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2014 ; 109
(508
): 1450-1465
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Exact meta-analysis approach for discrete data and its application to 2 × 2
tables with rare events
#MMPMID25620825
Liu D
; Liu RY
; Xie M
J Am Stat Assoc
2014[Oct]; 109
(508
): 1450-1465
PMID25620825
show ga
This paper proposes a general exact meta-analysis approach for synthesizing
inferences from multiple studies of discrete data. The approach combines the
p-value functions (also known as significance functions) associated with the
exact tests from individual studies. It encompasses a broad class of exact
meta-analysis methods, as it permits broad choices for the combining elements,
such as tests used in individual studies, and any parameter of interest. The
approach yields statements that explicitly account for the impact of individual
studies on the overall inference, in terms of efficiency/power and the type I
error rate. Those statements also give rises to empirical methods for further
enhancing the combined inference. Although the proposed approach is for general
discrete settings, for convenience, it is illustrated throughout using the
setting of meta-analysis of multiple 2 × 2 tables. In the context of rare events
data, such as observing few, zero or zero total (i.e., zero events in both arms)
outcomes in binomial trials or 2 × 2 tables, most existing meta-analysis methods
rely on the large-sample approximations which may yield invalid inference. The
commonly used corrections to zero outcomes in rare events data, aiming to improve
numerical performance can also incur undesirable consequences. The proposed
approach applies readily to any rare event setting, including even the zero total
event studies without any artificial correction. While debates continue on
whether or how zero total event studies should be incorporated in meta-analysis,
the proposed approach has the advantage of automatically including those studies
and thus making use of all available data. Through numerical studies in rare
events settings, the proposed exact approach is shown to be efficient and,
generally, outperform commonly used meta-analysis methods, including
Mental-Haenszel and Peto methods.