benchmarkCohortSurvival {CohortSurvival}

Estimate performance of estimateSurvival function for benchmarking

Description

Estimate performance of estimateSurvival function for benchmarking

Usage

benchmarkCohortSurvival(
  cdm,
  targetSize,
  outcomeSize,
  outcomeDateVariable = "cohort_start_date",
  competingOutcomeSize = NULL,
  competingOutcomeDateVariable = "cohort_start_date",
  censorOnCohortExit = FALSE,
  censorOnDate = NULL,
  followUpDays = Inf,
  strata = NULL,
  eventGap = 30,
  estimateGap = 1,
  minCellCount = 5,
  returnParticipants = FALSE
)

Arguments

cdm

CDM reference

targetSize

number of people in the target cohort table

outcomeSize

number of people in the outcome cohort table

outcomeDateVariable

Variable containing date of outcome event

competingOutcomeSize

number of people in the competing outcome cohort table

competingOutcomeDateVariable

Variable containing date of competing event

censorOnCohortExit

If TRUE, an individual's follow up will be censored at their cohort exit

censorOnDate

if not NULL, an individual's follow up will be censored at the given date

followUpDays

Number of days to follow up individuals (lower bound 1, upper bound Inf)

strata

strata

eventGap

Days between time points for which to report survival estimates. First day will be day zero with risk estimates provided for times up to the end of follow-up, with a gap in days equivalent to eventGap.

estimateGap

vector of time points at which to give survival estimates, if NULL estimates at all times are calculated

minCellCount

The minimum number of events to reported, below which results will be obscured. If 0, all results will be reported.

returnParticipants

Either TRUE or FALSE. If TRUE, references to participants from the analysis will be returned allowing for further analysis.

Value

tibble with performance of estimateSurvival function information, according to the selected input parameters

Examples

cdm <- mockMGUS2cdm()
cdm$condition_occurrence <- cdm$death_cohort %>%
dplyr::rename("condition_start_date" = "cohort_start_date",
             "condition_end_date" = "cohort_end_date") %>%
             dplyr::compute()
surv_timings <- benchmarkCohortSurvival(
cdm, targetSize = 100, outcomeSize = 20)