[R] How to estimate a hazard ratio using an external hazard function

[Terry Therneau]

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Hi,

I would like to compare the hazard functions of two samples using the  
Cox proportional hazards model. For sample 1 I have individual time-to- 
event data. For sample 2 I don't have individual data, but grouped  
data that allows to obtain a hazard function.

I am wondering if there is an R function that allows to obtain a  
hazard ratio of the two hazard funtions (under the proportionality  
assumption) taking into account the censoring of the data?

I am aware of survexp and survdiff functions, but I am not sure if  
that is the best way to do what I need.

Any help will be highly appreciated.

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  The functions for population expected survival are designed for just this 
problem, i.e., those that use the US death rate tables.  Under the assumption 
that the second (grouped data set) has a much larger sample size than the first 
(sample) data:
    For each subject in sample 1, compute the expected number of events for that 
subject, using the rates found in sample 2 = (time at risk) * (rate during that 
time).  For population rate tables this turns out to be a sum: the external 
rates are a function of age so one gets ......+ (# days at age 55)*(rate for 55 
year olds) + (# days at age 56)*(rate for 56 year olds) + ....  Call the result 
"expected", a vector with one element per subject.  

    Now fit a Poisson model with offset(log(expected)) as one of the predictors. 
This is a proportional hazards model, with the usual Cox baseline hazard 
lambda_0 replaced by the known hazard function from sample 2.

	Terry Therneau