gap (version 1.5-1)

mr_forestplot: Mendelian Randomization forest plot

Description

Mendelian Randomization forest plot

Usage

mr_forestplot(dat, sm = "", title = "", ...)

Arguments

dat

A data.frame with outcome id, effect size and standard error.

sm

Summary measure such as OR, RR, MD.

title

Title of the meta-analysis.

...

Other options for meta::forest().

Details

This is a wrapper of meta::forest() for multi-outcome Mendelian Randomization. It allows for the flexibility of both binary and continuous outcomes with and without summary level statistics.

Examples

if (FALSE) {
 tnfb <- '
             "multiple sclerosis"  0.69058600 0.059270400
   "systemic lupus erythematosus"  0.76687500 0.079000500
         "sclerosing cholangitis"  0.62671500 0.075954700
  "juvenile idiopathic arthritis" -1.17577000 0.160293000
                      "psoriasis"  0.00582586 0.000800016
           "rheumatoid arthritis" -0.00378072 0.000625160
     "inflammatory bowel disease" -0.14334200 0.025272500
         "ankylosing spondylitis" -0.00316852 0.000626225
                 "hypothyroidism" -0.00432054 0.000987324
              "allergic rhinitis"  0.00393075 0.000926002
         "IgA glomerulonephritis" -0.32696600 0.105262000
                  "atopic eczema" -0.00204018 0.000678061
 '
 require(dplyr)
 tnfb <- as.data.frame(scan(file=textConnection(tnfb),what=list("",0,0))) %>%
         setNames(c("outcome","Effect","StdErr")) %>%
         mutate(outcome=gsub("\\b(^[a-z])","\\U\\1",outcome,perl=TRUE))

 # default output
 mr_forestplot(tnfb, colgap.forest.left="0.05cm", fontsize=14, leftlabs=c("Outcome","b","SE"),
               common=FALSE, random=FALSE, print.I2=FALSE, print.pval.Q=FALSE, print.tau2=FALSE,
               spacing=1.6,digits.TE=2,digits.se=2)
 # no summary level statistics
 mr_forestplot(tnfb, colgap.forest.left="0.05cm", fontsize=14,
               leftcols="studlab", leftlabs="Outcome", plotwidth="3inch", sm="OR", rightlabs="ci", 
               sortvar=tnfb[["Effect"]],
               common=FALSE, random=FALSE, print.I2=FALSE, print.pval.Q=FALSE, print.tau2=FALSE,
               backtransf=TRUE, spacing=1.6)
 # with P values
 mr_forestplot(tnfb,colgap.forest.left="0.05cm", fontsize=14,
               leftcols=c("studlab"), leftlabs=c("Outcome"),
               plotwidth="3inch", sm="OR", sortvar=tnfb[["Effect"]],
               rightcols=c("effect","ci","pval"), rightlabs=c("OR","95%CI","P"),
               digits=3, digits.pval=2, scientific.pval=TRUE,
               common=FALSE, random=FALSE, print.I2=FALSE, print.pval.Q=FALSE, print.tau2=FALSE,
               addrow=TRUE, backtransf=TRUE, spacing=1.6)
}