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RE: st: Interpreting effect of covariate with min 0.2 and max 1.04 on odds/time ratios in PH and AFT models


From   Erik Aadland <erikaadland@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Interpreting effect of covariate with min 0.2 and max 1.04 on odds/time ratios in PH and AFT models
Date   Mon, 15 Aug 2011 10:10:40 +0000

Thank you, Maarten.
 
That helps. The covariate in question is a ratio variable I have calculated based on other variables.
I thought it would be easier to communicate the findings if I could show how the ratio variable influences the hazard ratio within the range of the observed values on the ratio variable. I guess that is not possible then?
 
Thanks again,
 
Erik.
 
 
 


----------------------------------------
> Date: Mon, 15 Aug 2011 11:09:25 +0200
> Subject: Re: st: Interpreting effect of covariate with min 0.2 and max 1.04 on odds/time ratios in PH and AFT models
> From: maartenlbuis@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> On Mon, Aug 15, 2011 at 10:40 AM, Erik Aadland wrote:
> > I am estimating Weibull PH and AFT models.
> > One of my timevarying covariates has a min of 0.2 and a max of 1.04.
> > I want to interpret the effect of different values on this variable on the odds/time ratio.
> > The values on this covariate are 0.2, 0.4, 0.6, 0.8, 0.9 and 1.04.
> >
> > The odds/time ratio reported is for each one unit increase in this covariate (I take it the one unit increase is 1).
> > A one unit increase (1) does not reflect the values on my covariate well.
> > Is there a way to get around this problem?
>
> I am assuming you want hazard ratios rather than odds ratios.
>
> One solution is to create a new variable which is the old variable
> multiplied by 5, and add that to your model.
>
> However, you do not need to evaluate the hazard ratio or time ratio at
> different values, they will, by assumption, be the same whichever
> value you choose, a unit increase in x will always lead to an increase
> in the expected time/hazard by a ratio of exp(b). This is similar to
> the linearity assumption in linear regression: a unit increase in x
> will always lead b units increase in the expected value of y. These
> assumptions do not have to be true, but you are not going to find
> deviations from that assumption by inspecting a model that enforces
> that assumption.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
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