R help - Apr 2013 - Comparing two different 'survival' events for the same subject using survdiff?

I have a dataset which for the sake of simplicity has two endpoints.  We would
like to test if two different end-points have the same eventual meaning.  To try
and take an example that people might understand better:

Lets assume we had a group of subjects who all received a treatment.  The could
stop treatment for any reason (side effects, treatment stops working etc). 
Getting that data is very easy.  Measuring if treatment stops working is very
hard to capture... so we would like to test if duration on treatment (easy) is
the same as time to treatment failure (hard).

My data might look like this:

A = c(9.77,  0.43,  0.03,  3.50,  7.07,  6.57,  8.57,  2.30,  6.17,  3.27, 
2.57,  0.77)
B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)  # 1 = yes (censored)
C = c( 9.80,  0.43,  5.93,  8.43,  6.80,  2.60,  8.93,  8.37, 12.23,  5.83,
13.17,  0.77)
D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) # 1 = yes (censored)
myData = data.frame (TimeOnTx = A, StillOnTx = B, TimeToFailure = C, NotFailed =
D)

We can do a survival analysis on those individually:
OnTxFit = survfit (Surv ( TimeOnTx, StillOnTx==0 ) ~ 1 , data = myData)

FailedFit = survfit (Surv ( TimeToFailure , NotFailed==0 ) ~ 1 , data = myData)

plot(OnTxFit)
lines(OnTxFit)

But how can I do a survdiff type of comparison between the two?  Do I have to
restructure the data so that Time's are all in one column, Event in another
and then a Group to indicate what type of event it is?  Seems a complex way to
do it (especially as the dataset is of course more complex than I've just
shown)... so I thought maybe I'm missing something...



********************************************************************************************************************

This message may contain confidential information. If yo...{{dropped:19}}

It isn't that complex:

myDataLong <- data.frame(Time=c(A, C), Censored=c(B, D), group=rep(0:1,
times=c(length(A), length(C))))
Fit = survfit(Surv(Time, Censored==0) ~ group, data=myDataLong)
plot(Fit, col=1:2)
survdiff(Surv(Time, Censored==0) ~ group, data=myDataLong)


However, your approach (a 'wide' data frame) suggests that there are
equal numbers in the two survival studies.  Are they even the same people?  Is
it even the same study?  If so, this is a competing risks question and would
have to be approached differently.

And, of course, absence of evidence is not evidence of absence.  Failing to
reject the null hypothesis that the distributions are different is not proof
that the distributions are equal.

Chris


-----Original Message-----
From: Polwart Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST)
[mailto:calum.polwart at nhs.net]
Sent: Monday, April 29, 2013 4:48 AM
To: r-help at r-project.org
Subject: [R] Comparing two different 'survival' events for the same
subject using survdiff?

I have a dataset which for the sake of simplicity has two endpoints.  We would
like to test if two different end-points have the same eventual meaning.  To try
and take an example that people might understand better:

Lets assume we had a group of subjects who all received a treatment.  The could
stop treatment for any reason (side effects, treatment stops working etc). 
Getting that data is very easy.  Measuring if treatment stops working is very
hard to capture... so we would like to test if duration on treatment (easy) is
the same as time to treatment failure (hard).

My data might look like this:

A = c(9.77,  0.43,  0.03,  3.50,  7.07,  6.57,  8.57,  2.30,  6.17,  3.27, 
2.57,  0.77) B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)  # 1 = yes (censored) C =
c( 9.80,  0.43,  5.93,  8.43,  6.80,  2.60,  8.93,  8.37, 12.23,  5.83, 13.17, 
0.77) D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) # 1 = yes (censored) myData =
data.frame (TimeOnTx = A, StillOnTx = B, TimeToFailure = C, NotFailed = D)

We can do a survival analysis on those individually:
OnTxFit = survfit (Surv ( TimeOnTx, StillOnTx==0 ) ~ 1 , data = myData)

FailedFit = survfit (Surv ( TimeToFailure , NotFailed==0 ) ~ 1 , data = myData)

plot(OnTxFit)
lines(OnTxFit)

But how can I do a survdiff type of comparison between the two?  Do I have to
restructure the data so that Time's are all in one column, Event in another
and then a Group to indicate what type of event it is?  Seems a complex way to
do it (especially as the dataset is of course more complex than I've just
shown)... so I thought maybe I'm missing something...



