si4bayesmeta-package {si4bayesmeta} | R Documentation |
Sensitivity and identification estimates for all the parameters in the Bayesian normal-normal hierarchical model (NNHM) induced by a Half-Normal (HN) and a Half-Cauchy (HC) heterogeneity priors are produced by two d2BC_S_I_HN_raw and d2BC_S_I_HC_raw functions. Six scenarios are considered: target relative latent model complexity (RLMC) values fixed at 0.25, 0.5 and 0.75 with RLMC-adjusted HN and HC heterogeneity priors. Corresponding posterior estimates can be obtained by the functions raw_estimates_HN and raw_estimates_HC. The methodology implemented in this package has been developed in Roos et al. (2020).
The DESCRIPTION file:
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Index of help topics:
BC_normal_m1m0 Bhattacharyya coefficient between two normal densities: the location modification BC_normal_sd1sd0 Bhattacharyya coefficient between two normal densities: the spread modification BC_normal_total Bhattacharyya coefficient between two normal densities HC_A0_2_Al_Au Epsilon-grid for a HC heterogeneity prior HN_A0_2_Al_Au Epsilon-grid for a HN heterogeneity prior Nmuh1N012h Hellinger distance between normal and standard normal densities d2BC The second derivative of the Bhattacharyya coefficient d2BC_S_I_HC_raw Sensitivity and Identification measure expressed by the second derivative of the Bhattacharyya coefficient for a Bayesian NNHM with a HC heterogeneity prior d2BC_S_I_HN_raw Sensitivity and Identification measure expressed by the second derivative of the Bhattacharyya coefficient for a Bayesian NNHM with a HN heterogeneity prior descr_extract Extraction of descriptive statistics from a bayesmeta object h2Nmuh1N01 Calibration of the Hellinger distance h_choice_all The choice of the numerical 'h' step value for RLMC perturbations median_rlmc Median RLMC pri_par_adjust_HC RLMC-based adjustment of the scale parameter for a HC distribution pri_par_adjust_HN RLMC-based adjustment of the scale parameter for a HN distribution raw_estimates_HC Raw estimates for the Bayesian meta-analysis expressed by a Bayesian NNHM with a HC heterogeneity prior raw_estimates_HN Raw estimates for the Bayesian meta-analysis expressed by a Bayesian NNHM with a HN heterogeneity prior rlmc_scaling_low RLMC adjusted scaling of the study-specific within-study standard deviation in the likelihood: reduced impact of observations rlmc_scaling_up RLMC adjusted scaling of the study-specific within-study standard deviation in the likelihood: increased impact of observations si4bayesmeta-package Sensitivity and Identification for the Bayesian Meta-Analysis sigma_ref Reference within-study standard deviation
An overview of how to use the package, including the most important functions
Sona Hunanyan [aut, cre], Malgorzata Roos [aut]
Maintainer: Sona Hunanyan <sona.hunanyan@uzh.ch>
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.
# 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))), # log-odds-ratio sigma = sqrt(1/rT+1/(nT-rT)+1/rC+1/(nC-rC)), # SE(log-odds-ratio) labels = c(1:2)) ####---- Settings: hh, t_RLMC, Prior parameter values for mu ----#### # 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 RMC (relative model complexity) 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) in the case of NNHM # Fix the value of the target RMC t_RLMC_025<-0.25 ####---- HN: S-I measure (based on the second derivative d2BC/dRLMC at base/target RLMC0) #computation for a NNHM ----#### res_d2BC_S_I_rlmc025_HN_raw <- d2BC_S_I_HN_raw(df=df, hh=hh0, rlmc=t_RLMC_025, mu_mean=mu_mean, mu_sd=mu_sd) res_d2BC_S_I_rlmc025_HN_raw ####---- HC: S-I measure (based on the second derivative d2BC/dRLMC at base/target RLMC0) #computation for a NNHM ----#### 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