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.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 Biometrika
2015 ; 102
(2
): 281-294
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On random-effects meta-analysis
#MMPMID26688589
Zeng D
; Lin DY
Biometrika
2015[Jun]; 102
(2
): 281-294
PMID26688589
show ga
Meta-analysis is widely used to compare and combine the results of multiple
independent studies. To account for between-study heterogeneity, investigators
often employ random-effects models, under which the effect sizes of interest are
assumed to follow a normal distribution. It is common to estimate the mean effect
size by a weighted linear combination of study-specific estimators, with the
weight for each study being inversely proportional to the sum of the variance of
the effect-size estimator and the estimated variance component of the
random-effects distribution. Because the estimator of the variance component
involved in the weights is random and correlated with study-specific effect-size
estimators, the commonly adopted asymptotic normal approximation to the
meta-analysis estimator is grossly inaccurate unless the number of studies is
large. When individual participant data are available, one can also estimate the
mean effect size by maximizing the joint likelihood. We establish the asymptotic
properties of the meta-analysis estimator and the joint maximum likelihood
estimator when the number of studies is either fixed or increases at a slower
rate than the study sizes and we discover a surprising result: the former
estimator is always at least as efficient as the latter. We also develop a novel
resampling technique that improves the accuracy of statistical inference. We
demonstrate the benefits of the proposed inference procedures using simulated and
empirical data.
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