diff --git a/NAMESPACE b/NAMESPACE index 5198649..7659eda 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,6 +5,7 @@ S3method(confint,SccsModel) S3method(print,SccsModel) S3method(print,summary.SccsData) S3method(print,summary.SccsIntervalData) +export(checkRareOutcomeAssumption) export(computeEventDependentObservation) export(computeExposureChange) export(computeExposureStability) @@ -47,7 +48,6 @@ export(getResultsSummary) export(hasAgeEffect) export(hasCalendarTimeEffect) export(hasSeasonality) -export(isRareOutcomeAssumptionViolated) export(isSccsData) export(isSccsIntervalData) export(loadExposuresOutcomeList) diff --git a/R/Diagnostics.R b/R/Diagnostics.R index 8658d10..e3851ac 100644 --- a/R/Diagnostics.R +++ b/R/Diagnostics.R @@ -744,14 +744,15 @@ computeExposureStability <- function(studyPopulation, #' Modelling Guide with R, CRC Press, 2018 #' #' @return -#' A logical value, which is TRUE if the rare outcome assumption is violated. The assumption is -#' violated when restricting to first outcome only and the prevalence exceeds the pre-defined -#' threshold. +#' A tibble with one row and three columns: `outcomeProportion` indicates the proportion of people +#' having the outcome at least once. `firstOutcomeOnly` indicated whether the analysis was restricted +#' to the first outcome only. `rare` is TRUE if the rare outcome assumption is met, or the analysis +#' was not restricted to the first outcome. #' #' @export -isRareOutcomeAssumptionViolated <- function(studyPopulation, - firstOutcomeOnly = NULL, - maxPrevalence = 0.1) { +checkRareOutcomeAssumption <- function(studyPopulation, + firstOutcomeOnly = NULL, + maxPrevalence = 0.1) { errorMessages <- checkmate::makeAssertCollection() checkmate::assertList(studyPopulation, min.len = 1, add = errorMessages) checkmate::assertLogical(firstOutcomeOnly, len = 1, null.ok = TRUE, add = errorMessages) @@ -762,8 +763,11 @@ isRareOutcomeAssumptionViolated <- function(studyPopulation, firstOutcomeOnly <- prevalence$definitelyFirstOutcomeOnly | prevalence$probablyFirstOutcomeOnly } if (firstOutcomeOnly) { - return(prevalence$outcomeProportion > maxPrevalence) + rare <- prevalence$outcomeProportion <= maxPrevalence } else { - return(FALSE) + rare <- TRUE } + return(tibble(outcomeProportion = prevalence$outcomeProportion, + firstOutcomeOnly = firstOutcomeOnly, + rare = rare)) } diff --git a/man/isRareOutcomeAssumptionViolated.Rd b/man/checkRareOutcomeAssumption.Rd similarity index 78% rename from man/isRareOutcomeAssumptionViolated.Rd rename to man/checkRareOutcomeAssumption.Rd index 23df137..1b12c24 100644 --- a/man/isRareOutcomeAssumptionViolated.Rd +++ b/man/checkRareOutcomeAssumption.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/Diagnostics.R -\name{isRareOutcomeAssumptionViolated} -\alias{isRareOutcomeAssumptionViolated} +\name{checkRareOutcomeAssumption} +\alias{checkRareOutcomeAssumption} \title{Check if rare outcome assumption is violated} \usage{ -isRareOutcomeAssumptionViolated( +checkRareOutcomeAssumption( studyPopulation, firstOutcomeOnly = NULL, maxPrevalence = 0.1 @@ -22,9 +22,10 @@ outcome when pulling the data from the server.} allowed when restricting to first outcome only.} } \value{ -A logical value, which is TRUE if the rare outcome assumption is violated. The assumption is -violated when restricting to first outcome only and the prevalence exceeds the pre-defined -threshold. +A tibble with one row and three columns: \code{outcomeProportion} indicates the proportion of people +having the outcome at least once. \code{firstOutcomeOnly} indicated whether the analysis was restricted +to the first outcome only. \code{rare} is TRUE if the rare outcome assumption is met, or the analysis +was not restricted to the first outcome. } \description{ Check if rare outcome assumption is violated