How to properly represent right censoring in the data for Surv? I have survival data on patients, coming from a clinic's datawarehouse. I want to do a survival analysis. The timeframe starts on the day a patient gets a certain examination, and ends 730 days (two years) later.
The data comes in with timestamps.




patient
examination.date
date.of.death
date.of.last.contact
comment




A
01.01.2000
NA
01.01.2010
Alive


B
01.02.2000
NA
01.03.2000
Lost to followup


C
01.03.2000
01.04.2000
01.04.2000
Died during study


D
01.04.2000
01.04.2010
01.04.2010
Died after study




So far, I have transformed the data to have the length of actual survival times, and an event column.




patient
surv.time
event
comment




A
NA
FALSE
Alive


B
NA
FALSE
Lost to followup


C
31
TRUE
Died during study


D
3653
FALSE
Died after study




Surv seems to take no argument for a time of last contact, so I am quite certain that I should at least make a correction for patient B, who was lost to followup, and enter 28 there (the duration between examination and last contact). Patient C is also quite clear: died during the study, and the duration until death is entered.
But my question is about the other two patients:

*

*should I enter 730 (the study duration) for patients like A, who lived beyond the end of the study?

*should I enter 730 for patients like D, who died after the study timeframe? Note that I have already set the event column to FALSE for these cases.

I read some examples on how to use the survival package, but they used very simplified cases with already-prepared datasets.
 A: Patient A should have time = 730 and event = FALSE. They were censored at the end of the study. EDIT: EdM makes a good point that this depends on whether Patient A was actually studied for a longer period of time, like Patient D apparently was. If A was followed after the original 730 days, then we have to make the same decision for A as we make for D below, either to censor at 730 days or at the time when we actually stopped observing them.
Patient D's data is very unusual. In typical studies there are three possibilities:

*

*The patient died during the study (event = TRUE, with event time).

*The patient was lost to follow-up (event = FALSE, at last observed time).

*The patient was alive at the end of the study (event = FALSE, last observed time is the end of the study).

It seems from your description that you kept observing patient D long after the "study" ended, which doesn't make sense. If the study is over, how are you still collecting data? But, given that you apparently have this data I think there are two ways to proceed.

*

*Enter the data as though the study really did continue until they died, time = 3653 and event = TRUE


*Enter the data as though they were censored at the end of the study, time = 730 and event = FALSE
If you're using a Cox proportional hazards model and patient D is the only one who is alive and uncensored after time 730 then the results will be identical for the two methods. If you're using a parametric regression or estimating survival curves then I think the results will depend on what you choose.
