30000 patients all have one diagnosis in common, 3000 of them have been exposed to a potential risk factor before the common diagnosis. 1500 patients develop a malignancy after the common diagnosis.
Ratios of patients that developed a malignancy between those exposed to risk factor and those who were not are similar (not statistically different using naive 2-sample test for equality of proportions):
Exposed Not exposed Malignancy 7,3% 6,7% Time to malignancy 2yrs 4yrs
But there is a very significantly shorter time from common diagnosis to malignancy if the patient was exposed.
A Cox Proportional-Hazards Regression was performed where patients were followed up until malignancy, death or end of study period. The event was developing malignancy and adjusting factors were age, sex and time of common diagnosis. A check of the proportionality assumtion was performed and age did not adhere so a repeated model was performed with age stratification.
The result was that exposure had HR of 1.3 and a p-value of 0.001.
The same data was also prepared for a competing risk analysis using the crrs() function in R. A status code was encoded, where censored=0, malignancy=1 and death=2. The model was stratified for age as in the Cox regression.
x <- cbind(factor2ind(exposure, "False"), factor2ind(sex, "M"), factor2ind(timeOfDiagnosis, "1980-1990")) res <- crrs(ftime = stime, fstatus = status, cov1 = x, strata = age, failcode = 1)
The result was that exposure had a SHR og 1.09 and a p-value of 0.32.
I'm new to using the competing risk analysis so I'm not sure what to conclude. Is the sample code on the right track or am I falling into a common pitfall? The Cox Proportional-Hazards Regression model has been used frequently and published in the domain of question and it is very significant. But on the other hand a patient can not develop a malignancy if he has already died which might constitute a competing risk.
Many thanks for any and all replies!