R/design_effect.R

In sjPlot/sjstats: Collection of Convenient Functions for Common Statistical Computations

#' @title Design effects for two-level mixed models
#' @name design_effect
#'
#' @description Compute the design effect (also called \emph{Variance Inflation Factor})
#'              for mixed models with two-level design.
#'
#' @param n Average number of observations per grouping cluster (i.e. level-2 unit).
#' @param icc Assumed intraclass correlation coefficient for multilevel-model.
#'
#' @return The design effect (Variance Inflation Factor) for the two-level model.
#'
#' @references Bland JM. 2000. Sample size in guidelines trials. Fam Pract. (17), 17-20.
#'             \cr \cr
#'             Hsieh FY, Lavori PW, Cohen HJ, Feussner JR. 2003. An Overview of Variance Inflation Factors for Sample-Size Calculation. Evaluation and the Health Professions 26: 239-257. \doi{10.1177/0163278703255230}
#'             \cr \cr
#'             Snijders TAB. 2005. Power and Sample Size in Multilevel Linear Models. In: Everitt BS, Howell DC (Hrsg.). Encyclopedia of Statistics in Behavioral Science. Chichester, UK: John Wiley and Sons, Ltd. \doi{10.1002/0470013192.bsa492}
#'             \cr \cr
#'             Thompson DM, Fernald DH, Mold JW. 2012. Intraclass Correlation Coefficients Typical of Cluster-Randomized Studies: Estimates From the Robert Wood Johnson Prescription for Health Projects. The Annals of Family Medicine;10(3):235-40. \doi{10.1370/afm.1347}
#'
#' @details The formula for the design effect is simply \code{(1 + (n - 1) * icc)}.
#'
#' @examples
#' # Design effect for two-level model with 30 observations per
#' # cluster group (level-2 unit) and an assumed intraclass
#' # correlation coefficient of 0.05.
#' design_effect(n = 30)
#'
#' # Design effect for two-level model with 24 observation per cluster
#' # group and an assumed intraclass correlation coefficient of 0.2.
#' design_effect(n = 24, icc = 0.2)
#'
#' @export
design_effect <- function(n, icc = 0.05) {
  1 + (n - 1) * icc
}