d2BC_S_I_HC_raw {si4bayesmeta} | R Documentation |
Returns the second derivative of the Bhattacharyya coefficient for a Bayesian NNHM with respect to Priori and Likelihood perturbations given a HC heterogeneity prior.
d2BC_S_I_HC_raw(df, hh, rlmc = 0.5, mu_mean = 0, mu_sd = 4)
df |
original base data frame in a |
hh |
the step for the numerical computation of derivatives of BC with respect to changing RLMC in a Bayesian NNHM |
rlmc |
the value of the target RLMC, usually set to 0.25, 0.5 or 0.75 |
mu_mean |
mean of the normal prior for mu |
mu_sd |
sd of the normal prior for mu |
Provides the Sensitivity and Identification estimates according to Roos et al. (2020). Sensitivity quantification is performed by a HC heterogeneity prior perturbation in a Bayesian NNHM with a fixed likelihood. Identification quantification is obtained by likelihood perturbation in a Bayesian NNHM with a fixed HC heterogeneity prior. The measure is based on the computation of second derivatives of the Bhattacharyya coefficient with respect to RLMC at a base RLMC value.
Table with the S-I (sensitivity-identification) measure: with sensitivity ("S_d2BC_P") and identification ("I_d2BC_L").
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.
pri_par_adjust_HC
, d2BC
, descr_extract
, bayesmeta
in 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)) # 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 # set a global parameter for #RLMC perturbation hh0<-0.0044 # new value obtained empirically #by adjusting it for the #epsilon-local sensitivity approach #with grid_epsilon=0.00354 for RLMC adjusted HN, HC #heterogeneity priors with targets (0.25,0.5,0.75) #for NNHM # Fix the value of the target RLMC t_RLMC_025<-0.25 res_d2BC_S_I_rlmc025_HC_raw <- d2BC_S_I_HC_raw(df=df, hh=hh0, rlmc=t_RLMC_025, mu_mean=mu_mean, mu_sd=mu_sd) res_d2BC_S_I_rlmc025_HC_raw