HC_A0_2_Al_Au: Epsilon-grid for a HC heterogeneity prior

In si4bayesmeta: Sensitivity and Identification for the Bayesian Meta-Analysis

Description

Returns the epsilon-grid for HC(A0) heterogeneity prior according to Ott et al. (2019).

Usage

HC_A0_2_Al_Au(AA0, eps = grid_epsilon)

Arguments

AA0	scale parameter of the base HC distribution
eps	epsilon for the epsilon grid

Details

Local epsilon-grid for the HC distribution which consists of two scale parameters AAl and AAu such that

H(HC(AAl), HC(AA0))=eps,

H(HC(AAu), HC(AA0))=eps.

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

Value

A vector of two scale parameters AAl and AAu, that is, the local epsilon-grid for the HC distribution which consists of two scale parameters AAl and AAu.

References

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).

See Also

pri_par_adjust_HC

Examples

# 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)$p_HC
HC_A0_2_Al_Au(AA0=tau_HC_rlmc025_s, eps=0.00354)

si4bayesmeta documentation built on Dec. 16, 2019, 3:39 p.m.