Kaplan Meier - not for dates
Thanks for the reply. The treatment is effectively for a chronic condition - so you stay on the treatment till it stops working. We know from trials how long that should be and we know the theoretical cost of that treatment but that's based on the text book dose (patients dose reduce and delay treatment and its based on weight so variable). We've been asked to provide our national planning team with an "average" cost based on our early experiences. So we have suggested to them we might be able to get a median cost. Some patients will stay on treatment several years so it will be impossible to get an average for years. So the censored patients will be those still on treatment (the event being stopping treatment) I'll give what you've suggested a go. Thanks Calum Polwart BSc(Hons) MSc MRPharmS SPres IPres Network Pharmacist - NECN and Pharmacy Clinical Team Manager (Cancer & Aseptic Services) - CDDFT Our website has now been unlocked and updated. Should you require contacts, meeting details, publications etc, please visit us on www.cancernorth.nhs.uk
From: Lancaster, Robert (Orbitz) [ROBERT.LANCASTER at orbitz.com]
Sent: 03 November 2011 19:55
To: Polwart Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST); r-help at r-project.org
Subject: RE: Kaplan Meier - not for dates
Sent: 03 November 2011 19:55
To: Polwart Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST); r-help at r-project.org
Subject: RE: Kaplan Meier - not for dates
I think it really depends on what your event of interest is. If your event is that the patient got better and "left treatment" then I think this could work. You would have to mark as censored any patient still in treatment or any patient that stopped treatment w/o getting better (e.g. in the case of death). You would then be predicting the cost required to make the patient well enough to leave treatment. It is a little non-standard to use $ instead of time, but time is money after all.
You could set up your data frame with two columns: 1) cost 2) event/censored.
Then create your survival object:
mySurv = Surv(my_data$cost,my_data$event)
And then use survfit to create your KM curves:
myFit = survfit(mySurv~NULL)
If you have other explanatory variables that you think may influence the cost, you can of course add them to your data frame and change the formula you use in survfit. For instance, you could have some severity measure, e.g. High, Medium, Low. You could then do:
myFit = survfit(mySurv~my_data$severity)
-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Polwart Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST)
Sent: Monday, October 31, 2011 1:29 PM
To: r-help at r-project.org
Subject: [R] Kaplan Meier - not for dates
I have some data which is censored and I want to determine the median. Its actually cost data for a cohort of patients, many of whom are still on treatment and so are censored.
I can do the same sort of analysis for a survival curve and get the median survival... ...but can I just use the survival curve functions to plot an X axis that is $ rather than date? If not is there some other way to achieve this?
Thanks
Calum
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