Check recorded follow-up before a time-related analysis

Summarises an event indicator and its follow-up intervals the way the dc-gfup template reports them: counts for the whole cohort and its event and censored subsets, the missing, negative and zero intervals, a proc_means table for each subset, and the rows worth a second look. It checks recorded follow-up; it does not establish completeness against a close date.

Usage

followup_check(
  data,
  event,
  followup,
  identifier = NULL,
  max_rows = 25L,
  stats = c("n", "nmiss", "mean", "std", "min", "p15", "median", "p85", "max")
)

Arguments

data

A data frame, one row per patient.

event

Name of the event indicator: 1 or TRUE for an event, 0 or FALSE for censored. Missing values are allowed and counted.

followup

Names of one or more follow-up interval columns, in years.

identifier

NULL (the default), or the name of one column to show in review.

max_rows

The most suspicious rows to return in review, in data order. Default 25.

stats

Character vector of SAS statistic keywords for the means tables, passed to proc_means and validated the same way. Default c("n", "nmiss", "mean", "std", "min", "p15", "median", "p85", "max").

Value

An object of class followup_check, a list of four components, three data frames and a list of three:

cohort

One row: full, event, censored and missing_event counts.

intervals

One row per interval: missing, negative and zero counts, then min, q1, median, q3, mean, sd and max of the observed values.

means

A named list, full, event and censored, of proc_means tables over the intervals, one column per statistic in stats.

review

Up to max_rows suspicious rows, with the event, the intervals and any identifier.

Details

Every argument is checked before anything is computed, and every column that is not in data is named in one error, so a job with three misspellings says so once.

A row with a missing event is counted in missing_event and belongs to neither the event nor the censored subset. A row is suspicious when its event is missing or any of its intervals is missing, negative or zero.

The quartiles in intervals use R's default interpolated quantiles (type = 7), while proc_means uses the SAS QNTLDEF=5 estimator, so a median in the two tables can disagree on a small subset. The difference is deliberate: intervals is a quick screen, and means is the table to compare against SAS.

means reports the 15th and 85th percentiles rather than the quartiles, because those are what the group's descriptive summaries show. SAS PROC MEANS prints no percentiles unless asked, so a parity comparison names the same statistics on the SAS side, or passes the SAS job's list to stats.

Identifiers are never included unless named in identifier. Check the result before sharing a report that shows review with one.

See also

proc_means, cohort_counts, which counts the analysable cohort rather than checking follow-up.

Examples

d <- data.frame(dead = c(1, 0, 0, NA, 1), iv_dead = c(2.5, 4, 0, 1.2, NA))
fc <- followup_check(d, event = "dead", followup = "iv_dead")
fc
#> <followup_check> event `dead`, interval(s) `iv_dead`
#>   Cohort     : 5 (2 event, 2 censored, 1 missing event)
#>   Suspicious : 3 row(s), 3 shown in $review
fc$cohort
#>   full event censored missing_event
#> 1    5     2        2             1
fc$review
#>   dead iv_dead
#> 1    0     0.0
#> 2   NA     1.2
#> 3    1      NA