Roland, you might also want to check on the articles :
- "Choice of time scale and its effects on significance of predictors in longitudinal studies" Pencina, Larson, and D'agostino, Stat in Medicine 2007-26, 1343-1359
- "Choice of time scale in Cox's model analysis of epidemiologic cohort data, a simulation study" Thiebaut and Benichou; Stat in Medicine, 2004, 23, 3803-3820
And
- "Time-to-Event analysis of longitudinal Follow up of a Survey: choice of time scale"
Korn, Graubard and Midthune; American Journal of Epidemiology 1997, 145, 1, 72-90.
As your analysis appears to pertain to the medical field.
Hope this helps
Isabelle
Isabelle Deltour, Ph D
-----Original Message-----
From: [email protected] [mailto:[email protected]] On Behalf Of [email protected]
Sent: Monday, December 14, 2009 9:09 PM
To: [email protected]
Subject: Re: st: Choice of analysis time in survival analysis where the hazard is associated with age
Definitely do use age as the time dimension. It is the natural
dimension. The start-time in 1/1/1991 is an artifact of your data
collection and has no natural meaning. I would, at least to begin,
also ignore events before 1/1/1991 and operate on the true cohort:
those alive and well without appendicitis on that date.
For analysis, I recommend -stpm2- by Patrick Royston and Paul
Lambert ("findit stpm2") That command fits flexible spline models to
the underlying hazard in the Cox model, and allows interactions of the
spline functions with other factors. The original -stpm- command is
described in: Patrick Royston Flexible parametric alternatives to the
Cox model, and more, The Stata Journal (2001) 1, Number 1, pp. 1-28.
Fitting a model to most of the life-span is a challenge. lRather than
fit a single model, stratified or not, l to the entire life span (ages
1-64), I suggest that initially, you divide age into "natural"
intervals and fit a separate model to each, with observations censored
at the interval end (this is not an analysis with age strata). Then
pause and decide if and how you would like to do a combined analysis.
You do not mention the goals of your analyses, but if you have a
sufficient number of events, consider setting aside one portion of
your data for choosing your best models, and another portion for
testing the hypotheses and fits of these models.
-Steve
On Mon, Dec 14, 2009 at 7:38 AM, roland andersson
<[email protected]> wrote:
> I have a large national dataset and plan to analyse socioeconomic
> differences in risk of having appendicitis. Persons born after 1949 to
> end of 1990 and alive at 1/1/1991 (n=4.500.000) are included in the
> dataset. The dataset contains the date when a person was operated for
> appendicitis between 1/11/1990 and 31/12/2003 and date of death.
>
> The hazard of having appendicitis is agedependent and is increasing
> till age 13 and then decreasing. Previous collaborators have made
> Coxregression with time from 1/11/1990 to the appendicitisdiagnosis or
> censoring as analysistime, and have entered age at operation as
> covariates to control for the age-dependent differencies in hazard.
>
> Knowing that the hazard is age-dependent I think this may give biased
> results. I therefore wonder if it would be better to use age as
> analysis time (with late entries) instead of time from start of follow
> up with adjustment for age. Am I right to think that this will be a
> better way of controling for the age-dependent differences in hazard?
> If that is the case may I also include age as a covariate in the
> analysis or would that complicate things?
>
> I have also considered to use a stratified analysis with ageintervals
> as stratification variable. Can you give some advice on how these
> ageintervals should be chosen in view of the agedependent increasing
> and decreasing hazard?
>
> I would appreciate your comments.
>
> Roland Andersson, MD PhD
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>
--
Steven Samuels
[email protected]
18 Cantine's Island
Saugerties NY 12477
USA
845-246-0774
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