R/hbbrAugResponseSim.R

In hbbr: Hierarchical Bayesian Benefit-Risk Assessment Using Discrete Choice Experiment

#' A list consisting of simulated data, design, baseline profiles, and true part-worth matrix
#' for the Augmented HBBR model framework.
#'
#' Simulated response data and associated information from 100 respondents each choosing
#' preference from 12 choice cards. Choice cards were randomly generated from 36 total choices.
#'
#' @docType data
#'
#' @usage data(simAugData)
#'
#' @format A list consisting of simulated response data from 100 subjects, design information,
#' baseline profiles, and true part-worth matrix of the Augmented HBBR model framework.
#'
#' \describe{
#'    \item{brdtaAug}{A data frame of 100x12 rows and 10 columns consists of simulated responses from
#'    100 subjects each providing tradeoff responses to 12 choice pairs. The 1st column consists of
#'    subject id. The 2nd column contains binary responses (1 indicating 1st of the choice
#'    pair was selected, 0 indicating 2nd was selected). Remaining 8 columns
#'    contain the design matrix X taking values 0, 1, or -1; a value of 1 or -1 is used to
#'    indicate presence of an attribute level in the 1st choice or in the 2nd choice of the choice
#'    pair, respectively; a value of 0 is used to indicate absence of an attribute in the
#'    choice pair.  See Details below for more about the discrete choice experiment that is coded as
#'    design matrix X.}
#'    \item{design}{A list of structure (b, r, bl, rl), where b and r indicate number of benefit
#'    and risk attributes, bl is a vector of integers of size b consisting number of levels within
#'    each benefit attribute; similarly rl is a vector of integers of size r consisting number
#'    of levels within each risk attribute.}
#'    \item{Z}{A data frame of size 100x3 consists of baseline characteristics from 100 subjects.
#'    The 1st column of Z is vector of 1, the 2nd column consists of standardized age, and
#'    the 3rd column indicates disease status at baseline (1= present, -1 = not present).
#'    The 1st  column being a constant vector of 1 indicates that the part-worth matrix to be estimated,
#'    say Del-hat, would have the overall mean part-worth in the 1st column, while 2nd and 3rd
#'    columns would consists of estimates of additive components of part-worth due to age and
#'    disease status.}
#'    \item{Del}{A matrix of true part-worth values of dimension 8x3. This was used along with
#'    X and Z to generate the responses from 100 subjects captured in the 2nd column of brdtaSim.}
#' }
#' @details The simulated discrete choice experiment (DCE) included 3 benefit
#'          attributes (b=2): B1, B2 (say) and 2 risk attributes (r=2): R1, R2 (say).
#'          There were 3 levels for each of the benefit attributes ("Low", "Moderate", "High")
#'          (i.e. bl= rep(3,2)) and
#'          3 levels for each of the risk attributes ("None", "Mild", "Severe")
#'          (i.e. rl = rep(3,2)).
#'          The DCE produced 36 distinct non-dominant choice pairs each with one benefit and one
#'          risk attribute. Panels (questionnaires) were generated with 12 (randomly) selected
#'          choice pairs per panel from the set of 36 choice pairs. Since the part-worth of various levels
#'          within each attribute are to be measured relatively to the part-worth of the 1st level of the
#'          attribute, columns for the 1st level of the attributes are not required. Thus, we have sum(bl)-b
#'          + sum(br)-r = 8 columns are needed to obtain information on the X matrix which are stored
#'          as the last 8 columns of brdtaAugSim.
#'
#' @keywords datasets
#'
#' @examples
#' data(simAugData)
"simAugData"