ggforest() doesn't show interactions HRs

[Generalized]

Dear Authors,
I noticed, that the ggforest() function doesn't plot interactions. The version from CRAN doesn't do that, so I downloaded and installed the most fresh code from the GitHub via devtools::install_github("kassambara/survminer", build_vignettes = FALSE). Same result.

Expected behavior

An interaction should be shown

Actual behavior

Only the main effects are shown

Steps to reproduce the problem

Fit any Cox model with an interaction.
Run ggforest on it.

> ggforest( fit <- coxph(Surv(time, status) ~ ph.karno * age, data=lung), data=lung)
> summary(fit, conf.int = FALSE)
Call:
coxph(formula = Surv(time, status) ~ ph.karno * age, data = lung)

  n= 227, number of events= 164 
   (1 observation deleted due to missingness)

                   coef  exp(coef)   se(coef)      z Pr(>|z|)  
ph.karno     -0.1211782  0.8858761  0.0486092 -2.493   0.0127 *
age          -0.1206758  0.8863212  0.0610426 -1.977   0.0481 *
ph.karno:age  0.0016586  1.0016600  0.0007525  2.204   0.0275 *
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Concordance= 0.598  (se = 0.025 )
Likelihood ratio test= 14.52  on 3 df,   p=0.002
Wald test            = 13.42  on 3 df,   p=0.004
Score (logrank) test = 13.44  on 3 df,   p=0.004

session_info()

tools::session_info()
- Session info ---------------------------------------------------------------
 setting  value                       
 version  R version 4.0.2 (2020-06-22)
 os       Windows 10 x64              
 system   x86_64, mingw32             
 ui       Rgui                        
 language (EN)                        
 date     2020-09-29                  

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[1] C:/tmp/r4/library

[zh-zhang1984]

I also encounter this issue; Hope the authors can address this issue in the future version.

[Generalized]

Dear Authors and Contributors, @kassambara , @MarcinKosinski, @zzawadz , @pbiecek Do you plan to fix this issue in the near future? I am sorry for asking, but I will need to consider switching to another package or write necessary procedure on my own and message my Professor about the need for additional work on that. Please, let me just know.

[pbiecek]

Dear @Generalized
In general case it may be tricky to automatically extract interactions or variable transformations from model objects.
So my advice would be to create manually new variables that capture desired effects of interactions and add them to the model in an explicit way.

Here is an example how to do this.

You have:

mm <- coxph(Surv(time, status) ~ ph.karno * age, data=lung)
ggforest( mm, data=lung)
summary(fit, conf.int = FALSE)

                   coef  exp(coef)   se(coef)      z Pr(>|z|)  
ph.karno     -0.1211782  0.8858761  0.0486092 -2.493   0.0127 *
age          -0.1206758  0.8863212  0.0610426 -1.977   0.0481 *
ph.karno:age  0.0016586  1.0016600  0.0007525  2.204   0.0275 *

What you can do is to create the variable that handles interaction manually:

lung$ph.karno_age <- lung$ph.karno * lung$age

and now you can fir an additive model and the ggforest will include it in the plot

fit <- coxph(Surv(time, status) ~ ph.karno + age + ph.karno_age, data=lung)
ggforest(fit, data=lung)
summary(fit, conf.int = FALSE)

                   coef  exp(coef)   se(coef)      z Pr(>|z|)  
ph.karno     -0.1211782  0.8858761  0.0486092 -2.493   0.0127 *
age          -0.1206758  0.8863212  0.0610426 -1.977   0.0481 *
ph.karno_age  0.0016586  1.0016600  0.0007525  2.204   0.0275 *

Please note that here you have two numerical variables, this is why I've just multiplied them.
If you have factors you should concatenate factors for bot variables

[Generalized]

Thank you. It's a perfect solution. Maybe, instead of "fixing" that automatically, especially if it requires some tricky coding, you could use this answer to make an example in a vignette? I think this may be rather a common issue, that people look for the interaction they explicitly specified yet which is missing in the output. Especially, that I saw a similar topic here on the list, but much earlier. This solution requires some extra work from the user, but gives the expected output, which is priceless, as we do not have switch to other packages to complete this task. Thank you!

[pbiecek]

Good idea,
will do this!

[kassambara]

New ggforest vignette added now

[ronammar]

How would one generalize this solution to interactions between categorical variables?

[benjaminwnelson]

This doesn't seem to work for an interaction between categorical variables. Is this a feature that might be added? Thanks!

[trentleslie]

This doesn't seem to work for an interaction between categorical variables. Is this a feature that might be added? Thanks!

If you have factors you should concatenate factors for bot variables

If you have categorical variables, you can concatenate them to make the combined, new categorical variables.

Cat 1 Cat 2 Concat
A C AC
A D AD
B D BD

Then use the "Concat" column as your interaction variable. Hope this helps.