I've recently started working on survival analysis, but I'm still new to the field. My dataset consists of a cohort of 50 individuals with advanced cancer for whom I have some blood quantitative measures taken at 2 to 5 (unevenly-spaced) time points, within the first three months after enrollment in the study. These subjects have then been monitored for 2-3 years and, for those who died, I have information about death date. This is how my data looks like:
IID Death Time var Age SEX 1 0 413 0.42 51.2 M 1 0 392 0.29 51.3 M 1 0 350 0.20 51.4 M 2 1 88 0.01 73.2 F 2 1 68 0.46 73.3 F 2 1 26 0.35 73.4 F
I want to identify blood parameters associated with survival time, and therefore tried to run a mixed-effect Cox regression analysis, including a clustering factor (individual id) in order to account for the repeated measures (
coxph function from survival R package):
fit <- coxph(Surv(time, death) ~ blood_variable + age + sex + cluster(iid))
I'm wondering if this a correct way to analyse the data, or there are more appropriate approaches. I've read about Cox-regression with time-dependent covariates, but I'm not sure if this is applicable to my dataset, given the irregular spacing between exposure measures, and the relatively large time difference between exposure measurement and outcome.
Thanks in advance