nmadt.hierarchical.MNAR: Network Meta-Analysis Using the Hierarchical Model Under MNAR...
In NMADiagT: Network Meta-Analysis of Multiple Diagnostic Tests

Description Usage Arguments Value References Examples

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

nmadt.hierarchical.MNAR performs meta-analysis using the hierarchical model \insertCiteMa2018NMADiagT based on the missing not at random(MNAR) assumption.

nmadt.hierarchical.MNAR(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, gamma1, gamma0, mu_gamma = 0, preci_gamma = 1,
  n.burnin = floor(n.iter/2), n.thin = max(1, floor((n.iter -
  n.burnin)/1e+05)), n.adapt = 5000, n.iter = 50000, n.chains = 3,
  conv.diag = FALSE, trace = NULL, dic = FALSE,
  mcmc.samples = FALSE)

`nstu`	an integer indimessageing 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 Σ. The default is 5.
`off_diag`	a number indicating the value of off-diagonal entries of the scale matrix R of the precision matrix Σ. 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 $0.1$.
`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.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`.
`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.
`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

Ma2018NMADiagT

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