ic_meta: Bayesian Hierarchical Model for Meta-Analysis

In csetraynor/condma:

Description

Bayesian hierarchical model used here to answer the question does the compiled literature provide evidence for the effectiveness of the treatment.

Usage

ic_meta(object, ...)

## S3 method for class 'data.frame'
ic_meta(object, transform = no_trans,
  hetero_var = TRUE, ...)

Arguments

object	A data frame containing the 'study' column, the 'yi' observed effect column , and at least one of the columns 'vi' variance or ; 'sei' standard error.
...	Additonal arguments to pass to [brms::brm()] such as 'set_prior', 'seed', 'warmup', etc.
transform	An named list of transformation and inverse transformation fuctions. See [logit_trans()] as an example.
hetero_var	A logical; if 'TRUE', then different variances are estimated for each model group. Otherwise, the same variance is used for each group akinf to the fixed effect model in the frequentist literature.

Details

These functions can be used to perform a Meta-analysis via Bayesian multilevel modeling.

By default, a generalized linear model with Gaussian error and an identity link is fit to the data.

Value

An object of class 'ic_meta'.

Examples

# Example objects from the "Getting Started" vignette at
#  https://topepo.github.io/tidyposterior/articles/Getting_Started.html

file <- system.file("examples", "roc_model.RData", package = "tidyposterior")
load(file)

roc_model

# Summary method shows the underlying `stan` model
summary(roc_model)

csetraynor/condma documentation built on May 4, 2019, 1:21 p.m.