I have a matched data set with 10,000 cases and 20,000 controls. Cases are defined as such due to a diagnosis of COPD (Chronic Obstructive Pulmonary Disease - a lung disease caused by smoking). Controls are healthy controls, matched for age and sex and region of residence. Cases and controls are not matched for socioeconomic variables, although I have that data as well. I have no other data for controls, although variables are plenty for cases. Mathching was done in the design phase of the study, and not during the analyses. to sum up, I have 10,000 cases with COPD and each individual has been matched to two age and sex matched controls.
AIMS Estimate the excess risk of developing dementia among cases, as compared to controls.
• I Use R; the "survival" and "RMS" packages.
• I plan to use Cox proportional hazards regression for relative risks and some measure of incidence to estimate absolute risks. I have the following questions:
(1) should I take the matching property into account in the Cox regressions? If that is the case, how do You recommend me doing this? Should I use the cluster()-function? the strata()-function? Or could I just ignore the matching?
(2) When calculating the incidence rate (and incidence rate ratio) do I have to adjust anything; I'm asking because mean age and proportion of women is exactly the same in cases and controls (for obvious reasons). Should I go for Poisson based incidence rates? Any other methods to consider?
I'd be very greateful for any advice on this.