nmadt.hsroc.MNAR: Network Meta-Analysis Using the hierarchical model
In NMADiagT: Network Meta-Analysis of Multiple Diagnostic Tests

Description Usage Arguments Value References Examples

View source: R/nmadt.hsroc.MNAR.R

nmadt.hsroc.MNAR performs network meta-analysis of diagnostic tests using the HSROC (hierarchical summary receiver operating characteristic) model \insertCitelian2018bayesianNMADiagT based on the MNAR assumption.

nmadt.hsroc.MNAR(nstu, K, data, testname, directory = NULL, eta = 0,
  xi_preci = 1.25, digits = 4, gamma1, gamma0, mu_gamma = 0,
  preci_gamma = 1, n.adapt = 10000, n.iter = 50000, n.chains = 3,
  n.burnin = floor(n.iter/2), n.thin = max(1, floor((n.iter -
  n.burnin)/1e+05)), conv.diag = FALSE, trace = NULL, dic = FALSE,
  mcmc.samples = FALSE)

`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.
`eta`	a number indicating the mean of log(S) and log(P) which determines the covariance matrices of the cutoff values and accuracy values respectively. The default is 0.
`xi_preci`	a number indicating the precision of log(S) and log(P) which determines the covariance matrices of the cutoff values and accuracy values respectively. The default is 1.25.
`digits`	a positive integer he number of digits to the right of the decimal point to keep for the results; digits=4 by default.
`gamma1`	a vector indicating coefficients of study-specific sensitivity in the MNAR model.
`gamma0`	a vector indicating coefficients of study-specific specificity in the MNAR model.
`mu_gamma`	a number specifying mean of intercept in the MNAR model. The default is 0.
`preci_gamma`	a number specifying precision of intercept in the MNAR model. The default is 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`.

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.

\insertRef

lian2018bayesianNMADiagT

kangdata<-read.csv(file=system.file("extdata","kangdata.csv",package="NMADiagT"),
header=TRUE, sep=",")
set.seed(9)
kangMNAR.out.hsroc <- nmadt.hsroc.MNAR(nstu=12, K=2, data=kangdata,
testname=c("D-dimer","Ultrasonography"),gamma1=c(-0.5,-0.5), gamma0=c(-0.5,-0.5))