\name{neurodeg}
\alias{neurodeg}
\docType{data}
\title{ Neurodegenerative disease simulated longitudinal dose-finding data set }
\description{
  This simulated data set is motivated by a real Phase 2 clinical study
  of a new drug for a neurodegenerative disease. The state of the
  disease is measured through a functional scale, with smaller values
  corresponding to more severe neurodeterioration. The goal of the drug
  is to reduce the rate of disease progression, which is measured by the
  linear slope of the functional scale over time.

  The trial design includes placebo and four doses: 1, 3, 10, and 30 mg,
  with balanced allocation of 50 patients per arm. Patients are
  followed up for one year, with measurements of the functional scale
  being taken at baseline and then every three months.

  The functional scale response is assumed to be normally distributed
  and, based on historical data, it is believed that the longitudinal
  progression of the functional scale over the one year of follow up can
  be modeled a simple linear trend. See the example below on how to
  analyse this type of data.

  This data set was used in Pinheiro et al. (2014) to illustrate the
  generalized MCPMod methodology.
}
\usage{data(neurodeg)}
\format{
  A data frame with 100 observations on the following 2 variables.
  \describe{
    \item{\code{resp}}{a numeric vector containing the response values}
    \item{\code{dose}}{a numeric vector containing the dose values}
    \item{\code{id}}{Patient ID}    
    \item{\code{time}}{time of measurement}
  }
}
\source{
  Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014)
  Model-based dose finding under model uncertainty using general
  parametric models, \emph{Statistics in Medicine}, \bold{33},
  1646--1661
}

\examples{
\dontrun{
## reproduce analysis from Pinheiro et al. (2014)
data(neurodeg)
## first fit the linear mixed effect model
library(nlme)
fm <- lme(resp ~ as.factor(dose):time, neurodeg, ~time|id, method = "ML")
muH <- fixef(fm)[-1] # extract estimates
covH <- vcov(fm)[-1,-1]

## derive optimal contrasts for candidate shapes
doses <- c(0, 1, 3, 10, 30)
mod <- Mods(emax = 1.11, quadratic= -0.022, exponential = 8.867,
            linear = NULL, doses = doses) # 
contMat <- optContr(mod, S=covH) # calculate optimal contrasts
## multiple contrast test
MCTtest(doses, muH, S=covH, type = "general", critV = TRUE,
        contMat=contMat)
## fit the emax model
fitMod(doses, muH, S=covH, model="emax", type = "general",
       bnds=c(0.1, 10))


## alternatively one can also fit the model using nlme
nlme(resp ~ b0 + (e0 + eM * dose/(ed50 + dose))*time, neurodeg,
     fixed = b0 + e0 + eM + ed50 ~ 1, random = b0 + e0 ~ 1 | id,
     start = c(200, -4.6, 1.6, 3.2))
## both approaches lead to rather similar results
}
}
\keyword{datasets}
