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#' @name Dickinson_outcome
#' @title Simulated individual-level binary outcome and baseline variables for study 1 in Dickinson et al (2015)
#' @description
#' At the end of the study, the researchers will have ascertained the outcome in the
#' 16 clusters. Suppose that the researchers were able to assess 300 children in each cluster.
#' We simulated correlated outcome data at the individual level using a generalized linear
#' mixed model (GLMM) to induce correlation by including a random effect. The intracluster
#' correlation (ICC) was set to be 0.01, using the latent response definition provided in
#' Eldrige et al. (2009). This is a reasonable value of the ICC for population health studies
#' (Hannan et al. 1994). We simulated one data set, with the outcome data dependent on the
#' county-level covariates used in the constrained randomization design and a positive treatment
#' effect so that the practice-based intervention increases up-to-date immunization rates more
#' than the community-based intervention. For each individual child, the outcome is equal to 1
#' if he or she is up-to-date on immunizations and 0 otherwise.
#'
#' Note that we still categorize the continuous variable of average income to illustrate
#' the use of cvcrand on multi-category variables, and we truncated the percentage
#' in CIIS variable at 100%.
#' @docType data
#' @format A data frame with 4800 rows and 7 variables:
#' \describe{
#' \item{county}{the identification for the county}
#' \item{location}{urban or rural}
#' \item{inciis}{percentage of children ages 19-35 months in the Colorado Immunization Information System (CIIS)}
#' \item{uptodateonimmunizations}{percentage of children already up-to-date on their immunization}
#' \item{hispanic}{percentage of population that is Hispanic}
#' \item{incomecat}{average income categorized into tertiles}
#' \item{outcome}{the status of being up-to-date on immunizations}
#' }
#' @references
#' Dickinson, L. M., B. Beaty, C. Fox, W. Pace, W. P. Dickinson, C. Emsermann,
#' and A. Kempe (2015): Pragmatic cluster randomized trials using covariate
#' constrained randomization: A method for practice-based research networks (PBRNs).
#' The Journal of the American Board of Family Medicine 28(5): 663-672
#'
#' Eldridge, S. M., Ukoumunne, O. C., & Carlin, J. B. (2009). The Intra Cluster
#' Correlation Coefficient in Cluster Randomized Trials: A Review of Definitions.
#' International Statistical Review, 77(3), 378-394.
#'
#' Hannan, P. J., Murray, D. M., Jacobs Jr, D. R., & McGovern, P. G. (1994).
#' Parameters to aid in the design and analysis of community trials: intraclass
#' correlations from the Minnesota Heart Health Program. Epidemiology, 88-95. ISO 690
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