MetaStan (version 1.0.0)

meta_stan: Fitting a meta-analysis model using Stan

Description

`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,
  param = "Smith",
  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.

param

Paramteriztaion used. The default is the `Smith` model suggested by Smith et al (1995). The alternative is `Higgins` is the common meta-analysis model (Simmonds and Higgins, 2014).

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.

Value

an object of class `MetaStan`.

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

# NOT RUN {
## 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)
# }
# NOT RUN {
# }