rBinomGauss: Data generation: Sampling data of clinical trials

Description Usage Arguments Details Value Examples

View source: R/methods-for-data-generation.R

Description

A random draw of a hierarchical binomial Gaussian model.

Usage

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  rBinomGauss(h, s, a, r, x, b)

Arguments

h

heterogeneity.

s

study sizes.

a

balance of group assignments.

r

fixed risk in the control group.

x

design matrix.

b

regression coefficients.

Details

It is always assumed that at least one response in a study has happend, i.e., a response of 0 in a treatment or control group is rounded up to 1. Note that this may lead to an overestimation of small risks. If possible, make sure your sample sizes are large enough to compensate for this effect.

You may work around this by increasing study sizes.

Value

A list containing the risk and a data frame with the studies.

Examples

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h_test <- .03
s_test <- rep(2000, 13)
a_test <- rep(.3, 13)
x_test <- cbind(1,1:13)
b_test <- c(0.02, 0.03)
dat <- rBinomGauss(h=h_test, s=s_test, a=a_test, r=0.03 , x=x_test,
b=b_test)$study

if(!all(dim(dat) == c(dim(x_test)[1], 4))) stop("Wrong dimension")

metagen documentation built on May 29, 2017, 7:13 p.m.