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A re-evaluation of random-effects meta-analysis.

Higgins JP, Thompson SG, Spiegelhalter DJ - J R Stat Soc Ser A Stat Soc (2009)

Bottom Line: A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution.We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods.We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction.

View Article: PubMed Central - PubMed

Affiliation: Medical Research Council Biostatistics Unit Cambridge, UK.

ABSTRACT
Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.

No MeSH data available.


Related in: MedlinePlus

(a) Predictive distribution for θnew and (b) cumulative distribution function of the random-effects distribution (with 95% interval) estimated from a Bayesian normal random-effects meta-analysis of set shifting studies
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fig03: (a) Predictive distribution for θnew and (b) cumulative distribution function of the random-effects distribution (with 95% interval) estimated from a Bayesian normal random-effects meta-analysis of set shifting studies

Mentions: Probabilities for study effects may be obtained similarly by counting the proportion of MCMC iterations in which θnew<θ0. The proportion of positive (or negative) effect sizes provides an alternative to the test of qualitative interaction. Note that reading vertically from the cumulative distribution function in Fig. 3(b) in Section 7 directly provides estimates and 95% credibility intervals for P(θnew<θ0) for various values of θ0. Bayesian probabilities can address a variety of questions more readily than classical hypothesis tests.


A re-evaluation of random-effects meta-analysis.

Higgins JP, Thompson SG, Spiegelhalter DJ - J R Stat Soc Ser A Stat Soc (2009)

(a) Predictive distribution for θnew and (b) cumulative distribution function of the random-effects distribution (with 95% interval) estimated from a Bayesian normal random-effects meta-analysis of set shifting studies
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2667312&req=5

fig03: (a) Predictive distribution for θnew and (b) cumulative distribution function of the random-effects distribution (with 95% interval) estimated from a Bayesian normal random-effects meta-analysis of set shifting studies
Mentions: Probabilities for study effects may be obtained similarly by counting the proportion of MCMC iterations in which θnew<θ0. The proportion of positive (or negative) effect sizes provides an alternative to the test of qualitative interaction. Note that reading vertically from the cumulative distribution function in Fig. 3(b) in Section 7 directly provides estimates and 95% credibility intervals for P(θnew<θ0) for various values of θ0. Bayesian probabilities can address a variety of questions more readily than classical hypothesis tests.

Bottom Line: A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution.We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods.We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction.

View Article: PubMed Central - PubMed

Affiliation: Medical Research Council Biostatistics Unit Cambridge, UK.

ABSTRACT
Meta-analysis in the presence of unexplained heterogeneity is frequently undertaken by using a random-effects model, in which the effects underlying different studies are assumed to be drawn from a normal distribution. Here we discuss the justification and interpretation of such models, by addressing in turn the aims of estimation, prediction and hypothesis testing. A particular issue that we consider is the distinction between inference on the mean of the random-effects distribution and inference on the whole distribution. We suggest that random-effects meta-analyses as currently conducted often fail to provide the key results, and we investigate the extent to which distribution-free, classical and Bayesian approaches can provide satisfactory methods. We conclude that the Bayesian approach has the advantage of naturally allowing for full uncertainty, especially for prediction. However, it is not without problems, including computational intensity and sensitivity to a priori judgements. We propose a simple prediction interval for classical meta-analysis and offer extensions to standard practice of Bayesian meta-analysis, making use of an example of studies of 'set shifting' ability in people with eating disorders.

No MeSH data available.


Related in: MedlinePlus