I am running an analysis where I have 2500 cases and 2500 controls. The cases have disease A, and the controls do not. I am trying to see if having disease A increases the odds of various diseases. For the sake of simplicity, we can focus on one disease, call it disease B.
D = 1 if disease B present, 0 otherwise
E = 1 if disease A present, 0 otherwise
I am also including in the model a measure of healthcare utilization.
F is a positive integer proportional to an individual's utilization of healthcare.
I am running the logistic regression model as such in R:
glm(D ~ E + F, family = "binomial")
Now, this works fine.
However, when I try to run conditional logistic regression, it gives me an error:
library(survival) clogit(D ~ E + F, strata(matched.pairs)) Error in fitter(X, Y, strats, offset, init, control, weights = weights, : NA/NaN/Inf in foreign function call (arg 5) In addition: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, : Ran out of iterations and did not converge
I have tried different strata, including dividing the individuals into quantile bins based on F. It does not seem to change anything. (note: pairs are matched on age, gender, race, and F)
This occurs only when I run it on a larger sample size. I ran this same analysis on a sample size of 200 (100 cases and 100 controls) and it worked fine. When I use a sample size of 5000, I get the above error.
I also made sure that at least 10 cases and 10 controls had the disease in question (disease B, for this example).
I am not sure why logistic regression runs fine when conditional logistic regression does not. Can anyone offer me any advice?