It is a common practice in epidemiological papers to present regression estimates in both adjusted and unadjusted format. The unadjusted format is just that particular variable without any of the covariates and is often referred to as the crude estimate. The advantage with the two is that you provide the reader with a better understanding of how much the variables affect each other and also a sense of how much confounding is present in the model.
printCrudeAndAdjusted functionThe printCrudeAndAdjusted was designed for this purpose.
It provides a table with the coef and confint
estimates for the full model, then reruns for each variable through an
update call on the model providing
outcome ~ variable as input. Below is a basic example:
library(datasets)
data(mtcars)
mtcars$am <- factor(mtcars$am, labels = c("Automatic", "Manual"))
fit <- lm(mpg ~ cyl + disp + hp + am, data = mtcars)
library(Greg)
printCrudeAndAdjustedModel(fit)| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| (Intercept) | 20.09 | 17.92 to 22.26 | 30.48 | 24.60 to 36.36 | |
| cyl | -2.88 | -3.53 to -2.22 | -0.83 | -2.39 to 0.72 | |
| disp | -0.04 | -0.05 to -0.03 | -0.01 | -0.03 to 0.01 | |
| hp | -0.07 | -0.09 to -0.05 | -0.03 | -0.06 to 0.00 | |
| Manual | 7.24 | 3.64 to 10.85 | 3.45 | 0.46 to 6.43 | |
As the variable names often aren’t that pretty you can use the
rowname.fn in order to get the row names desired. Here’s a
simple substitution example together with some limits to the digits, and
a reference group:
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE,
rowname.fn = function(n){
if (n == "disp")
return("Displacement (cu.in.)")
if (n == "hp")
return("Gross horsepower")
if (n == "cyl")
return("No. cylinders")
if (n == "am")
return("Transmission")
return(n)
})| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | |
| Displacement (cu.in.) | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Gross horsepower | -0.1 | -0.1 to 0.0 | 0.0 | -0.1 to 0.0 | |
| Transmission | |||||
| Automatic | 0.0 | ref | 0.0 | ref | |
| Manual | 7.2 | 3.6 to 10.8 | 3.4 | 0.5 to 6.4 | |
You can achieve the same effect with the label function
in the Hmisc package:
library(Hmisc)
label(mtcars$disp) <- "Displacement (cu.in)"
label(mtcars$cyl) <- "No. cylinders"
label(mtcars$hp) <- "Gross horsepower"
label(mtcars$am) <- "Transmission"
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE)| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | |
| Displacement (cu.in) | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Gross horsepower | -0.1 | -0.1 to 0.0 | 0.0 | -0.1 to 0.0 | |
| Transmission | |||||
| Automatic | 0.0 | ref | 0.0 | ref | |
| Manual | 7.2 | 3.6 to 10.8 | 3.4 | 0.5 to 6.4 | |
If we want to style the table we can use
htmlTable::addHtmlTableStyle
library(htmlTable)
printCrudeAndAdjustedModel(fit,
digits = 1,
add_references = TRUE) %>%
# We can also style the output as shown here
addHtmlTableStyle(css.rgroup = "")| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | |
| Displacement (cu.in) | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Gross horsepower | -0.1 | -0.1 to 0.0 | 0.0 | -0.1 to 0.0 | |
| Transmission | |||||
| Automatic | 0.0 | ref | 0.0 | ref | |
| Manual | 7.2 | 3.6 to 10.8 | 3.4 | 0.5 to 6.4 | |
The style can be added to all the tables by setting a theme option and activating the style for all the table elements.
