I'm a medical student trying to understand statistics(!) - so please be gentle! ;)
I'm writing an essay containing a fair amount of statistical analysis including survival analysis (Kaplan-Meier, Log-Rank and Cox regression).
I ran a Cox regression on my data trying to find out if I can find a significant difference between the deaths of patients in two groups (high risk or low risk patients).
I added several covariates to the Cox regression to control for their influence.
Risk (Dichotomous) Gender (Dichotomous) Age at operation (Integer level) Artery occlusion (Dichotomous) Artery stenosis (Dichotomous) Shunt used in operation (Dichotomous)
I removed Artery occlusion from the covariates list because its SE was extremely high (976). All other SEs are between 0,064 and 1,118. This is what I get:
B SE Wald df Sig. Exp(B) 95,0% CI for Exp(B) Lower Upper risk 2,086 1,102 3,582 1 ,058 8,049 ,928 69,773 gender -,900 ,733 1,508 1 ,220 ,407 ,097 1,710 op_age ,092 ,062 2,159 1 ,142 1,096 ,970 1,239 stenosis ,231 ,674 ,117 1 ,732 1,259 ,336 4,721 op_shunt ,965 ,689 1,964 1 ,161 2,625 ,681 10,119
I know that risk is only borderline-significant at 0,058. But besides that how do I interpret the Exp(B) value? I read an article on logistic regression (which is somewhat similar to Cox regression?) where the Exp(B) value was interpreted as: "Being in the high-risk group includes an 8-fold increase in possibility of the outcome," which in this case is death. Can I say that my high-risk patients are 8 times as likely to die earlier than ... what?
Please help me! ;)
By the way I'm using SPSS 18 to run the analysis.