[R] cox.zph

[Kevin Thorpe]

While the statements below about cox.zph are true, plotting the cox.zph result does tell you what the HR is doing. I never use one without the other.

-- 
Kevin E. Thorpe
Head of Biostatistics,  Applied Health Research Centre (AHRC)
Li Ka Shing Knowledge Institute of St. Michael's
Assistant Professor, Dalla Lana School of Public Health
University of Toronto
email: kevin.thorpe using utoronto.ca  Tel: 416.864.5776  Fax: 416.864.3016

> On Apr 1, 2021, at 9:00 AM, Bendix Carstensen <bendix.carstensen using regionh.dk> wrote:
> 
> EXTERNAL EMAIL:
> 
> Further to John Sorkin's post on the cox.zph:
> You get test(s) of whether there is an interaction between a variable, say, sex, and time.
> 
> Suppose it is significant. You will have no clue whether the M/W hazard ratio is increasing or decreasing by time.
> 
> Suppose it is not significant. You will have no clue whether the (non-significant) M/W hazrad ratio exhibits a pattern that is worth looking further into or not.
> 
> In this sense the cox.zph is a perfect tool to allow you to write 'we checked for non proportionality' instead of 'we have no clue of how the M/W ratio varies by time'.
> 
> If you label it what it is, namely a test of interaction, you might realize that you should ESTIMATE the shape and size of the interaction before deriving a test, either ad-hoc by the Shoenfeld residuals or by proper modeling.
> 
> See for example pp 202 ff. in 'Epidemiology with R' by (surprise, surprise) me, published by OUP a few months ago.
> 
> b.r.
> Bendix Carstensen
> Senior Statistician
> Steno Diabetes Center Copenhagen
> Clinical Epidemiology
> Niels Steensens Vej 2-4
> DK-2820 Gentofte
> Denmark
> tel: +45 30 91 29 61
> b using bxc.dk
> bendix.carstensen using regionh.dk
> http://BendixCarstensen.com
> 
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