| clogit {survival} | R Documentation |
Estimates a logistic regression model by maximising the conditional
likelihood. Uses a model formula of the form
case.status~exposure+strata(matched.set).
The default is to use the exact conditional likelihood, a commonly
used approximate conditional likelihood is provided for compatibility
with older software.
clogit(formula, data, weights, subset, na.action,
method=c("exact", "approximate", "efron", "breslow"),
...)
formula |
Model formula |
data |
data frame |
weights |
optional, names the variable containing case weights |
subset |
optional, subset the data |
na.action |
optional na.action argument. By default the
global option |
method |
use the correct (exact) calculation in the conditional likelihood or one of the approximations |
... |
optional arguments, which will be passed to
|
A stratified Cox model with time=constant, status of 1=case 0=control, and using the exact partial likelihood has the same likelihood formula as a conditional logistic regression. The clogit routine creates the necessary dummy variable of times (all 1) and then calls coxph.
The computation of the exact partial likelihood can be very slow, however. If a particular strata had say 10 events out of 20 subjects we have to add up a denominator that involves all possible ways of choosing 10 out of 20, which is 20!/(10! 10!) = 184756 terms. Gail et al describe a fast recursion method, which largely ameleorates this; it was incorporated into version 2.36-11 of the survival package. (Most of the time conditional logistic modeling is applied data with 1 case + k controls per set, however, which involves choosing 1 out of k and the computational issue above does not arise.) The 'appoximate' option maps to the Breslow approximation for historical reasons.
It is not clear how case weights should be handled. For instance if there are two deaths in a strata, one with weight=1 and one with weight=2, should the likelihood calculation consider all subsets of size 2 or all subsets of size 3? Consequently, case weights are ignored by the routine.
An object of class "clogit", which is a wrapper for a
"coxph" object.
Michell H Gail, Jay H Lubin and Lawrence V Rubinstein. Likelihood calculations for matched case-control studies and survival studies with tied death times. Biometrika 68:703-707, 1980.
Thomas Lumley
## Not run: clogit(case ~ spontaneous + induced + strata(stratum), data=infert)
# A multinomial response recoded to use clogit
# The revised data set has one copy per possible outcome level, with new
# variable tocc = target occupation for this copy, and case = whether
# that is the actual outcome for each subject.
# See the catspec package for more details.
resp <- levels(logan$occupation)
n <- nrow(logan)
indx <- rep(1:n, length(resp))
logan2 <- data.frame(logan[indx,],
id = indx,
tocc = factor(rep(resp, each=n)))
logan2$case <- (logan2$occupation == logan2$tocc)
clogit(case ~ tocc + tocc:education + strata(id), logan2)