I am conducting a retrospective study where I have a cohort of cases who underwent the same surgical procedure. The primary outcome of the study is the recurrence incidence rate during a follow up period of up to seven years. The risk of recurrence is known to be highest during the first year and then decrease over time.
I am investigating how a specific event during follow-up influence the risk of recurrence. I have identified 237 cases with the specific event (P) (group A), and matched this group 1:3 (based on other known risk factors) with cases without the specific event (group B).
Overall recurrence rate:
Group A: 43/237 = 18.1% Group B: 78/711 = 11.0%
Thus, P seems to effect the recurrence rate. However, in group A, 19 of the recurrences actually happened prior to P, and thus these 19 recurrences can't be contributed to the effect of P.
Therefore, I fitted an Extended Cox PH regression using the survival-package in R as follows:
data <- read.csv2(file="Dataset.csv", header=T, sep=";", dec=",") sdata <- tmerge(data, data, id=1:nrow(data),death = event(ftime, Recurrence), P = tdc(Ptime)) ftime = total days of follow up, Recurrence = 0/1, P = the specific event (0/1), and Ptime = days from start to P (NA if P=0). Call: coxph(formula = Surv(tstart, tstop, death) ~ P, data = sdata) coef exp(coef) se(coef) z p P -0.552 0.576 0.23 -2.4 0.017 Likelihood ratio test=6.35 on 1 df, p=0.0118 n= 1165, number of events= 121
This model reports that those who have experienced P are less likely to have a recurrence. However, this must be due to P occurring after a median of ~ 1 year after start, and the model thus simply reports the reduced risk of a recurrence, if you have not failed until then.
Is it possible to fit a model that take this into account?