Epi (version 0.6.2)

icut: Function to cut the follow-up in cohort at a point in time.

Description

The follow up time from enter to exit is classified as to wheter it is before cut (Time=0) or after Time=1). If cut is between enter and exit, the follow-up is split in two intervals, the first gets the value cens for the status.

Usage

icut( enter, exit, cut, fail = 0, cens.value = 0,
      data = data.frame(enter, exit, fail, cut),
      Expand = 1:nrow( data ),
      na.cut = Inf )

Arguments

enter

Date of entry. Numerical vector.

exit

Date of exit. Numerical vector.

fail

Indicator if exit status.

cens.value

Value to use for censoring status.

cut

Date where to cut follow-up. Numerical vector.

data

Dataframe of variables to carry over to the output dataframe.

Expand

Variable identifying original records.

na.cut

What value should be assigned to missing values of the cutpoint. Defaults to Inf, so the inetrmediate event is considered not to have occcurred. If set to -Inf, all persons with missing cut are conside

Value

• A data frame with one row per interval of follow up and columns given in the data argument, preceded by the columns:
• Expand
  Identification of the rows from the input dataframe.
• Enter
  Entry date for the interval.
• Exit
  Exit date for the interval.
• Fail
  Failure indicator for the interval.
• Time
  Indicator variable for intervals after cut.

Details

The purpose of this function is to divide follow-up into pre- and post some intermediate event like recurrence of disease, thus enabling Follow-up for persons with a recurrence date (cut) will be split in two, with indication (in Time) of what is pre and what is post recurrence. This is typically what precedes a survival analysis where recurrence is modelled as a time-dependent variable.

See Also

Lexis, isec, fcut1, fcut, ex1

Examples

one <- round( runif( 15, 0, 15 ), 1 )
two <- round( runif( 15, 0, 15 ), 1 )
doe <- pmin( one, two )
dox <- pmax( one, two )
# Goofy data rows to test possibly odd behaviour
doe[1:3] <- dox[1:3] <- 8
dox[2] <- 6
dox[3] <- 7.5
# Some failure indicators
fail <- sample( 0:1, 15, replace=TRUE, prob=c(0.7,0.3) )
# So what have we got
data.frame( doe, dox, fail )
# Cut follow-up at 5
icut( doe, dox, fail, cut=5 )