I would like to determine the probability of an event at a specific
timepoint given the linear predictor of a given Cox model. For
instance, assume that I fit the following model:
data(pbc)
fit <- coxph(Surv(time, status)~ age, data=pbc)
To extract the value of the linear predictor for each patient in the
dataset:
prd <- predict(fit, newdata=pbc, type="lp")
However, what I am really interested in is the predicted probability
from the Cox model that an individual will experience an event by some
time t (say 2000 days in this example). To my knowledge, calculating
this probability requires knowing one of the 3 baseline functions-the
survivor function or the hazard function or the cumulative hazard
function-estimated from the Cox model. I would like to know :
1) How do I extract the correct baseline hazard so that I can
predict the individual probablility of failure at 2000 days, given a
particular patient covariate pattern (in this case, a particular
patient's age? Is the baseline hazard for the average patient
covariates the ideal one?
2) Is there a better way of directly extracting these probabilities?
Brant Inman
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