bend|x@c@r@ten@en @end|ng |rom reg|onh@dk
Thu Apr 1 15:00:21 CEST 2021
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.
Steno Diabetes Center Copenhagen
Niels Steensens Vej 2-4
tel: +45 30 91 29 61
b using bxc.dk
bendix.carstensen using regionh.dk
Region Hovedstaden anvender de personoplysninger, du giver os i forbindelse med din henvendelse. Du kan læse mere om formålet med anvendelsen samt dine rettigheder på vores hjemmeside: www.regionh.dk/persondatapolitik
More information about the R-help