. They can also be superior in estimating pooled effects, particularly when the number of included studies is small (which is very often the case in practice).
What is the structure of a meta-analytic model?
In Chapter 10, we learned that every meta-analytic model comes with an inherent “multilevel”, and thus hierarchical, structure. On the first level, we have the individual participants. Data on this level usually reaches us in the form of calculated effect sizes ^θk θ ^ k of each study k k.
