Generate a nested case-control study
Given the basic outcome variables for a cohort study: the time of entry to the cohort, the time of exit and the reason for exit ("failure" or "censoring"), this function computes risk sets and generates a matched case-control study in which each case is compared with a set of controls randomly sampled from the appropriate risk set. Other variables may be matched when selecting controls.
ccwc( entry=0, exit, fail, origin=0, controls=1, match=list(),
include=list(), data=NULL, silent=FALSE )entry |
Time of entry to follow-up |
exit |
Time of exit from follow-up |
fail |
Status on exit (1=Fail, 0=Censored) |
origin |
Origin of analysis time scale |
controls |
The number of controls to be selected for each case |
match |
List of categorical variables on which to match cases and controls |
include |
List of other variables to be carried across into the case-control study |
data |
Data frame in which to look for input variables |
silent |
If FALSE, echos a . to the screen for each case-control set created; otherwise produces no output. |
The case-control study, as a dataframe containing:
Set |
case-control set number |
Map |
row number of record in input dataframe |
Time |
failure time of the case in this set |
Fail |
failure status (1=case, 0=control) |
These are followed by the matching variables, and finally by the
variables in the include list
David Clayton
Clayton and Hills, Statistical Models in Epidemiology, Oxford University Press, Oxford:1993.
#
# For the diet and heart dataset, create a nested case-control study
# using the age scale and matching on job
#
data(diet)
dietcc <- ccwc( doe, dox, chd, origin=dob, controls=2, data=diet,
include=energy, match=job)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.