col.headings: Specify the columns of the data frame required by score...

In InformativeCensoring: Multiple Imputation for Informative Censoring

Specify the columns of the data frame required by score imputation method

Description

Specify the columns of the data frame required by score imputation method

Usage

col.headings(arm, has.event, time, Id, DCO.time, to.impute, censor.type = NULL)

Arguments

arm	column name which will contain the subject's treatment group
has.event	column name which will contain whether the subject has an event (1) or not(0)
time	column name of censoring/event time
Id	column name of subject Id
DCO.time	column name of the time at which the subject would have been censored had they not had an event before data cut off
to.impute	column name of the logical column as to whether events should be imputed
censor.type	column name of the column containing the reason for censoring, 0=had event, 1=non-administrative censoring 2=administrative censoring – only subjects with 1 in this column count as having an ‘event’ in the Cox model for censoring (optionally used – if not used then all subjects who are censored are used)

Value

A list contain the given arguments

InformativeCensoring documentation built on June 7, 2023, 6:09 p.m.