Epi (version 0.6.2)

Lexis: Split follow-up time in cohort studies.

Description

For cohort input data the follow-up time is chopped in pieces along several time scales, and a dataframe of follow-up intervals is returned. Entry and exit times are assumed to be in the same timescale (the input time scale).

Usage

Lexis( entry = 0,
        exit,
        fail,
      origin = 0,
       scale = 1,
      breaks,
     include = NULL,
        data = NULL )

Arguments

entry

Date of entry on the input timescale. Numerical variable.

exit

Date of exit on the input timescale. Numerical variable.

fail

Failure indicator.

origin

Origin of the output timescale(s) on the input timescale. If for example the input timescale is calendar time and the output timescale is (current) age, the the origin is date of birth. If more than one timescale is used for splitting time

scale

Scale of the output timescale(s) relative to the input timescale. Elements of the list must be named and have the same names as those in origin and breaks.

breaks

Points on the output scale where the follow-up is cut. If more than one timescale is used for splitting time this is a list. Elements of the list must be named and must have the same names as those in origin and scale

include

List of variables to carry unchanged from the original dataframe to the output dataframe.

data

Dataframe in which to interpret the arguments.

Value

• A dataframe with one row per follow-up interval, with the following variables:
• Expand
  A numerical vector with values in 1:nrows(data), pointing at the rows of the input data frame that is expanded to the output intervals.
• Entry
  Date of entry for each interval. On the input time scale.
• Exit
  Date of exit for each interval. On the input time scale.
• Fail
  Exit status for each interval. Coded 0 for censoring, for the last follow-up interval for each person it takes the value of fail.
• Time
  If origin, scale or breaks were given as vectors this gives the left endpoints of the intervals on the output scale. If origin, scale or breaks, were given as lists, there is no variable Time in the dataframe, instead variables with the same names as the list elements of these will be in the dataframe. The variables have values corresponding to the left endpoints of the intervals on the respective output time scales.
• Finally, variables given in the argument include, values replicated across all intervals from each individual.

Details

The data is assumed to be a dataframe describing the follow-up of a cohort, giving entry and exit time (on the input timescale) for each individual as well as the exit status (failure status, fail). The purpose of the function is to split each individual's follow-up time along a number of timescales for example age, calendar time, time since entry etc. Any follow-up time before the first break on any timescale or after the last break on any of these timescales (the output timescales) is discarded. NOTE: If a person has his/her exit before the first break or his entry after the last break on any of the timescales the function will crash.

References

This function has approximately the same functionality as stsplit in Stata and the SAS-macro %Lexis (http://www.biostat.ku.dk/~bxc/Lexis/Lexis.sas). It has been attempted to keep argument names similar between the three functions.

See Also

Lexis.diagram

Examples

# A small bogus cohort
#
xcoh <- structure( list( id = c("A", "B", "C"),
                      birth = c("14/07/1952", "01/04/1954", "10/06/1987"),
                      entry = c("04/08/1965", "08/09/1972", "23/12/1991"),
                       exit = c("27/06/1997", "23/05/1995", "24/07/1998"),
                       fail = c(1, 0, 1) ),
                     .Names = c("id", "birth", "entry", "exit", "fail"),
                  row.names = c("1", "2", "3"),
                      class = "data.frame" )

# Convert the character dates into numerical variables (fractional years)
#
xcoh$bt <- cal.yr( xcoh$birth, format="%d/%m/%Y" )
xcoh$en <- cal.yr( xcoh$entry, format="%d/%m/%Y" )
xcoh$ex <- cal.yr( xcoh$exit , format="%d/%m/%Y" )

# See how it looks
#
xcoh 

# Split time along one time-axis
#
Lexis( entry = en,
        exit = ex,
        fail = fail,
       scale = 1,
      origin = bt,
      breaks = seq( 5, 40, 5 ),
     include = list( bt, en, ex, id ),
        data = xcoh )

# Split time along two time-axes
#
( x2 <- 
Lexis( entry = en,
        exit = ex,
        fail = fail,
       scale = 1,
      origin = list( per=0,                 age=bt          ),
      breaks = list( per=seq(1900,2000,10), age=seq(0,80,5) ),
     include = list( bt, en, ex, id ),
        data = xcoh ) )

# Tabulate the cases and the person-years
#
tapply( x2$Fail, list( x2$age, x2$per ), sum )
tapply( x2$Exit - x2$Entry, list( x2$age, x2$per ), sum )