R: Generate a nested case-control study

ccwc {Epi} | R Documentation

## Generate a nested case-control study

### 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

    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

    #
    # 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)

[Package Epi version 2.42 Index]