forestplotCombineRegrObj {Greg}

Compares different scores in different regression objects.

Description

Creates a composite from different regression objects into one forestplot where you can choose the variables of interest to get an overview and easier comparison.

Usage

forestplotCombineRegrObj(
  regr.obj,
  variablesOfInterest.regexp = NULL,
  estimate.txt = NULL,
  add_first_as_ref = FALSE,
  ref_txt = "ref.",
  digits = 1,
  post_process_data = function(x) x,
  is.summary = NULL,
  xlab = NULL,
  zero = NULL,
  xlog = NULL,
  exp = xlog,
  ...
)

Arguments

regr.obj

A list with all the fits that have variables that are to be identified through the regular expression

variablesOfInterest.regexp

A regular expression identifying the variables that are of interest of comparing. For instance it can be "(score|index|measure)" that finds scores in different models that should be compared.

estimate.txt

The text of the estimate, usually HR for hazard ratio, OR for odds ratio

add_first_as_ref

If you want that the first variable should be reference for that group of variables. The ref is a variable with the estimate 1 or 0 depending if exp() and the confidence interval 0.

ref_txt

Text instead of estimate number

digits

Number of digits to use for the estimate output

post_process_data

A function that takes the data frame just prior to calling 'forestplot' and allows you to manipulate it. Primarily used for changing the 'column_label' that has the names shown in the final plot.

is.summary

A vector indicating by TRUE/FALSE if the value is a summary value which means that it will have a different font-style

xlab

x-axis label

zero

Indicates what is zero effect. For survival/logistic fits the zero is 1 while in most other cases it's 0.

xlog

If TRUE, x-axis tick marks are to follow a logarithmic scale, e.g. for logistic regression (OR), survival estimates (HR), Poisson regression etc. Note: This is an intentional break with the original forestplot function as I've found that exponentiated ticks/clips/zero effect are more difficult to for non-statisticians and there are sometimes issues with rounding the tick marks properly.

exp

Report in exponential form. Default true since the function was built for use with survival models.

...

Passed to forestplot()

See Also

Other forestplot wrappers: forestplotRegrObj()

Examples

org.par <- par("ask" = TRUE)

# simulated data to test
library(tidyverse)
set.seed(10)
cov <- tibble(ftime = rexp(200),
              fstatus = sample(0:1, 200, replace = TRUE),
              x1 = runif(200),
              x2 = runif(200),
              x3 = runif(200)) |> 
  # Add some column labels
  Gmisc::set_column_labels(x1 = "First variable",
                           x2 = "Second variable")

library(rms)
ddist <- datadist(cov)
options(datadist = "ddist")

fit1 <- cph(Surv(ftime, fstatus) ~ x1 + x2, data = cov)
fit2 <- cph(Surv(ftime, fstatus) ~ x1 + x3, data = cov)

list(`First model` = fit1, 
     `Second model` = fit2) |> 
  forestplotCombineRegrObj(variablesOfInterest.regexp = "(x2|x3)") |> 
  fp_set_style(lines = "steelblue",
               box = "darkblue")

# How to add expressions to the plot label
list(fit1, fit2) |> 
  forestplotCombineRegrObj(variablesOfInterest.regexp = "(x2|x3)",
                           reference.names = c("First model", "Second model"),
                           post_process_data = \(data) {
                             data$column_label[4] <- c(rlang::expr(expression(Fever >= 38.5)))
                             return(data)
                           })

par(org.par)