#' @title Convert effect size d into OR
#' @name convert_d2or
#'
#' @description Compute effect size \code{OR} from effect size \code{d}.
#'
#' @param d The effect size \code{d}.
#' @param se The standard error of \code{d}. One of \code{se} or \code{v}
#' must be specified.
#' @param v The variance of \code{d}. One of \code{se} or \code{v} must be
#' specified.
#' @param es.type Type of effect size odds ratio that should be returned.
#' May be \code{es.type = "logit"} or \code{es.type = "cox"}
#' (see 'Details').
#' @param info String with information on the transformation. Used for the
#' print-method. Usually, this argument can be ignored
#'
#' @inheritParams esc_beta
#' @inheritParams hedges_g
#'
#' @note Effect size is returned as \code{exp(log_values)} (odds ratio),
#' confidence intervals are also exponentiated. To get the log-values,
#' use \code{\link{convert_d2logit}}.
#' \strong{However}, variance and standard error of this function
#' are returned on the log-scale!
#'
#' @details Conversion from \code{d} to odds ratios can be done with two
#' methods:
#' \describe{
#' \item{\code{es.type = "logit"}}{uses the Hasselblad and Hedges logit method.}
#' \item{\code{es.type = "cox"}}{uses the modified logit method as proposed by Cox.
#' This method performs slightly better for rare or frequent events, i.e.
#' if the success rate is close to 0 or 1.}
#' }
#'
#' @return The effect size \code{es}, the standard error \code{se}, the variance
#' of the effect size \code{var}, the lower and upper confidence limits
#' \code{ci.lo} and \code{ci.hi}, the weight factor \code{w} and the
#' total sample size \code{totaln}.
#'
#' @references Lipsey MW, Wilson DB. 2001. Practical meta-analysis. Thousand Oaks, Calif: Sage Publications
#' \cr \cr
#' Wilson DB. 2016. Formulas Used by the "Practical Meta-Analysis Effect Size Calculator". Unpublished manuscript: George Mason University
#' \cr \cr
#' Cox DR. 1970. Analysis of binary data. New York: Chapman & Hall/CRC
#' \cr \cr
#' Hasselblad V, Hedges LV. 1995. Meta-analysis of screening and diagnostic tests. Psychological Bulletin 117(1): 167–178. \doi{10.1037/0033-2909.117.1.167}
#' \cr \cr
#' Borenstein M, Hedges LV, Higgins JPT, Rothstein HR. 2009. Introduction to Meta-Analysis. Chichester, West Sussex, UK: Wiley
#'
#' @examples
#' # d to odds ratio
#' convert_d2or(0.7, se = 0.5)
#' # odds ratio to d
#' convert_or2d(3.56, se = 0.91)
#'
#' @export
convert_d2or <- function(d, se, v, totaln,
es.type = c("logit", "cox"),
info = NULL, study = NULL) {
# match arguments
es.type <- match.arg(es.type)
# check if parameter are complete
if ((missing(se) || is.null(se) || is.na(se)) && (missing(v) || is.null(v) || is.na(v))) {
warning("Either `se` or `v` must be specified.", call. = F)
return(esc_generic(es = NA, v = NA, es.type = es.type, grp1n = NA, grp2n = NA, info = NA, study = NA))
}
# do we have se?
if (!missing(se) && !is.null(se) && !is.na(se)) v <- se ^ 2
# do we have total n?
if (missing(totaln) || is.na(totaln)) totaln <- NULL
# do we have a separate info string?
if (is.null(info)) {
info <- "effect size d to effect size OR"
if (es.type == "cox") info <- paste0(info, "(Cox)")
}
if (es.type == "logit") {
# Hasselblad and Hedges logit
es <- pi / sqrt(3) * d
v <- (pi ^ 2) / 3 * v
measure <- "or"
} else {
# Cox logit
es <- d * 1.65
v <- v / .367
measure <- "cox-or"
}
# return effect size d
structure(
class = c("esc", "convert_d2or"),
list(
es = exp(es),
se = sqrt(v),
var = v,
ci.lo = exp(lower_d(es, v)),
ci.hi = exp(upper_d(es, v)),
w = 1 / v,
totaln = totaln,
measure = measure,
info = info,
study = study
)
)
}
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