[R] Presenting Hazard ratios for interacting variables in a Cox model

[Stuart Patterson]

Dear David,
Thank you for your reply. Your suggestions on how better to write the
command are very useful, and I can see how the simplification would help. I
hadn't realised that the lower order variables would be included if I
simply wrote in teh interaction terms. Thank you

The table below is how I feel that I ought to present my results. If anyone
has any suggestions on how to obtain these values I would be very grateful!

Cheers
Stuart



Variable
Hazard Ratio
95% Confidence Interval
p Value

Year
Prev. TB
Age
2002-2005
No
< 24 months
24-48 months



>48 months



Yes
< 24 months



24-48 months



>48 months



2006-2010
No
< 24 months



24-48 months



>48 months



Yes
< 24 months



24-48 months
a
b
c
>48 months



2011-2015
No
< 24 months



24-48 months



>48 months



Yes
< 24 months



24-48 months



>48 months
















On 18 November 2016 at 19:21, David Winsemius <dwinsemius at comcast.net>
wrote:

>
> > On Nov 18, 2016, at 6:56 AM, Stuart Patterson via R-help <
> r-help at r-project.org> wrote:
> >
> > I have a time-dependent cox model with three variables, each of which
> > interacts with the other two. So my final model is:
> >
> > fit12<-coxph(formula = Surv(data$TimeIn, data$Timeout, data$Status) ~
> data$
> > Year+data$Life_Stg+data$prev.tb +data$prev.tb*data$Life_Stg +
> data$Year*data
> > $Life_Stg + data$Year*data$prev.tb + frailty(data$Natal_Group), data =
> data)
>
> It seems fairly likely that you are shooting yourself in the foot by using
> the `data$variate` inside the formula. It will prevent the regression
> result from having correctly assembled references to variables. And that
> will become evident when you try to do any predictions. Try instead:
>
> fit12<-coxph(formula = Surv( TimeIn,  Timeout,  Status) ~
>             prev.tb * Life_Stg +  Year *Life_Stg +  Year * prev.tb +
> frailty( Natal_Group),
>             data = data)
>
> The `*` in a formula automatically includes the lower order individual
> variates in the estimates. Your model RHS could have also been written
> (more clearly in my opinion):
>
>          ~ (prev.tb + Life_Stg +  Year)^2
>
> ... since R formulas interpret the `(.)^N` operation as "all base effects
> and interactions up to order N".
>
> >
> > For my variables, there are 3 categories of year, three of year, and
> > prev.tb is a binary variable. Because of the interactions, when I present
> > the results, I want to present the Hazard ratio, 95% CI, and p value for
> > each combination of the three variables. How do I get R to give me these
> > values please?
> >
> > I think that the contrast function does this for other models but does
> not
> > work for coxph?
>
> The usual method would be to use `predict` on a 'newdata' dataframe with
> all the combinations generated from `expand.grid`. The combination of the
> reference values of all three variables should yield a 1.0 hazard ratio.
> But time-dependent model predictions need a complete specification of a
> sequence of values over the time course of the study (as well as
> specification of the frailty term. So I'm not in a position to comment on
> feasibility for this situation.
>
> >
> > Grateful for any suggestions
> >
> > Best wishes
> >
> > Stuart Patterson, Royal Veterinary College, University of London
> >
> >       [[alternative HTML version deleted]]
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > https://stat.ethz.ch/mailman/listinfo/r-help
> > PLEASE do read the posting guide http://www.R-project.org/
> posting-guide.html
> > and provide commented, minimal, self-contained, reproducible code.
>
> David Winsemius
> Alameda, CA, USA
>
>

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