Although many software tools can perform meta-analyses on genetic case-control data,
none of these apply to combined case-control and family-based (TDT) studies. This package conducts
fixed-effects (with inverse variance weighting) and random-effects [DerSimonian and Laird (1986)
This package conducts fixed-effects (with inverse variance weighting) and random-effects (DerSimonian and Laird (1986)) meta-analyses of case-control or family-based (TDT) genetic data. In addition, catmap performs meta-analyses which combine these two types of study designs. Specifically, this package implements a fixed-effects model (Kazeem and Farrall (2005)) and a random-effects model (Nicodemus (2008)) for combined studies. This package was removed from the CRAN repository sometime after 2009. This is a rendition of the original package updated to work with the newest version of R. The algorithms have not changed since catmap version 1.6.0; however, this version has added some aesthetic improvements.
The main function,
matrix, or file input. See
?catmapdata for help.
## 1 Peter,2002 2 0 0 316 338 220 218 ## 2 Abrams,2001 2 0 0 710 146 422 96 ## 3 Todd,2003 2 0 0 1004 344 233 543 ## 4 Yu,2007 2 0 0 3344 434 544 322 ## 5 Wei,2007 1 65 32 0 0 0 0
It is important to save the output of the
catmap function for the next step in the analysis.
c1 <- catmap(catmapdata, 0.95, FALSE)
Four secondary functions use the output of the
catmap function to build the meta-analysis figures, including the forest plot and the funnel plot. The functions below output these figures to the working directory as pdf files.
# Make forest plots?catmap.forest?catmap.sense?catmap.cumulative# Make funnel plot?catmap.funnel
test_catmap.Runit tests complete and validated
test_forest.Runit tests complete and validated
makeForestfunction builds all forest figures
catmapdataobject in RDA format