Is it possible to use 'panel' setting with forest_model package when also use a model list? (R statistical analysis)

I would like to plot results of multiple cox proportional univariate analysis into one forest plot. Thanksfully this is possible by using the forest_model package in R.

I was wondering if it is possible to use the 'panel' argument of the function forest_model() while also using the 'model_list' argument.

When only using one analysis with forest_model() everything works fine:

  panels <- list(
    list(width = 0.03),
    list(width = 0.1, display = ~variable, fontface = "bold", heading = "Xariable"),
    list(width = 0.1, display = ~level),
    list(width = 0.05, display = ~n, hjust = 1, heading = "N"),
    list(width = 0.03, item = "vline", hjust = 0.5),
    list(
        width = 0.55, 
        item = "forest",
        hjust = 0.5,
        heading = "Hazard ratio",
        linetype = "dashed",
        line_x = 0
        ),
    list(width = 0.03, item = "vline", hjust = 0.5),
    list(
        width = 0.12, 
        display = ~ ifelse(reference, "Reference", sprintf("%0.2f (%0.2f - %0.2f)",trans(estimate), trans(conf.low), trans(conf.high))),
        display_na = NA,
        heading="95% CI (Range)"
        ),
    list(
        width = 0.05,
        display = ~ ifelse(reference, "", format.pval(p.value, digits = 1, eps = 0.001)),
        display_na = NA, hjust = 1, heading = "p"
        ),
    list(width = 0.03)
  )

  forest_model(coxph(Surv(Duration, AliveDead) ~ Gender, pretty_Datensatz_3), panels, exponentiate=TRUE)

Plot with customization with the panel arguemnt

When using the model_list argument the customization done with the panels argument seems to be ignored.

coxVar1 <- coxph(Surv(Duration, AliveDead) ~ Gender, pretty_Datensatz_3)
# coxVar2 till coxVar8 in the same way

model_list <- list(Age = coxVar2, Gender = coxVar1, RRT = coxVar4, Catecholamine = coxVar5, InvVent = coxVar6, SAPS = coxVar3, TISS = coxVar7, DoV=coxVar8)

forest_model(model_list = model_list,merge_models =TRUE, panels)

I have already tried to use fewer list elements within model_list but this did not change anything.

I have searched through Google and ChatGPT but this problem wasn't addressed there. I also read through the documentation but either I missed something or there was no info about this.

Anyone any suggestions?

I tested it out and I have something for you to try.

Let's try a reproducible example.

library(survival)
library(dplyr)
library(tidyverse)
pretty_lung <- lung %>%
  transmute(time,
            status,
            Age = age,
            Sex = factor(sex, labels = c("Male", "Female")),
            ECOG = factor(lung$ph.ecog),
            `Meal Cal` = meal.cal
  )

coxVar1 <- coxph(Surv(time, status) ~ ECOG, pretty_lung)
coxVar2 <- coxph(Surv(time, status) ~ Sex, pretty_lung)

model_list <- list(ECOG = coxVar1, Sex = coxVar2)

And then I read in your panels list. Note that I saw that there was a typo in "Xariables", which may have been intentional.

This did not work for me, and gave the same result as you specified.

forest_model(model_list = model_list,merge_models =TRUE, panels) #does not work

If I used panels = panels, this had the expected result.

forest_model(model_list = model_list,merge_models =TRUE, panels=panels)  #specify panels=

The reason seems that there is a if(missing(panels)) statement that displays the default_forest_panels assignment unless you explicitly assign your panels.