pri_par_adjust_HC {si4bayesmeta} | R Documentation |
Performs a RLMC-based adjustment of the scale parameter for a HC distribution according to Ott et al. (2019).
pri_par_adjust_HC(df, rlmc = 0.5, tail_prob = 0.5)
df |
data frame in |
rlmc |
requested target RLMC |
tail_prob |
tail probability of a heterogeneity prior |
Given the target amount of RLMC, we use 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_HC |
RLMC-based adjusted scale parameter for a HC 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).
# 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_HC_rlmc025_s<-pri_par_adjust_HC(df=df, rlmc=0.25, tail_prob=0.5) tau_HC_rlmc025_s