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st: R: Problem with Left Truncation


From   "Carlo Lazzaro" <carlo.lazzaro@tin.it>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: R: Problem with Left Truncation
Date   Fri, 23 Oct 2009 10:56:58 +0200

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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