1,721,025 research outputs found
Bayesian Random-Effects Meta-Analysis Using the bayesmeta R Package
Full text linkThe random-effects or normal-normal hierarchical model is commonly utilized in a wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on few studies. The bayesmeta R package provides readily accessible tools to perform Bayesian meta-analyses and generate plots and summaries, without having to worry about computational details. It allows for flexible prior specification and instant access to the resulting posterior distributions, including prediction and shrinkage estimation, and facilitating for example quick sensitivity checks. The present paper introduces the underlying theory and showcases its usage
Using the bayesmeta R package for Bayesian random-effects meta-regression
Full text linkBACKGROUND: Random-effects meta-analysis within a hierarchical normal
modeling framework is commonly implemented in a wide range of evidence
synthesis applications. More general problems may even be tackled when
considering meta-regression approaches that in addition allow for the inclusion
of study-level covariables. METHODS: We describe the Bayesian meta-regression
implementation provided in the bayesmeta R package including the choice of
priors, and we illustrate its practical use. RESULTS: A wide range of example
applications are given, such as binary and continuous covariables, subgroup
analysis, indirect comparisons, and model selection. Example R code is
provided. CONCLUSIONS: The bayesmeta package provides a flexible
implementation. Due to the avoidance of MCMC methods, computations are fast and
reproducible, facilitating quick sensitivity checks or large-scale simulation
studies.Comment: 17 pages, 8 figure
Dynamically borrowing strength from another study through shrinkage estimation
No full textMeta-analytic methods may be used to combine evidence from different sources of information. Quite commonly, the normal–normal hierarchical model (NNHM) including a random-effect to account for between-study heterogeneity is utilized for such analyses. The same modeling framework may also be used to not only derive a combined estimate, but also to borrow strength for a particular study from another by deriving a shrinkage estimate. For instance, a small-scale randomized controlled trial could be supported by a non-randomized study, e.g. a clinical registry. This would be particularly attractive in the context of rare diseases. We demonstrate that a meta-analysis still makes sense in this extreme case, effectively based on a synthesis of only two studies, as illustrated using a recent trial and a clinical registry in Creutzfeld-Jakob disease. Derivation of a shrinkage estimate within a Bayesian random-effects meta-analysis may substantially improve a given estimate even based on only a single additional estimate while accounting for potential effect heterogeneity between the studies. Alternatively, inference may equivalently be motivated via a model specification that does not require a common overall mean parameter but considers the treatment effect in one study, and the difference in effects between the studies. The proposed approach is quite generally applicable to combine different types of evidence originating, e.g. from meta-analyses or individual studies. An application of this more general setup is provided in immunosuppression following liver transplantation in children.FP7 Health https://doi.org/10.13039/10001127
Investigating the heterogeneity of "study twins"
Full text linkMeta-analyses are commonly performed based on random-effects models, while in certain cases one might also argue in favour of a common-effect model. One such case may be given by the example of two "study twins" that are performed according to a common (or at least very similar) protocol. Here we investigate the particular case of meta-analysis of a pair of studies, e.g. summarizing the results of two confirmatory clinical trials in phase III of a clinical development programme. Thereby, we focus on the question of to what extent homogeneity or heterogeneity may be discernible, and include an empirical investigation of published ("twin") pairs of studies. A pair of estimates from two studies only provides very little evidence on homogeneity or heterogeneity of effects, and ad-hoc decision criteria may often be misleading
MetaStan: An R package for Bayesian (model-based) meta-analysis using Stan
No full textMeta-analysis methods are used to combine evidence from multiple studies.
Meta-regression as well as model-based meta-analysis are extensions of standard
pairwise meta-analysis in which information about study-level covariates and
(arm-level) dosing amount or exposure may be taken into account. A Bayesian
approach to inference is very attractive in this context, especially when a
meta-analysis is based on few studies only or rare events. In this article, we
present the R package MetaStan which implements a wide range of pairwise and
model-based meta-analysis models.
A generalised linear mixed model (GLMM) framework is used to describe the
pairwise meta-analysis, meta-regression and model-based meta-analysis models.
Within the GLMM framework, the likelihood and link functions are adapted to
reflect the nature of the data. For example, a binomial likelihood with a logit
link is used to perform a meta-analysis based on datasets with dichotomous
endpoints. Bayesian computations are conducted using Stan via the rstan
interface. Stan uses a Hamiltonian Monte Carlo sampler which belongs to the
family of Markov chain Monte Carlo methods. Stan implementations are done by
using suitable parametrizations to ease computations.
