`meta_stan` fits a meta-analysis model using Stan.
Usage
meta_stan(
data = NULL,
likelihood = NULL,
mu_prior = c(0, 10),
theta_prior = NULL,
tau_prior = 0.5,
tau_prior_dist = "half-normal",
beta_prior = c(0, 100),
delta = NULL,
re = TRUE,
ncp = TRUE,
interval.type = "shortest",
mreg = FALSE,
cov = NULL,
chains = 4,
iter = 2000,
warmup = 1000,
adapt_delta = 0.95,
...
)Arguments
- data
Data frame created by `create_MetaStan_dat`
- likelihood
A string specifying the likelihood function defining the statistical model. Options include `normal`, `binomial`, and `Poisson`.
- mu_prior
A numerical vector specifying the parameter of the normal prior density for baseline risks, first value is parameter for mean, second is for variance. Default is c(0, 10).
- theta_prior
A numerical vector specifying the parameter of the normal prior density for treatment effect estimate, first value is parameter for mean, second is for variance. Default is NULL.
- tau_prior
A numerical value specifying the standard dev. of the prior density for heterogeneity stdev. Default is 0.5.
- tau_prior_dist
A string specifying the prior density for the heterogeneity standard deviation, option is `half-normal` for half-normal prior, `uniform` for uniform prior, `half-cauchy` for half-cauchy prior.
- beta_prior
A numerical vector specifying the parameter of the normal prior density for beta coefficients in a meta-regression model, first value is parameter for mean, second is for variance. Default is c(0, 100).
- delta
A numerical value specifying the upper bound of the a priori interval for treatment effect on odds ratio scale (Guenhan et al (2020)). This is used to calculate a normal weakly informative prior. for theta. Thus when this argument is specified, `theta` should be left empty. Default is NULL.
- re
A string specifying whether random-effects are included to the model. When `FALSE`, the model corresponds to a fixed-effects model. The default is `TRUE`.
- ncp
A string specifying whether to use a non-centered parametrization. The default is `TRUE`.
- interval.type
A string specifying the type of interval estimate. Options include shortest credible interval `shortest` (default) and qui-tailed credible interval `central`.
- mreg
A string specifying whether to fit a meta-regression model. The default is `FALSE`.
- cov
A numeric vector or matrix specifying trial-level covariates (in each row). This is needed when `mreg = TRUE`.
- chains
A positive integer specifying the number of Markov chains. The default is 4.
- iter
A positive integer specifying the number of iterations for each chain (including warmup). The default is 2000.
- warmup
A positive integer specifying the number of warmup (aka burnin) iterations per chain. The default is 1000.
- adapt_delta
A numerical value specifying the target average proposal acceptance probability for adaptation. See Stan manual for details. Default is 0.95. In general you should not need to change adapt_delta unless you see a warning message about divergent transitions, in which case you can increase adapt_delta from the default to a value closer to 1 (e.g. from 0.95 to 0.99, or from 0.99 to 0.999, etc).
- ...
Further arguments passed to or from other methods.
References
Guenhan BK, Roever C, Friede T. MetaStan: An R package for meta-analysis and model-based meta-analysis using Stan. In preparation.
Guenhan BK, Roever C, Friede T. Random-effects meta-analysis of few studies involving rare events Resarch Synthesis Methods 2020; doi:10.1002/jrsm.1370.
Jackson D, Law M, Stijnen T, Viechtbauer W, White IR. A comparison of 7 random-effects models for meta-analyses that estimate the summary odds ratio. Stat Med 2018;37:1059--1085.
Kuss O. Statistical methods for meta-analyses including information from studies without any events-add nothing to nothing and succeed nevertheless, Stat Med, 2015; 4; 1097--1116, doi: 10.1002/sim.6383.
Examples
if (FALSE) {
## TB dataset
data('dat.Berkey1995', package = "MetaStan")
## Fitting a Binomial-Normal Hierarchical model using WIP priors
dat_MetaStan <- create_MetaStan_dat(dat = dat.Berkey1995,
armVars = c(responders = "r", sampleSize = "n"))
ma.stan <- meta_stan(data = dat_MetaStan,
likelihood = "binomial",
mu_prior = c(0, 10),
theta_prior = c(0, 100),
tau_prior = 0.5,
tau_prior_dist = "half-normal")
print(ma.stan)
forest_plot(ma.stan)
meta.reg.stan <- meta_stan(data = dat_MetaStan,
likelihood = "binomial",
mu_prior = c(0, 10),
theta_prior = c(0, 100),
tau_prior = 0.5,
tau_prior_dist = "half-normal",
mreg = TRUE,
cov = dat.Berkey1995$Latitude)
print(meta.reg.stan)
}