********************************************************************************************************************

This message may contain confidential information. If yo...{{dropped:7}}

> It isn't that complex:
>
> myDataLong <- data.frame(Time=c(A, C), Censored=c(B, D), group=rep(0:1,
times=c(length(A), length(C))))
> Fit = survfit(Surv(Time, Censored==0) ~ group, data=myDataLong)
> plot(Fit, col=1:2)
> survdiff(Surv(Time, Censored==0) ~ group, data=myDataLong)
Yes - for the example its not complex - but once we get down to having more data
columns I think it may...  Maybe I ignore those and just build
'myDataLong' for this specific test.
> However, your approach (a 'wide' data frame) suggests that there
are equal numbers in the two survival
> studies.  Are they even the same people?  Is it even the same study?  If
so, this is a competing risks question
> and would have to be approached differently.
Yes its the same patients. The two events are technically independant of each
other but the hope is that the easier outcome measure would predict the other...
I'm not familliar with competing risks and so will have to read up on it but
it isn't a scenario where A or B happens, A happens and B happens and you
might expect A happened because B happened...
> And, of course, absence of evidence is not evidence of absence.  Failing to
reject the null hypothesis that the
> distributions are different is not proof that the distributions are equal.
Yes absolutely - however I'm half expecting to detect a difference and so
then dismiss using A as a surrogate of B...

Thanks



-----Original Message-----
From: Polwart Calum (COUNTY DURHAM AND DARLINGTON NHS FOUNDATION TRUST)
[mailto:calum.polwart at nhs.net]
Sent: Monday, April 29, 2013 4:48 AM
To: r-help at r-project.org
Subject: [R] Comparing two different 'survival' events for the same
subject using survdiff?

I have a dataset which for the sake of simplicity has two endpoints.  We would
like to test if two different end-points have the same eventual meaning.  To try
and take an example that people might understand better:

Lets assume we had a group of subjects who all received a treatment.  The could
stop treatment for any reason (side effects, treatment stops working etc). 
Getting that data is very easy.  Measuring if treatment stops working is very
hard to capture... so we would like to test if duration on treatment (easy) is
the same as time to treatment failure (hard).

My data might look like this:

A = c(9.77,  0.43,  0.03,  3.50,  7.07,  6.57,  8.57,  2.30,  6.17,  3.27, 
2.57,  0.77) B = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1)  # 1 = yes (censored) C =
c( 9.80,  0.43,  5.93,  8.43,  6.80,  2.60,  8.93,  8.37, 12.23,  5.83, 13.17, 
0.77) D = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1) # 1 = yes (censored) myData =
data.frame (TimeOnTx = A, StillOnTx = B, TimeToFailure = C, NotFailed = D)

We can do a survival analysis on those individually:
OnTxFit = survfit (Surv ( TimeOnTx, StillOnTx==0 ) ~ 1 , data = myData)

FailedFit = survfit (Surv ( TimeToFailure , NotFailed==0 ) ~ 1 , data = myData)

plot(OnTxFit)
lines(OnTxFit)

But how can I do a survdiff type of comparison between the two?  Do I have to
restructure the data so that Time's are all in one column, Event in another
and then a Group to indicate what type of event it is?  Seems a complex way to
do it (especially as the dataset is of course more complex than I've just
shown)... so I thought maybe I'm missing something...



********************************************************************************************************************

This message may contain confidential information. If yo...{{dropped:29}}

-----Original Message-----

I have a dataset which for the sake of simplicity has two endpoints. We would
like to test
if two different end-points have the same eventual meaning. To try and take an
example
that people might understand better:

Lets assume we had a group of subjects who all received a treatment. The could
stop
treatment for any reason (side effects, treatment stops working etc). Getting
that data is
very easy. Measuring if treatment stops working is very hard to capture... so we
would
like to test if duration on treatment (easy) is the same as time to treatment
failure (hard).

--- End ----

The problem you describe is known as "surrogate endpoints" and
addressing it is harder
than you think. You will need to look in the literature to gain an understanding
of the
issues before you proceed. Your question is an important one and lots of folks
have
thought about it more deeply than I.

Cohn JN (2004). "Introduction to Surrogate Markers". Circulation
(American Heart
Association) 109 (25 Suppl 1): IV20?1. doi:10.1161/01.CIR.0000133441.05780.1d

Fleming T, David D. Surrogate End Points in Clinical Trials: Are We Being
Misled? Ann
Intern Med. 1996 Oct 1;125(7):605-13

Terry T.