Dear R-Help-List,
A few days ago I asked for help simulating
case-control data. I got a great answer to help me
with my code, but I am having trouble modifying it for
1:M matched case-control data. Does anyone have any
guidance/pointers for simulating 1:M matched data?.
Thank you,
-R
> Dear R-Help-List,
>
> I was wondering if anyone had experience simulating
> case-control data in R?
I think the only simple method that allows you to
specify any arbitrary
population distribution of predictors and does not
rely on the logistic
regression model being true is to simulate cohorts and
then take a
case-control sample from each one
Eg for a case-control sample of 500 cases and 1000
controls where there
is
about a 1% cumulative incidence
1. Generate all your predictor variables for a cohort
of 50,000 people,
from any distributions you want
2. Specify the disease model. This could be logistic
logit(p(Y=1))=eta = b0+b1x1+b2x2+...
p = exp(eta)/(1+exp(eta))
or it could be anything else.
3. Now sum(p) gives the expected number of cases.
Adjust b0 so that
this
is a bit bigger than your desired number, eg 550.
4. Generate Y for the population by rbinom(50000,1,p)
5. Choose 500 cases and 1000 controls using sample().
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