prome (version 3.0.1.5)

blinding.test: Latent Shift Logistic Regression

Description

To be updated.

Usage

blinding.test(x, group, guess, mu0 = 0, s0 = 1,...)

Value

• `sig.sham`: sd of effect size of sham treatment.

Arguments

x,guess

outcome variable and guess response from blinding survey

group

group assignments. Current version support one or two groups only

mu0,s0

initial mean and sd of the latent variable of having sham effects

...

Parameters ("adapt_delta","stepsize","max_treedepth") to improve model fitting/convergence.

Examples

# \donttest{
u1      = 5.5 # trt
u2      = 2.0 # ctrl
theta   = 3.2 # sham
sigma2  = 2.5   # v(rij)
ntreat  = 500      
nsham   = 500

beta0 = 1.0
beta1 = 2.0
beta2 = 1.0 # no contamination

Tind  = c(rep(1, ntreat), rep(0,nsham))  #treatment group indicator
u1v   = rep(u1,ntreat)
u2v   = rep(u2,nsham)
uv    = c(u1v,u2v)
tauv  = uv - rep(u2, ntreat+nsham)
r = rnorm(ntreat + nsham, mean = 0, sd = sqrt(sigma2))
q = 1/(1 + exp(-(beta0 + beta1*Tind + beta2*(tauv+r))))
bernGen = function(qq){rbinom(1,1,qq)}
I = sapply(q,bernGen)
x = uv + theta*I + r   # fixed sham effect
## I have concerns about the error term(s). x.sham~N(theta,sigma.sham)?
sigma.sham = 1.5
r2 = rnorm(ntreat + nsham, mean = 0, sd = sqrt(sigma.sham))
x = (uv + r) + theta*I #+ r2   # fixed sham effect

out1 <- blinding.test(x=x,group=Tind,guess=I);
out1

##data(bd012)
##blinding.test(x=bd012$y, group=bd012$group,guess=bd012$guess)
##data(bd011)
##blinding.test(x=bd011$y, group=bd011$group,guess=bd011$guess)
##data(bd010)
##blinding.test(x=bd010$y, group=bd010$group,guess=bd010$guess)
# }