I am not sure about the best design in this case.
I have a representative sample (N=15,000) of individuals with a moderately rare condition and I want to match them to controls who do not have this condition. I want to compare rates of fatal and non-fatal vascular events and all-cause mortality between cases and controls controlling for confounders (demographic, SES, and clinical characteristics). As for controls I can extract them from a random sample (N=1,000,000) which is also state representative.
In summary I want to match case and controls and follow them up for 10 years to compare rates of different outcomes.
My first thought was to perform a propensity score 1:m kernel matching (including for the matching estimator age, sex, but also vascular risk factors considering I am going to look at vascular outcomes) and compare incident outcomes. However, I read it does not perform well when you try to match selected cases to a random sample of control. Any good reference that might support the opposite?
I also thought about a nested case control design matching by age and sex and controlling in the conditional logistic regression for the baseline cardiovascular risk factor. However, in my case I would match at baseline and then follow-up my two groups over time to compare rates (or probability as in the conditional logistic regression). Would this design still be appropriate? Normally NCC look retrospectively at outcomes. Also, I am considering multiple outcomes, would it be suitable for that?
I also thought about the inverse probability weighting but I guess this approach shares majority of the weakness and strengths with the first. correct?
Any good reference would also be appreciated.