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st: RE: R: Sources focused on time-varying covariates in survival analysis?


From   Hugh Robinson <hugh.robinson@umontana.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: R: Sources focused on time-varying covariates in survival analysis?
Date   Fri, 8 May 2009 15:48:14 -0600

Chelsea,

Hosmer, Lemeshow, and May (2008).  Applied Survival Analysis.  Is also a good source but may not have the  depth you're looking for.  However the textbook examples on the UCLA - ATS website are very helpful.  http://www.ats.ucla.edu/stat/stata/examples/asa2/default.htm

I'd be interested to hear if anyone knows of a source where time-varying covariates are used in a competing risks analysis.

HR

Hugh S Robinson Ph.D.
Postdoctoral Researcher
Wildlife Biology Program
College of Forestry and Conservation
University of Montana
Missoula, MT 59802
(406) 243-2931





-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu [mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Carlo Lazzaro
Sent: Friday, May 08, 2009 12:07 PM
To: statalist@hsphsun2.harvard.edu
Cc: 'Polis, Chelsea B.'
Subject: st: R: Sources focused on time-varying covariates in survival analysis?


Dear Chelsea,
Blossfeld HP, Golsch K and Rohwer G. Event History Analysis with Stata.
Lawrence Erlbaum Associates, 2007 (Chapters 6 and - perhaps -7) may be
helpful.

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 Polis, Chelsea B.
Inviato: venerdì 8 maggio 2009 19.32
A: statalist@hsphsun2.harvard.edu
Oggetto: st: Sources focused on time-varying covariates in survival
analysis?

Many thanks to all who responded!

One further question based on the readings recommended below: I do have the
Cleves,
Gould, and Gutierrez book mentioned.  In fact, I practically sleep with it
under my
pillow - it's been an absolutely invaluable guide for my dissertation.

At the same time, I find myself wishing for a reference that delves even
more deeply
into survival analyses using time-varying covariates.  Some sources address
time-varying
covariates, but seem to stop just shy of the full explanations given for
other circumstances.
Such a resource would be useful - even on these boards I have noticed that
things
like the potentially extraneous nature of tvc() function (if your data has
multiple
records per subject) has confused several people.  Perhaps it is my own
thick-headedness,
but I found this point difficult to understand both from the STATA help and
from the
Cleves book - it was looking through responses on this board from Roberto
Gutierrez
that set this piece straight for me.  Simply trying to check the
proportionality assumption
in my analyses has left me in a state of semi-permanent mild panic that I am
erroneously
applying advice for different issues to my own analysis - which involves not
only
time-varying covariates, but also a time-varying exposure.

Which is a long-winded way of asking --- does anybody know of any resources
which provide
an in-depth treatment of time-varying covariates in survival analysis using
STATA?  It
would be much appreciated!

And once again, I have to say - I don't know what I would do without the
benefit of having
this group available as a resource.  I am extremely grateful for its
existence, and for all
of you gurus who take the time to help point us in the right direction!

Best regards,
Chelsea Polis

-----Original Message-----
From: Carlo Lazzaro [mailto:carlo.lazzaro@tiscalinet.it]
Sent: Thursday, May 07, 2009 1:53 AM
To: statalist@hsphsun2.harvard.edu
Cc: Polis, Chelsea B.
Subject: R: RE: Why don't my IRs and Cox HRs echo each other?



Dear Chelsea,
folowing Kieran's helpful advice, besides Kaplan-Meier estimates, you could
take a look at whether deaths in one out of the compared groups tended to
occur earlier or later vs the other one via log-rank test (more sensitive to
differences occurring at the end of the follow-up) and Wilcoxon test (more
sensitive to differences occurring at the beginning of the follow-up).

Amongst other contributions, this topic is well covered in Maarten L. Buis.
An introduction to Survival Analysis. 2006 (http://home.fsw.vu.nl/m.buis/)
Cleves MA, Gould WG, Gutierrez R. An Introduction To Survival Analysis Using
Stata. Revised edition. College Station: StataPress, 2006.

HTH and Kind Regards,
Carlo
-----Messaggio originale-----
Da: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] Per conto di Polis, Chelsea B.
Inviato: giovedì 7 maggio 2009 1.35
A: statalist@hsphsun2.harvard.edu
Oggetto: st: RE: Why don't my IRs and Cox HRs echo each other?

I apologize for the formatting of my table, it looked ok when I sent it.
Please let me try again.


Variable  Deaths  PY at risk    IR      HR      95% CI  p-value

HC use                                                          0.07
 No         91  1262.7  7.21    1.00
 Yes        13  293.0           4.44    0.58    0.32-1.04
Current age                                                             0.38
 15-24    20    394.0           5.08    1.00
 25-34    49    711.8           6.88    0.73    0.43-1.24
 35+        35  449.9           7.78    0.68    0.38-1.20
Sex partners in past year                                       0.01
 None       18  241.2           7.46    1.00
 One        76  1204.6  6.31    1.31    0.78-2.21
 Two+       10  109.9           9.10    3.40    1.54-7.54

-----Original Message-----
From: owner-statalist@hsphsun2.harvard.edu
[mailto:owner-statalist@hsphsun2.harvard.edu] On Behalf Of Polis, Chelsea B.
Sent: Wednesday, May 06, 2009 7:29 PM
To: statalist@hsphsun2.harvard.edu
Subject: st: Why don't my IRs and Cox HRs echo each other?

Dear statalisters,

I am doing survival analysis on time to death with time-varying covariates
on an open, population
-based cohort study.  The base sample is essentially a census of individuals
in 56 villages, and
I am utilizing information from all female incident HIV seroconverters.

I computed incidence rates based on the raw data ((number of
deaths/person-time at risk)*100 - I obtained
time at risk using the stdes command), but the IRs don't seem to echo trends
in the univariate Cox HRs.

In the sample data below, things appear reasonable for HC use (deaths per
100 person years is lower
if HC=yes, and the HR reflects this).  But for current age, deaths are
higher in the 25-34 category
than in the 15-24 category, but the HR trends suggests that being 25-34 is
protective (though not
significantly).  Also, the magnitude seems off, for example, in the variable
"Sex partners in past
year" - having two or more seems to more than triple the hazard in the Cox
regression, but merely
increases from 7.46 to 9.10 in the deaths per 100 p-y.

Am I missing something in expecting these numbers to echo trends in each
other?  Is this just a
matter of non-significance within individual categories?  Or a difference in
time-to-event versus
person-time analysis?  Or because I am doing an analysis with time-varying
covariates?  Should I not
expect these to align? Any help is appreciated!

Variable        Deaths  PY at risk      Deaths per      HR      95% CI
p-value
                                                100 p-y
HC use
0.07
 No             91              1262.7  7.21            1.00
 Yes            13              293.0           4.44            0.58
0.32-1.04
Current age
0.38
 15-24  20              394.0           5.08            1.00
 25-34  49              711.8           6.88            0.73    0.43-1.24
 35+            35              449.9           7.78            0.68
0.38-1.20
Sex partners in past year
0.01
 None           18              241.2           7.46            1.00
 One            76              1204.6  6.31            1.31    0.78-2.21
 Two+           10              109.9           9.10            3.40
1.54-7.54


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