Generate a nested case-control study

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Description

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.

Usage

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ccwc( entry=0, exit, fail, origin=0, controls=1, match=list(),
      include=list(), data=NULL, silent=FALSE )

Arguments

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.

Value

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

Author(s)

David Clayton

References

Clayton and Hills, Statistical Models in Epidemiology, Oxford University Press, Oxford:1993.

See Also

Lexis

Examples

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#
# 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)

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