or2logor: Transform the effect estimates from the odds (ratio) to the...
In sl4bayesmeta: Sensitivity and learning for Bayesian meta-analysis

Description Usage Arguments Details Value See Also Examples

Transforms the posterior, sensitivity and learning estimates for the odds (ratio) in the given table to the log-odds (ratio) scale.

1	or2logor(tab)

tab

A matrix with 16 rows and 16 columns, which has the same structure as the output of the sensitivity_learning_table_flexibe function (including the column names)

Since the sensitivity and learning measures are invariant with respect to monotone transformations of the parameter, the sensitvity and learning estimates for the effect do not change under the log transformation.

A matrix with 16 rows and 16 columns, which is identical to the input matrix tab, except that columns 5-8 have been recalculated and renamed as follows, where mu=log(OR) denotes the effect on the log-odds (ratio) scale:

`median_post_mu`	posterior median for the effect mu
`95CrI_post_mu_low`	lower end point of the 95 % shortest credible interval (CrI) for the effect mu
`95CrI_post_mu_up`	upper end point of the 95 % shortest CrI for the effect mu
`length_95CrI_post_mu`	length of the 95 % shortest CrI for the effect mu

sensitivity_learning_table_flexible

# Acute Graft rejection (AGR) data analyzed in Friede et al. (2017),  
# Sect. 3.2, URL: https://doi.org/10.1002/bimj.201500236
# First study: experimental group: 14 cases out of 61; 
# control group: 15 cases out of 20 
# Second study: experimental group: 4 cases out of 36; 
# control group: 11 cases out of 36 
rT <- c(14,4)
nT <- c(61,36)
rC <- c(15,11)
nC <- c(20,36)
df <- data.frame(y = log((rT*(nC-rC))/(rC*(nT-rT))), # log-OR
                 sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), # SE(log-OR)
                 labels = c(1:2))
  
# compute the table for the AGR data
# warning: it takes ca. 5-10 minutes to run this function
# on the above data set!
tab.OR <- sensitivity_learning_table_flexible(df)
tab.logOR <- or2logor(tab.OR)