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.
| Version: |
0.0.1 |
| Depends: |
R (≥ 2.10) |
| Imports: |
graphics, Rcpp, Rdpack, stats |
| LinkingTo: |
Rcpp, progress |
| Suggests: |
testthat (≥ 3.0.0), knitr, rmarkdown |
| Published: |
2021-10-28 |
| DOI: |
10.32614/CRAN.package.rema |
| Author: |
Brinley N. Zabriskie
 [aut, cre],
Benjamin Kinard [aut],
Chris Sypherd [aut],
Ryan Whetten [aut],
Madeleine Hays [ctb] |
| Maintainer: |
Brinley N. Zabriskie <zabriskie at stat.byu.edu> |
| License: |
GPL (≥ 3) | file LICENSE |
| NeedsCompilation: |
yes |
| Materials: |
README NEWS |
| In views: |
MetaAnalysis |
| CRAN checks: |
rema results |
Documentation:
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