MarkMissing {public.ctn0094extra}

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 who, when, visit and randomized. This data frame measures on which days the subjects visited the clinic (visit) and indicates when the subjects were randomized to Phase I of their respective studies (the randomized column). This data set will contain one (and only one) record per subject per day; and enough rows to cover all potential visits per the protocol length of the study.

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