Hello,
we have a question concerning the interpretation of the hazard ratio of a time dependent covariate.
We used the syntax of the following example of SAS
PROC PHREG: Model Using Time-Dependent Explanatory Variables :: SAS/STAT(R) 9.3 User's Guide
time = survival time, Status=1=death, Waittime = Waiting time until transplantation
proc phreg data= Heart; model Time*Status(0)= XStatus XAge XScore; where NotTyped ^= 'y'; if (WaitTime = . or Time < WaitTime) then do; XStatus=0.; XAge=0.; XScore= 0.; end; else do; XStatus= 1.0; XAge= Xpl_Age; XScore= Mismatch; end; run;
The hazard ration for xstatus is equal 0.935.
What does this mean in case of time dependency?
Does is it mean that the risk to die is lower (-6.5%) in case of status gets one, i.e. the patients gets a transplant where the time dependence is taken into account ?
Best regards,
statstats
Hello @sasstats,
First, note that the hazard ratio 0.935 for XStatus resulted from the model
model Time*Status(0)= XStatus Acc_Age;
The corresponding p-value 0.8261 indicates that the true hazard ratio might well be 1 (i.e., no difference in hazard rates due to XStatus; see the 95% confidence interval [0.513, 1.703]).
But let's assume that the model accurately describes the population, so the hazard ratio is "real." Then we would conclude that a patient with XStatus=1 (i.e., who has received a transplant) is slightly less likely to die (by a factor 0.935) than a patient
Or in other words: Comparing two sufficiently and equally large subpopulations "XStatus=0" vs. "XStatus=1" with the same distribution of variable Acc_Age, we might see 1000 deaths in the first group within a certain period of time and would expect only about 935 deaths in the parallel group of transplant recipients.
Hello @sasstats,
First, note that the hazard ratio 0.935 for XStatus resulted from the model
model Time*Status(0)= XStatus Acc_Age;
The corresponding p-value 0.8261 indicates that the true hazard ratio might well be 1 (i.e., no difference in hazard rates due to XStatus; see the 95% confidence interval [0.513, 1.703]).
But let's assume that the model accurately describes the population, so the hazard ratio is "real." Then we would conclude that a patient with XStatus=1 (i.e., who has received a transplant) is slightly less likely to die (by a factor 0.935) than a patient
Or in other words: Comparing two sufficiently and equally large subpopulations "XStatus=0" vs. "XStatus=1" with the same distribution of variable Acc_Age, we might see 1000 deaths in the first group within a certain period of time and would expect only about 935 deaths in the parallel group of transplant recipients.
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