The user-friendly R package MetaStan, available on CRAN, supports a wide
range of pairwise and model-based meta-analysis models. MetaStan provides
fitting functions for pairwise meta-analysis with the option of including
covariates and model-based meta-analysis. The supported outcome types are
continuous, binary, and count. Forest plots for the pairwise meta-analysis and
dose-response plots for the model-based meta-analysis can be obtained from the
package. The use of MetaStan is demonstrated through clinical examples
Double arcsine transform not appropriate for meta-analysis
Full text linkThe variance-stabilizing Freeman-Tukey double arcsine transform was
originally proposed for inference on single proportions. Subsequently, its use
has been suggested in the context of meta-analysis of proportions. While some
erratic behaviour has been observed previously, here we point out and
illustrate general issues of monotonicity and invertibility that make this
transform unsuitable for meta-analysis purposes.Comment: 4 pages, 2 figures, 1 tabl
Contribution to the discussion of “When should meta‐analysis avoid making hidden normality assumptions?” A Bayesian perspective
No full textModel averaging for robust extrapolation in evidence synthesis
No full textExtrapolation from a source to a target, e.g., from adults to children, is a promising approach to utilizing external information when data are sparse. In the context of meta-analysis, one is commonly faced with a small number of studies, while potentially relevant additional information may also be available. Here we describe a simple extrapolation strategy using heavy-tailed mixture priors for effect estimation in meta-analysis, which effectively results in a model-averaging technique. The described method is robust in the sense that a potential prior-data conflict, i.e., a discrepancy between source and target data, is explicitly anticipated. The aim of this paper to develop a solution for this particular application, to showcase the ease of implementation by providing R code, and to demonstrate the robustness of the general approach in simulations
Bounds for the weight of external data in shrinkage estimation
No full textAbstract Shrinkage estimation in a meta‐analysis framework may be used to facilitate dynamical borrowing of information. This framework might be used to analyze a new study in the light of previous data, which might differ in their design (e.g., a randomized controlled trial and a clinical registry). We show how the common study weights arise in effect and shrinkage estimation, and how these may be generalized to the case of Bayesian meta‐analysis. Next we develop simple ways to compute bounds on the weights, so that the contribution of the external evidence may be assessed a priori. These considerations are illustrated and discussed using numerical examples, including applications in the treatment of Creutzfeldt–Jakob disease and in fetal monitoring to prevent the occurrence of metabolic acidosis. The target study's contribution to the resulting estimate is shown to be bounded below. Therefore, concerns of evidence being easily overwhelmed by external data are largely unwarranted.Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/50110000165
MetaStan: An R package for Bayesian (model-based) meta-analysis using Stan
No full textMeta-analysis methods are used to combine evidence from multiple studies.
Meta-regression as well as model-based meta-analysis are extensions of standard
pairwise meta-analysis in which information about study-level covariates and
(arm-level) dosing amount or exposure may be taken into account. A Bayesian
approach to inference is very attractive in this context, especially when a
meta-analysis is based on few studies only or rare events. In this article, we
present the R package MetaStan which implements a wide range of pairwise and
model-based meta-analysis models.
A generalised linear mixed model (GLMM) framework is used to describe the
pairwise meta-analysis, meta-regression and model-based meta-analysis models.
Within the GLMM framework, the likelihood and link functions are adapted to
reflect the nature of the data. For example, a binomial likelihood with a logit
link is used to perform a meta-analysis based on datasets with dichotomous
endpoints. Bayesian computations are conducted using Stan via the rstan
interface. Stan uses a Hamiltonian Monte Carlo sampler which belongs to the
family of Markov chain Monte Carlo methods. Stan implementations are done by
using suitable parametrizations to ease computations.
The user-friendly R package MetaStan, available on CRAN, supports a wide
range of pairwise and model-based meta-analysis models. MetaStan provides
fitting functions for pairwise meta-analysis with the option of including
covariates and model-based meta-analysis. The supported outcome types are
continuous, binary, and count. Forest plots for the pairwise meta-analysis and
dose-response plots for the model-based meta-analysis can be obtained from the
package. The use of MetaStan is demonstrated through clinical examples
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