Bob Green
2014-Jun-10 06:31 UTC
[R] Recurrent analysis survival analysis data format question
Hello,
I'm hoping for advice regarding how to set up a recurrent event
survival analysis data file. My data consists of people released from
custody, with survival time being measured as days before re
imprisonment or end of the study. In the example below, id 5155 is
released 5 times and jailed five times. All events are therefore
true. Daysfree is the difference in days between release and return
to custody. Id 7155 is released 3 times and only re-imprisoned
twice, so the third event value is false.
id <- c(5155, 5155,5155,5155, 7155, 7155,7155)
Release <- c("29/10/10","9/01/11", "25/03/12",
"15/10/13", "9/01/10",
"16/12/12","29/10/13")
JailNew <- c("1/12/10","01/12/11", "27/09/12",
"24/01/14",
"22/09/12","24/01/12","24/01/14")
DaysFree <- c(24,234,134,74,709,29,64)
Event <- c("true", "true", "true",
"true", "true", "true", "false" )
DF1<- data.frame(id, Release, JailNew, DaysFree, Event)
DF1
After speaking to a statistician today I'm not sure if I my method
of formatting the data is correct. Should all time intervals be
included, not just the period from release to event/end of study
period. Currently period imprisoned is not counted. For example,
for id 5155, would I also include 1/12/10 - 9/01/11 etc, which would
be FALSE for event and have a duration of 39 days; and then include
all the other similar intervals as well. The statistican thought
including this additional information more closely resembled the
bladder1 data in the Survival package.
Any assistance is appreciated,
Regards
Bob
Andrews, Chris
2014-Jun-10 19:02 UTC
[R] Recurrent analysis survival analysis data format question
I wouldn't consider the person at risk for re incarceration if he is
currently imprisoned. So I wouldn't use those intervals as part of the
response variable. Perhaps time in custody would be a covariate used to model
the time until re incarceration. One variable that is commonly needed for
analysis of recurrent data is the number of previous events. It can be used to,
e.g., stratify.
Chris
-----Original Message-----
From: Bob Green [mailto:bgreen at dyson.brisnet.org.au]
Sent: Tuesday, June 10, 2014 2:32 AM
To: r-help at r-project.org
Subject: [R] Recurrent analysis survival analysis data format question
Hello,
I'm hoping for advice regarding how to set up a recurrent event
survival analysis data file. My data consists of people released from
custody, with survival time being measured as days before re
imprisonment or end of the study. In the example below, id 5155 is
released 5 times and jailed five times. All events are therefore
true. Daysfree is the difference in days between release and return
to custody. Id 7155 is released 3 times and only re-imprisoned
twice, so the third event value is false.
id <- c(5155, 5155,5155,5155, 7155, 7155,7155)
Release <- c("29/10/10","9/01/11", "25/03/12",
"15/10/13", "9/01/10",
"16/12/12","29/10/13")
JailNew <- c("1/12/10","01/12/11", "27/09/12",
"24/01/14",
"22/09/12","24/01/12","24/01/14")
DaysFree <- c(24,234,134,74,709,29,64)
Event <- c("true", "true", "true",
"true", "true", "true", "false" )
DF1<- data.frame(id, Release, JailNew, DaysFree, Event)
DF1
After speaking to a statistician today I'm not sure if I my method
of formatting the data is correct. Should all time intervals be
included, not just the period from release to event/end of study
period. Currently period imprisoned is not counted. For example,
for id 5155, would I also include 1/12/10 - 9/01/11 etc, which would
be FALSE for event and have a duration of 39 days; and then include
all the other similar intervals as well. The statistican thought
including this additional information more closely resembled the
bladder1 data in the Survival package.
Any assistance is appreciated,
Regards
Bob
**********************************************************
Electronic Mail is not secure, may not be read every day, and should not be used
for urgent or sensitive issues