I have a dataset which for the sake of simplicity has two endpoints. We would
like to test if two different end-points have the same eventual meaning. To
try and take an example that people might understand better:
Lets assume we had a group of subjects who all received a treatment. The could
stop treatment for any reason (side effects, treatment stops working etc).
Getting that data is very easy. Measuring if treatment stops working is very
hard to capture... so we would like to test if duration on treatment (easy) is
the same as time to treatment failure (hard).
My data might look like this:
A = c(9.77, 0.43, 0.03, 3.50, 7.07, 6.57, 8.57, 2.30, 6.17, 3.27,
2.57, 0.77)
B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) # 1 = yes (censored)
C = c( 9.80, 0.43, 5.93, 8.43, 6.80, 2.60, 8.93, 8.37, 12.23, 5.83,
13.17, 0.77)
D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) # 1 = yes (censored)
myData = data.frame (TimeOnTx = A, StillOnTx = B, TimeToFailure = C, NotFailed
= D)
We can do a survival analysis on those individually:
OnTxFit = survfit (Surv ( TimeOnTx, StillOnTx==0 ) ~ 1 , data = myData)
FailedFit = survfit (Surv ( TimeToFailure , NotFailed==0 ) ~ 1 , data = myData)
plot(OnTxFit)
lines(OnTxFit)
But how can I do a survdiff type of comparison between the two? Do I have to
restructure the data so that Time's are all in one column, Event in another and
then a Group to indicate what type of event it is? Seems a complex way to do
it (especially as the dataset is of course more complex than I've just
shown)... so I thought maybe I'm missing something...
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