pri_par_adjust_HN: RLMC-based adjustment of the scale parameter for a HN...
In si4bayesmeta: Sensitivity and Identification for the Bayesian Meta-Analysis

Description Usage Arguments Details Value References See Also Examples

Performs a RLMC-based adjustment of the scale parameter for a HN distribution according to Ott et al. (2019).

1	pri_par_adjust_HN(df, rlmc = 0.5, tail_prob = 0.5)

`df`	data frame in `bayesmeta` format
`rlmc`	requested target RLMC
`tail_prob`	tail probability of a heterogeneity prior

Given the target amount of RLMC, we suggest using a reference threshold U_{ref} for heterogeneity prior adjustment

U_{ref} = σ_{ref} √{RLMC/(1-RLMC)},

where σ_{ref} is the reference standard deviation. The RLMC-adjustment on the median of different heterogeneity priors aligned at U_{ref} reference threshold

P[τ > U_{ref}]= α = 50 \% .

See more details in Ott et al. (2019).

A list of

p_HN

RLMC-based adjusted scale parameter for a HN distribution

Ott, M., Hunanyan, S., Held, L., Roos, M., (2019). The relative latent model complexity adjustment for heterogeneity prior specification in Bayesian meta-analysis. Research Synthesis Methods (under revision).

sigma_ref, package bayesmeta

# 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))), 
            sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), 
                  labels = c(1:2))

tau_HN_rlmc025_s<-pri_par_adjust_HN(df=df, rlmc=0.25, tail_prob=0.5)
tau_HN_rlmc025_s