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From |
"Sayer, Bryan" <BSayer@s-3.com> |

To |
"'Hebe.B.Quinton@dartmouth.edu '" <Hebe.B.Quinton@dartmouth.edu>, "'statalist@hsphsun2.harvard.edu '" <statalist@hsphsun2.harvard.edu> |

Subject |
st: RE: Adjusted survival question for Mario Cleves or Bill Gould |

Date |
Thu, 10 Jul 2003 13:52:39 -0400 |

I haven't checked the web site, but several issues always spring to mind when looking at "adjusted" outcomes. 1. If you average a covariate, what does it mean to be 0.52 male or 0.13 black? 2. Is the average over the estimation sample, a super-sample, or an outside sample? What is the variance associated with the average? 3. Will a non-linear model produce adjusted estimates outside the range of un-adjusted estimates? Does this make any sense? I recommend (as usual) Graubard. B. and Korn, E. "Predictive Margins With Survey Data" Biometrics, June 1999 Bryan Sayer Statistician, SSS Inc. -----Original Message----- From: Hebe.B.Quinton@dartmouth.edu To: statalist@hsphsun2.harvard.edu Cc: Kathryn.A.Sabadosa@dartmouth.edu Sent: 7/10/03 9:57 AM Subject: st: Adjusted survival question for Mario Cleves or Bill Gould 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 ========= http://www.ucalgary.ca/~hquan/adjsurv.html 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 covariate-adjusted survival curves. 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. * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/ * * For searches and help try: * http://www.stata.com/support/faqs/res/findit.html * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

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