Practical guide to the meta-analysis of rare events
O Efthimiou - BMJ Ment Health, 2018 - mentalhealth.bmj.com
BMJ Ment Health, 2018•mentalhealth.bmj.com
Objective Meta-analysing studies with low event rates is challenging as some of the
standard methods for meta-analysis are not well suited to handle rare outcomes. This is
more evident when some studies have zero events in one or both treatment groups. In this
article, we discuss why rare events require special attention in meta-analysis, we present an
overview of some approaches suitable for meta-analysing rare events and we provide
practical recommendations for their use. Methods We go through several models suggested …
standard methods for meta-analysis are not well suited to handle rare outcomes. This is
more evident when some studies have zero events in one or both treatment groups. In this
article, we discuss why rare events require special attention in meta-analysis, we present an
overview of some approaches suitable for meta-analysing rare events and we provide
practical recommendations for their use. Methods We go through several models suggested …
Objective
Meta-analysing studies with low event rates is challenging as some of the standard methods for meta-analysis are not well suited to handle rare outcomes. This is more evident when some studies have zero events in one or both treatment groups. In this article, we discuss why rare events require special attention in meta-analysis, we present an overview of some approaches suitable for meta-analysing rare events and we provide practical recommendations for their use.
Methods
We go through several models suggested in the literature for performing a rare events meta-analysis, highlighting their respective advantages and limitations. We illustrate these models using a published example from mental health. We provide the software code needed to perform all analyses in the appendix.
Results
Different methods may give different results, and using a suboptimal approach may lead to erroneous conclusions. When data are very sparse, the choice between the available methods may have a large impact on the results. Methods that use the so-called continuity correction (eg, adding 0.5 to the number of events and non-events in studies with zero events in one treatment group) may lead to biased estimates.
Conclusions
Researchers should define the primary analysis a priori, in order to avoid selective reporting. A sensitivity analysis using a range of methods should be used to assess the robustness of results. Suboptimal methods such as using a continuity correction should be avoided.
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