raw_estimates_HC: Raw estimates for the Bayesian meta-analysis expressed by a...

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

Description

Computes the raw estimates for a Bayesian NNHM with a RLMC-adjusted HC heterogeneity prior given a target RLMC according to Roos et al. (2020).

Usage

raw_estimates_HC(df, rlmc = 0.5, mu_mean = 0, mu_sd = 4)

Arguments

df	original base data frame in a bayesmeta format
rlmc	the value of the target RLMC, usually set to 0.25, 0.5 or 0.75. Default value is 0.5.
mu_mean	mean of the normal prior for mu. Default value is 0.
mu_sd	standard deviation of the normal prior for mu. Default value is 4.

Value

A matrix with the raw estimates and shortest 95% CrI and its length for mu, tau, thetai, i=1,..,k and theta_new.

Note

Needs to have the package bayesmeta loaded.

References

Roos, M., Hunanyan, S., Bakka, H., Rue, H., (2020). Sensitivity and identification quantification by a relative latent model complexity perturbation in the Bayesian meta-analysis. Manuscript submitted to Research Synthesis Methods.

See Also

pri_par_adjust_HC

Examples

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-odds-ratio
                  sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), # SE(log-odds-ratio)
                  labels = c(1:2))
# Set the normal prior parameter values for mu according to Roever 2018 bayesmeta
# Suitable for logOR and log-odds scale with the unit information argument
mu_mean<-0
mu_sd<-4

# Fix the value of the target RMC
t_RLMC_025<-0.25

####---- HC: raw parameter estimates for the base NNHM ----####

raw_est_rlmc025_HC<-raw_estimates_HC(df=df, rlmc=t_RLMC_025, mu_mean=mu_mean, mu_sd=mu_sd)
raw_est_rlmc025_HC

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