definemissingstatus: Utility function to generate missing status variables in...

In isni: Index of Local Sensitivity to Nonignorability

Description

Utility function to generate missing status variables in longitudinal data with dropout and/or intermittent missingness.

Usage

definemissingstatus(data, id, time, y)

Arguments

data	the name of the panel dataset in the long format with each row denoting a subject-visit observation for ALL the planned visits, regardless of being missed or not. When a subject is lost to follow up, the data set must include the observation at the first time of being lost to follow up.
id	the name of the level-2 clustering variable.
time	the name of the variable denoting the time of the visit. Can set time=NULL if data is already sorted by id and time within id.
y	the name of the outcome variable of the interest that is subject to missingness.

Value

a dataset with the following three new variables added:

• g_ : missingness indicator, "O"-observed, "I"-intermittent missing, "D"-dropout

• gp_: missingness indicator in the previous visit, "O"-observed, "I"-intermittent missing, "D"-dropout, "U"-undefined.

• yp_: the immediately observed prior outcome.

Examples

qolefnew <- definemissingstatus(qolef, id=id, time=time, y=y)

isni documentation built on Aug. 23, 2021, 9:06 a.m.