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 ). 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) and Corcoran et al. (2001) , is employed using C++ and integrated into the package.


Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.


0.0.1 by Brinley N. Zabriskie, a month ago

Browse source code at https://github.com/cran/rema

Authors: Brinley N. Zabriskie [aut, cre] , Benjamin Kinard [aut] , Chris Sypherd [aut] , Ryan Whetten [aut] , Madeleine Hays [ctb]

Documentation:   PDF Manual  

GPL (>= 3) | file LICENSE license

Imports graphics, Rcpp, Rdpack, stats

Suggests testthat, knitr, rmarkdown

Linking to Rcpp, progress

See at CRAN