MarkMissing {public.ctn0094extra} | R Documentation |
Code Empty Visit Values as "Missing" as Appropriate
Description
Given a complete timeline of potential subject visits per study protocol, mark certain visits as "Missing"
Usage
MarkMissing(timeline_df, windowWidth = 7, daysGrace = 0)
Arguments
timeline_df |
A data frame with columns |
windowWidth |
How many days are expected between clinic visits? Defaults to 7, representing weekly clinic visits. |
daysGrace |
How many days late are subjects allowed to be for their weekly visit. Defaults to 0. Under this default behavior with weekly visits, a subject who visits the clinic on days 8 and 14 instead of days 7 and 14 will have a missing visit imputed for day 7. |
Details
Most definitions of opioid use disorder treatment success or failure partially depend on a tally of the number of missed clinic visits. For example, a definition of early treatment failure could be "3 or more UDS positive for non-study opioids or missing visits within the first 28 days of randomization". Given a table of subject visits by day over the entire protocol timeline, this function will estimate when each subject missed a clinic visit (unfortunately, missed visits can often be improperly recorded in the patient logs; if such information is complete, using this function is unnecessary).
This estimation is conducted as follows: (1) first, for each subject, a
regular grid of days is spread from the randomization day to the end of
treatment by windowWidth
; (2) next, we iterate over each day in
this regular grid, and at each step we check the next windowWidth
plus daysGrace
days for a visit in that range, and we mark the day
at the end of the window as "missing" if there are no visits in that
range; (3) and finally, we combine these subject-specific data tables.
Value
A copy of timeline_df
with the column visitYM
added.
This column is a copy of the visit
column with additional cells
marking if a subject should have attended the clinic but did not.
Examples
# TO DO