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2017 ; 8
(3
): 290-302
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Power analysis for random-effects meta-analysis
#MMPMID28378395
Jackson D
; Turner R
Res Synth Methods
2017[Sep]; 8
(3
): 290-302
PMID28378395
show ga
One of the reasons for the popularity of meta-analysis is the notion that these
analyses will possess more power to detect effects than individual studies. This
is inevitably the case under a fixed-effect model. However, the inclusion of the
between-study variance in the random-effects model, and the need to estimate this
parameter, can have unfortunate implications for this power. We develop methods
for assessing the power of random-effects meta-analyses, and the average power of
the individual studies that contribute to meta-analyses, so that these powers can
be compared. In addition to deriving new analytical results and methods, we apply
our methods to 1991 meta-analyses taken from the Cochrane Database of Systematic
Reviews to retrospectively calculate their powers. We find that, in practice, 5
or more studies are needed to reasonably consistently achieve powers from
random-effects meta-analyses that are greater than the studies that contribute to
them. Not only is statistical inference under the random-effects model
challenging when there are very few studies but also less worthwhile in such
cases. The assumption that meta-analysis will result in an increase in power is
challenged by our findings.
free
free
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