R/missingness.param.prior_function.R

In rnmamod: Bayesian Network Meta-Analysis with Missing Participants

#' Define the mean value of the normal distribution of the missingness parameter
#'
#' @description
#'   Generates the mean value of the normal distribution of the missingness
#'   parameter in the proper format depending on the assumed structure of the
#'   missingness parameter. \code{\link{run_model}} inherits
#'   \code{missingness_param_prior} through the argument \code{mean_misspar}
#'   (see 'Argument' in \code{\link{run_model}}).
#'
#' @param assumption Character string indicating the structure of the
#'   informative missingness parameter.
#'   Set \code{assumption} equal to one of the following: \code{"HIE-COMMON"},
#'   \code{"HIE-TRIAL"}, \code{"HIE-ARM"}, \code{"IDE-COMMON"},
#'   \code{"IDE-TRIAL"}, \code{"IDE-ARM"}, \code{"IND-CORR"}, or
#'   \code{"IND-UNCORR"}.
#'   The default argument is \code{"IDE-ARM"}. The abbreviations \code{"IDE"},
#'   \code{"HIE"}, and \code{"IND"} stand for identical, hierarchical and
#'   independent, respectively. \code{"CORR"} and \code{"UNCORR"} stand for
#'   correlated and uncorrelated, respectively.
#' @param mean_misspar A numeric value or a vector of two numeric values for the
#'   mean of the normal distribution of the informative missingness parameter
#'   (see 'Details'). The default argument is 0 and corresponds to the
#'   missing-at-random assumption for \code{assumption = "IDE-ARM"}.
#'
#' @return A scalar or numeric vector to be passed to \code{\link{run_model}}.
#'
#' @details \code{\link{run_model}} considers the informative missingness odds
#'   ratio in the logarithmic scale for binary outcome data (Spineli, 2019a;
#'   Turner et al., 2015; White et al., 2008), the informative missingness
#'   difference of means when \code{measure} is \code{"MD"} or \code{"SMD"},
#'   and the informative missingness ratio of means in the logarithmic scale
#'   when \code{measure} is \code{"ROM"} (Spineli et al., 2021;
#'   Mavridis et al., 2015).
#'
#'   When \code{assumption} is trial-specific (i.e., \code{"IDE-TRIAL"} or
#'   \code{"HIE-TRIAL"}), or independent (i.e., \code{"IND-CORR"} or
#'   \code{"IND-UNCORR"}), only one numeric value can be assigned to
#'   \code{mean_misspar} because the same missingness scenario is applied to all
#'   trials and trial-arms of the dataset, respectively. When \code{assumption}
#'   is \code{"IDE-ARM"} or \code{"HIE-ARM"}, a maximum of two
#'   \emph{different or identical} numeric values can be assigned as a vector to
#'   \code{mean_misspars}: the first value refers to the experimental arm,
#'   and the second value refers to the control arm of a trial.
#'   In the case of a network, the first value is considered for all
#'   non-reference interventions and the second value is considered for the
#'   reference intervention of the network (see 'Argument' \code{ref} in
#'   \code{\link{run_model}}). This is necessary to ensure transitivity in the
#'   assumptions for the missingness parameter across the comparisons in the
#'   network (Spineli, 2019b).
#'
#'   Currently, there are no empirically-based prior distributions for the
#'   informative missingness parameters. The users may refer to
#'   Mavridis et al. (2015) and Spineli (2019) to determine \code{mean_misspar}
#'   for an informative missingness parameter.
#'
#' @author {Loukia M. Spineli}
#'
#' @seealso \code{\link{run_model}}
#'
#' @references
#' Mavridis D, White IR, Higgins JP, Cipriani A, Salanti G. Allowing for
#' uncertainty due to missing continuous outcome data in pairwise and
#' network meta-analysis. \emph{Stat Med} 2015;\bold{34}(5):721--41.
#' doi: 10.1002/sim.6365
#'
#' Spineli LM, Kalyvas C, Papadimitropoulou K. Continuous(ly) missing outcome
#' data in network meta-analysis: a one-stage pattern-mixture model approach.
#' \emph{Stat Methods Med Res} 2021;\bold{30}(4):958--75.
#' doi: 10.1177/0962280220983544
#'
#' Spineli LM. An empirical comparison of Bayesian modelling strategies for
#' missing binary outcome data in network meta-analysis.
#' \emph{BMC Med Res Methodol} 2019a;\bold{19}(1):86.
#' doi: 10.1186/s12874-019-0731-y
#'
#' Spineli LM. Modeling missing binary outcome data while preserving
#' transitivity assumption yielded more credible network meta-analysis
#' results. \emph{J Clin Epidemiol} 2019b;\bold{105}:19--26.
#' doi: 10.1016/j.jclinepi.2018.09.002
#'
#' Turner NL, Dias S, Ades AE, Welton NJ. A Bayesian framework to account
#' for uncertainty due to missing binary outcome data in pairwise meta-analysis.
#' \emph{Stat Med} 2015;\bold{34}(12):2062--80. doi: 10.1002/sim.6475
#'
#' White IR, Higgins JP, Wood AM. Allowing for uncertainty due to missing data
#' in meta-analysis--part 1: two-stage methods. \emph{Stat Med}
#' 2008;\bold{27}(5):711--27. doi: 10.1002/sim.3008
#'
#' @export
missingness_param_prior <- function(assumption, mean_misspar) {

