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st: R: R: RE: Problem with Left Truncation


From   "Carlo Lazzaro" <carlo.lazzaro@tin.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: R: RE: Problem with Left Truncation
Date   Sat, 24 Oct 2009 16:35:22 +0200

Dear Elaine,
re-thinking over the debate started by your yesterday's interesting thread,
I would like to add two more comments:
- assuming that some (or all) observations in your dataset are left
truncated, Stata can easily deal with it simply by increasing t0 to tx,
where 0 is the onset of risk-time and x is the time when the patient is
enrolled in your study and starts to be under your observation. Under
left-truncation, x>0. This topic is clearly covered in Cleves MA, Gould WG,
Gutierrez R. An Introduction To Survival Analysis Using Stata. Revised
edition. College Station: StataPress, 2004: 35 (and surely in Mario Cleves,
William Gould, Roberto Gutierrez, and Yulia Marchenko (2008)
"An Introduction to Survival Analysis using Stata". College Station: Stata
Press., but I own 2004 edition only);

- about right truncation, which is conceptually different from right
censoring but rather difficult to differentiate from it in practice: can't
you perform a sort of "scenario(s)sensitivity analysis" figuring out what
happens to your results if you assume that a given percentage(s) of your "X
treated" patients is right censored (i.e., still alive) by the end of 2008? 

Kind Regards and enjoy your W_E,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Carlo Lazzaro
Inviato: venerdì 23 ottobre 2009 17.41
A: statalist@hsphsun2.harvard.edu
Cc: 'Liu, Elaine '
Oggetto: st: R: RE: Problem with Left Truncation

Dear Elaine,
now I am clearer with your problem. Unfortunately, I have never come across
such a tricky issue, nor I can figure out how to tackle it with Stata. 
Alan and Antoine both gave relevant hints. I can only recommend you once
more the following textbook
Klein JP, Moeschberger ML. Survival Analysis. Techniques for Censored and
Truncated Data. Second Edition. Berlin: Springer, 2003.

All the best for your research project and Kind Regards,
Carlo

-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Liu, Elaine 
Inviato: venerdì 23 ottobre 2009 15.45
A: Carlo Lazzaro; statalist@hsphsun2.harvard.edu
Oggetto: st: RE: Problem with Left Truncation

Hi Carlo, 

Thank you for your reply. Sorry, I didn't describe the problem clearly. 
I understand the estimation can be done, but my worry is that without any
treatment, the estimated coefficient would be biased. 

X is an indicator variable and it changes over time. 
Let's suppose X=1 can prolong people's life (think of a drug). 
And different patients are treated with X=1 at different time (they will
only be treated drug when they are found to be at risk).
My dataset is right truncated at 2008. 

I can observe everyone who have failed in the past when there was no drug.
I'll observe all patients who are treated (X=1) but failed by 2008. 
However, I don't observe any patients who are at risk with X=1 and live
beyond 2008. 
In this case, estimating the impact of X, would most likely be estimated
downward, since we don't observe the on-going cases. 
This is probably not called "left truncation", but I can't find a better
term describing the problem. 

Is there way, we can make an adjustment to the coefficient to correct the
bias in Stata? 
Or is there any paper that addresses this issue?

Thank you all. 

I just saw Antoine and Alan's replies after I completed the email.  

Elaine
 




-----Original Message-----
From: Carlo Lazzaro [mailto:carlo.lazzaro@tin.it] 
Sent: Friday, October 23, 2009 3:57 AM
To: statalist@hsphsun2.harvard.edu
Cc: Liu, Elaine 
Subject: R: Problem with Left Truncation

Dear Elaine,
Please find beneath the following point-to-point comments about your query:

<We are doing survival analysis, but unlike other dataset, our dataset only
includes observations that have failed.>

I would not be concerned about all failure=1; how long patient takes to
failure (failure time (tn)- risk onset (t0)) it's the relevant issue.  

<Once it fails, the dataset would provide detail information on the date one
starts to be at risk, when it fails, some other individual
characteristics(X') at the entry time.>

My suspect is that you are dealing with a retrospective survival analysis
(ie, your dataset moves from death to risk onset).
If you have both t(0) and t(n), what's the matter? You have simply to -
stset- your data before performing survival analysis.

<Our goal is to estimate the impact of X on the probability of survival>.

Hence, the choice is between semiparametric (Cox regression) -stcox- and
parametric -streg- survival models, provided that your dataset fulfills some
requirements (eg. proportional hazard assumption in Cox model).


For further details on survival analysis topics, I will recommend you to
take a thorough look at:

Klein JP, Moeschberger ML. Survival Analysis. Techniques for Censored and
Truncated Data. Second Edition. Berlin: Springer, 2003.

Cleves MA, Gould WG, Gutierrez R. An Introduction To Survival Analysis Using
Stata. Revised edition. College Station: StataPress, 2004; 

Mario Cleves, William Gould, Roberto Gutierrez, and Yulia Marchenko (2008)
"An Introduction to Survival Analysis using Stata". College Station: Stata
Press.

[ST] Stata manual. Survival analysis and epidemiological table. Release 9

Two other relevant contributors of the Statalist - Maarten Buis
(http://home.fsw.vu.nl/m.buis/)  and Stephen Jenkins
(http://www.iser.essex.ac.uk/teaching/degree/stephenj/ec968/index.php.)
published really interesting papers as well as teaching-notes on the topics
you are interested in.

HTH and Kind Regards,

Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Liu, Elaine
Inviato: giovedì 22 ottobre 2009 20.26
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Problem with Left Truncation

Dear Statalist readers,

I have a question regarding the use of survival analysis with a problem
similar to left truncation.

We are doing survival analysis, but unlike other dataset, our dataset only
includes observations that have failed. 

Once it fails, the dataset would provide detail information on the date one
starts to be at risk, when it fails, some other individual
characteristics(X') at the entry time. 

Our goal is to estimate the impact of X on the probability of survival. 

I think it's a common problem in medicine (for example if you are estimating
the probability some event causes death but you only observe people after
they died)

I have checked several posts in the archive and the textbook solution to
left truncation, but they don't seem to address the problem. 

This is my first time posting in this community. Let me know if more
information is needed. 

Thank you very much.

Elaine 


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