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#' Calculate the Observed Covariate E-value
#'
#' @param lb Numeric. The lower bound of the full model
#' @param ub Numeric. The upper bound of the full model
#' @param lb_adj Numeric. The lower bound of the adjusted model
#' @param ub_adj Numeric. The upper bound of the adjusted model
#' @param transform Character. If your effect is an odds ratio or hazard ratio, this will
#' perform the transformation suggested by VanderWeele and Ding. Allowed values are:
#' * "OR"
#' * "HR"
#'
#' @return The Observed Covariate E-value
#' @export
observed_covariate_e_value <- function(lb, ub, lb_adj, ub_adj, transform = NULL) {
if (!is.null(transform)) {
if (!transform %in% c("OR", "HR")) {
stop_glue("You input\n * `transform`: {transform}\n ",
"The only valid `transform` inputs are\n * 'HR'\n * 'OR'")
}
if (transform == "OR") {
lb <- sqrt(lb)
ub <- sqrt(ub)
lb_adj <- sqrt(lb_adj)
ub_adj <- sqrt(ub_adj)
}
if (transform == "HR") {
lb <- hr_transform(lb)
ub <- hr_transform(ub)
lb_adj <- hr_transform(lb_adj)
ub_adj <- hr_transform(ub_adj)
}
}
b <- get_limiting_bound(lb, ub)
b_adj <- get_limiting_bound_adj(b, lb_adj, ub_adj)
if (b < 1) {
b <- 1 / b
b_adj <- 1 / b_adj
}
if (b < b_adj) {
return((b_adj / b) + sqrt((b_adj / b) * ((b_adj / b) - 1)))
}
(b / b_adj) + sqrt((b / b_adj) * ((b / b_adj) - 1))
}
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