# st: Adjusted survival question for Mario Cleves or Bill Gould

 From Hebe.B.Quinton@dartmouth.edu (Hebe B. Quinton) To statalist@hsphsun2.harvard.edu Subject st: Adjusted survival question for Mario Cleves or Bill Gould Date 10 Jul 2003 09:57:01 EDT

```Stata Tech Support suggested this as the appropriate venue for posing this
question:

Below is a web site and the abstract about a comparison of methods for plotting
adjusted survival.  I would be very curious as to what you think of this
method, since adjustfor() in sts graph I think is doing the "average covariate
method".  Is there any thought of implementing the alternate approach in Stata?

(The web site gives an .ado file, but it is a bit clunky)

Hebe Quinton
Clinical Research
Dartmouth Medical School
(603) 650-7710
=========

PLOTTING ADJUSTED SURVIVAL CURVES FROM PROPORTIONAL HAZARDS MODELS: A
COMPARISON OF TWO METHODS.

WA Ghali, H Quan, R Brant, CM Norris, G van Melle, ML Knudtson, for the
APPROACH (Alberta Provincial Program for Outcome Assessment in Coronary Heart
Disease) Investigators.

University of Calgary, Calgary, AB, Canada
APPROACH is a large inception cohort study which captures all patients
undergoing cardiac catheterization in Alberta, Canada. We used data on 11,804
patients from this study to examine the two year survival experience of
diabetics vs. non-diabetics, while controlling for covariates such as left
ventricular ejection fraction, coronary anatomy, sociodemographic variables,
and comorbidities. Here, we present a comparison of two methods for calculating

The most commonly-used method for generating such curves is the "average
covariate method", in which the average values of covariates of interest are
entered into a proportional hazards regression equation to generate adjusted
survival estimates. We used data from the APPROACH study to compare the
survival curves generated by the "average covariate method" with those
generated by a newer method - the "corrected group prognosis method" - in which
a survival curve for each level of covariates is calculated, after which an
average survival curve is calculated as a weighted average of the individual
survival curves. The resulting adjusted survival curves are shown below, along
with the corresponding unadjusted survival curves for diabetics and
non-diabetics.

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