rema - Rare Event Meta Analysis
The rema package implements a permutation-based approach
for binary meta-analyses of 2x2 tables, founded on conditional
logistic regression, that provides more reliable statistical
tests when heterogeneity is observed in rare event data
(Zabriskie et al. 2021 <doi:10.1002/sim.9142>). To adjust for
the effect of heterogeneity, this method conditions on the
sufficient statistic of a proxy for the heterogeneity effect as
opposed to estimating the heterogeneity variance. While this
results in the model not strictly falling under the
random-effects framework, it is akin to a random-effects
approach in that it assumes differences in variability due to
treatment. Further, this method does not rely on large-sample
approximations or continuity corrections for rare event data.
This method uses the permutational distribution of the test
statistic instead of asymptotic approximations for inference.
The number of observed events drives the computation complexity
for creating this permutational distribution. Accordingly, for
this method to be computationally feasible, it should only be
applied to meta-analyses with a relatively low number of
observed events. To create this permutational distribution, a
network algorithm, based on the work of Mehta et al. (1992)
<doi:10.2307/1390598> and Corcoran et al. (2001)
<doi:10.1111/j.0006-341x.2001.00941.x>, is employed using C++
and integrated into the package.