  # Specifying the 'mean_misspar' for the missingness parameter
  if (!is.element(assumption, c("IDE-ARM",
                                "IDE-TRIAL",
                                "IDE-COMMON",
                                "HIE-ARM",
                                "HIE-TRIAL",
                                "HIE-COMMON",
                                "IND-CORR",
                                "IND-UNCORR"))) {
    stop("Insert 'IDE-ARM', 'IDE-TRIAL', 'IDE-COMMON', 'HIE-ARM', 'HIE-TRIAL',
         'HIE-COMMON', or 'IND-CORR', 'IND-UNCORR'.", call. = FALSE)
  } else if (is.element(assumption, c("HIE-ARM", "IDE-ARM")) &
             missing(mean_misspar)) {
    mean_misspar <- rep(0.0001, 2)
  } else if (is.element(assumption, c("IDE-TRIAL",
                                      "IDE-COMMON",
                                      "HIE-TRIAL",
                                      "HIE-COMMON",
                                      "IND-CORR",
                                      "IND-UNCORR")) &
             missing(mean_misspar)) {
    mean_misspar <- 0.0001
  } else if (is.element(assumption, c("HIE-ARM", "IDE-ARM")) &
             (length(mean_misspar) != 2)) {
    stop("'mean_misspar' must be a vector of two numeric values.",
         call. = FALSE)
  } else if (is.element(assumption, c("HIE-ARM", "IDE-ARM")) &
             (length(mean_misspar) == 2)) {
    mean_misspar <- as.vector(mean_misspar)
    mean_misspar[1] <- ifelse(mean_misspar[1] == 0, 0.0001, mean_misspar[1])
    mean_misspar[2] <- ifelse(mean_misspar[2] == 0, 0.0001, mean_misspar[2])
  } else if (is.element(assumption, c("IDE-TRIAL",
                                      "IDE-COMMON",
                                      "HIE-TRIAL",
                                      "HIE-COMMON",
                                      "IND-CORR",
                                      "IND-UNCORR")) &
             (length(mean_misspar) > 1)) {
    stop("'mean_misspar' must be a scalar.", call. = FALSE)
  } else if (is.element(assumption, c("IDE-TRIAL",
                                      "IDE-COMMON",
                                      "HIE-TRIAL",
                                      "HIE-COMMON",
                                      "IND-CORR",
                                      "IND-UNCORR")) &
             (length(mean_misspar) == 1)) {
    mean_misspar <- ifelse(mean_misspar == 0, 0.0001, mean_misspar)
  }

  return(mean_misspar)
}