\name{glycobrom}
\alias{glycobrom}
\docType{data}
\title{ Glycopyrronium Bromide dose-response data }
\description{
  Data from a clinical study evaluating Efficacy and Safety of Four
  Doses of Glycopyrronium Bromide in Patients With Stable Chronic
  Obstructive Pulmonary Disease (COPD).  This data set was obtained from
  clinicaltrials.gov (NCT00501852).  The study design was a 4 period
  incomplete cross-over design. The primary endpoint is the trough
  forced expiratory volume in 1 second (FEV1) following 7 days of
  Treatment.

  The data given here are summary estimates (least-square means) for
  each dose.
}
\usage{data(glycobrom)}
\format{
  A data frame with 5 summary estimates (one per dose). Variables:
  \describe{
    \item{\code{dose}}{a numeric vector containing the dose values}
    \item{\code{fev1}}{a numeric vector containing the least square
      mean per dose}
    \item{\code{sdev}}{a numeric vector containing the standard errors
      of the least square means per dose}
   \item{\code{n}}{Number of participants analyzed per treatment group}
  }
}
\source{
  http://clinicaltrials.gov/ct2/show/results/NCT00501852
}
\examples{
 ## simulate a full data set with given means and sdv (here we ignore
  ## the original study was a cross-over design, and simulate a parallel
  ## group design)
  simData <- function(mn, sd, n, doses, fixed = TRUE){
    ## simulate data with means (mns) and standard deviations (sd), for
    ## fixed = TRUE, the data set will have observed means and standard
    ## deviations as given in mns and sd
    resp <- numeric(sum(n))
    uppind <- cumsum(n)
    lowind <- c(0,uppind)+1
    for(i in 1:length(n)){
      rv <- rnorm(n[i])
      if(fixed)
        rv <- scale(rv)
      resp[lowind[i]:uppind[i]] <- mn[i] + sd[i]*rv
    }
    data.frame(doses=rep(doses, n), resp=resp)
  }
  data(glycobrom)
  fullDat <- simData(glycobrom$fev1, glycobrom$sdev, glycobrom$n,
                     glycobrom$dose)
}
\keyword{datasets}
