Visualize Simaerep

create a ggplot2 visualisation for a simaerep KRI

Usage

Visualize_Simaerep(
  dfInput,
  dfFlagged,
  strStudyId = "StudyID",
  strScoreCol = "Score",
  nSiteMax = 16,
  vColors = NULL,
  strDenominator = "Denominator",
  strNumerator = "Numerator"
)

Arguments

dfInput

data.frame created by Input_CumCount()

dfFlagged

data.frame created by Flag_Simaerep()

strStudyId

character, study label, Default: "StudyID"

strScoreCol

character, name of score column in dfFlagged, Default: "Score"

nSiteMax

integer, maximum of flagged sites to plot, Default: 16

vColors

vector, named hex values for every Flag value in dfFlagged$Flag, Default NULL

strDenominator

vector, label for Denominator x-column, Default: "Denominator"

strNumerator

vector, label for Numerator y-column, Default: "Numerator"

Details

A widget that creates a simaerep visualisation of group-level metric results. It plots the mean cumulative numerator count per denominator in the left panel and highlights groups based on the over and under-reporting probability calculated by the simaerep bootstrap algorithm. Flagged groups are shown in the right panel including the total numerator counts per single patient.

Examples

 dfInput <- Input_CumCount(
   dfSubjects = clindata::rawplus_dm,
   dfNumerator = clindata::rawplus_ae,
   dfDenominator = clindata::rawplus_visdt %>% dplyr::mutate(visit_dt = lubridate::ymd(visit_dt)),
   strSubjectCol = "subjid",
   strGroupCol = "invid",
   strGroupLevel = "Site",
   strNumeratorDateCol = "aest_dt",
   strDenominatorDateCol = "visit_dt"
 )

 dfAnalyzed <- Analyze_Simaerep(dfInput)
 dfFlagged <- Flag_Simaerep(dfAnalyzed, vThreshold = c(-0.99, -0.95, 0.95, 0.99))
#> ℹ Sorted dfFlagged using custom Flag order: 2.Sorted dfFlagged using custom Flag order: -2.Sorted dfFlagged using custom Flag order: 1.Sorted dfFlagged using custom Flag order: -1.Sorted dfFlagged using custom Flag order: 0.

 Visualize_Simaerep(dfInput, dfFlagged)