nmadt.hierarchical {NMADiagT}

Network Meta-Analysis Using the Hierarchical Model

Description

nmadt.hierarchical performs meta-analysis using the hierarchical model (Ma et al. 2018) and outputs CIs for accuracy measurements.

Usage

nmadt.hierarchical(nstu, K, data, testname, directory = NULL, diag = 5,
  off_diag = 0.05, digits = 4, mu_alpha = 0, mu_beta = 0,
  mu_eta = -0, preci_alpha = 0.1, preci_beta = 0.1,
  preci_eta = 0.1, n.adapt = 5000, n.iter = 50000, n.chains = 3,
  n.burnin = floor(n.iter/2), n.thin = max(1, floor((n.iter -
  n.burnin)/50000)), conv.diag = FALSE, trace = NULL, dic = FALSE,
  mcmc.samples = FALSE)

Arguments

nstu

an integer indicating the number of studies included in the dataset.

K

an integer indicating the number of candiate test in the dataset.

data

a list conating the input dataset to be used for meta-analysis.

testname

a string vector of the names of the candidate tests in the dataset in the same order as presetned in the dataset.

directory

a string specifying the designated directory to save trace plots or potential scale reduction factors calculated in the function. The default is NULL.

diag

a number indicating the value of diagonal entries of the scale matrix R of the precision matrix \Sigma. The default is 5.

off_diag

a number indicating the value of off-diagonal entries of the scale matrix R of the precision matrix \Sigma. The default is 0.05.

digits

a positive integer he number of digits to the right of the decimal point to keep for the results; digits=4 by default.

mu_alpha

a number indicating the mean of the normal distribution that the prior of the fixed effect for sensitivity follows. The default is 0.

mu_beta

a number indicating the mean of the normal distribution that the prior of the fixed effect for specificity follows. The default is 0.

mu_eta

a number indicating the mean of the normal distribution that the prior of the fixed effect for prevalence follows. The default is 0.

preci_alpha

a number indicating the precision of the normal distribution that the prior of the fixed effect for sensitivity follows. The default is 0.1.

preci_beta

a number indicating the precision of the normal distribution that the prior of the fixed effect for specificity follows. The default is 0.1.

preci_eta

a number indicating the precision of the normal distribution that the prior of the fixed effect for prevalence follows. The default is 0.1.

n.adapt

a positive integer indicating the number of iterations for adaptation. The default is 5,000.

n.iter

a postive integer indicating the number of iterations in each MCMC chain. The default is 50,000.

n.chains

a postive interger indicating the number of MCMC chains. The default is 3.

n.burnin

a positive integer indicating the number of burn-in iterations at the beginning of each chain without saving any of the posterior samples. The default is floor(n.iter/2).

n.thin

the thinning rate for MCMC chains, which is used to save memory and computation time when n.iter is large. For example, the algorithm saves only one sample in every nth iteration, where n is given by n.thin.

conv.diag

a logical value specifying whether to compute potential scale reduction factors proposed for convergence diagnostics. The default is FALSE.

trace

a string vector containing a subset of different quantities which can be chosen from prevalence("prev"), sensitivity ("Se"), specificity ("Sp"), positive and negative predictive values ("ppv" and "npv" repectively), positive likelihood ("LRpos"), and negative likelihood ("LRneg").

dic

a logical value indicating whether the function will output the deviance information criterion (DIC) statistic. The default is false.

mcmc.samples

a logical value indicating whether the coda samples generated in the meta-analysis. The default is FALSE.

Value

A list with the raw output for graphing the results, the effect size estimates, which lists the posterior mean, standard deviation, median, and a $95$% equal tail credible interval for the median.

References

Ma X, Lian Q, Chu H, Ibrahim JG, Chen Y (2018). “A Bayesian hierarchical model for network meta-analysis of multiple diagnostic tests.” Biostatistics, 19(1), 87–102. ISSN 14684357, doi: 10.1093/biostatistics/kxx025.

Examples

kangdata<-read.csv(file=system.file("extdata","kangdata.csv",package="NMADiagT"),
header=TRUE, sep=",")
set.seed(9)
kang.out <- nmadt.hierarchical(nstu=12, K=2, data=kangdata, testname=c("D-dimer","Ultrasonography"))