setHtmlTableTheme(css.rgroup = "")The returned variable from printCrudeAndAdjustedModel
works just like any matrix, i.e. you can bind it both on rows and on
columns:
fit_mpg <- lm(mpg ~ cyl + disp + hp + am, data = mtcars)
fit_weight <- lm(wt ~ cyl + disp + hp + am, data = mtcars)
p_mpg <- printCrudeAndAdjustedModel(fit_mpg, digits = 1, add_references = TRUE)
p_weight <- printCrudeAndAdjustedModel(fit_weight, digits = 1, add_references = TRUE)
rbind("Miles per gallon" = p_mpg,
"Weight (1000 lbs)" = p_weight)| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| Miles per gallon | |||||
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | |
| Displacement (cu.in) | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Gross horsepower | -0.1 | -0.1 to 0.0 | 0.0 | -0.1 to 0.0 | |
| Transmission | |||||
| Automatic | 0.0 | ref | 0.0 | ref | |
| Manual | 7.2 | 3.6 to 10.8 | 3.4 | 0.5 to 6.4 | |
| Weight (1000 lbs) | |||||
| (Intercept) | 3.2 | 2.9 to 3.6 | 2.3 | 1.5 to 3.2 | |
| No. cylinders | 0.4 | 0.3 to 0.6 | -0.1 | -0.3 to 0.2 | |
| Displacement (cu.in) | 0.0 | 0.0 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Gross horsepower | 0.0 | 0.0 to 0.0 | 0.0 | 0.0 to 0.0 | |
| Transmission | |||||
| Automatic | 0.0 | ref | 0.0 | ref | |
| Manual | -1.4 | -1.9 to -0.8 | -0.6 | -1.0 to -0.1 | |
cbind("Miles per gallon" = p_mpg,
"Weight (1000 lbs)" = p_weight)| Variable | Crude | 2.5 % to 97.5 % | Adjusted | 2.5 % to 97.5 % | Crude | 2.5 % to 97.5 % | Adjusted | 2.5 % to 97.5 % |
|---|---|---|---|---|---|---|---|---|
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | 3.2 | 2.9 to 3.6 | 2.3 | 1.5 to 3.2 |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | 0.4 | 0.3 to 0.6 | -0.1 | -0.3 to 0.2 |
| Displacement (cu.in) | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | 0.0 | 0.0 to 0.0 | 0.0 | 0.0 to 0.0 |
| Gross horsepower | -0.1 | -0.1 to 0.0 | 0.0 | -0.1 to 0.0 | 0.0 | 0.0 to 0.0 | 0.0 | 0.0 to 0.0 |
| Transmission | ||||||||
| Automatic | 0.0 | ref | 0.0 | ref | 0.0 | ref | 0.0 | ref |
| Manual | 7.2 | 3.6 to 10.8 | 3.4 | 0.5 to 6.4 | -1.4 | -1.9 to -0.8 | -0.6 | -1.0 to -0.1 |
[It is also possible to select individual rows/columns if so desired
using the [ operator:
p_mpg[,1:2]| Variable | Coef | 2.5 % to 97.5 % |
|---|---|---|
| (Intercept) | 20.1 | 17.9 to 22.3 |
| No. cylinders | -2.9 | -3.5 to -2.2 |
| Displacement (cu.in) | 0.0 | -0.1 to 0.0 |
| Gross horsepower | -0.1 | -0.1 to 0.0 |
| Transmission | ||
| Automatic | 0.0 | ref |
| Manual | 7.2 | 3.6 to 10.8 |
p_mpg[1:2,]| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | Coef | 2.5 % to 97.5 % | Coef | 2.5 % to 97.5 % | |
| (Intercept) | 20.1 | 17.9 to 22.3 | 30.5 | 24.6 to 36.4 | |
| No. cylinders | -2.9 | -3.5 to -2.2 | -0.8 | -2.4 to 0.7 | |
The function is prepared for use with splines and strata but is currently not tested for mixed models. As the spline estimates are best interpreted in a graph they are omitted from the output.
library("survival")
set.seed(10)
n <- 500
ds <- data.frame(
ftime = rexp(n),
fstatus = sample(0:1, size = n, replace = TRUE),
y = rnorm(n = n),
x1 = factor(sample(LETTERS[1:4], size = n, replace = TRUE)),
x2 = rnorm(n, mean = 3, 2),
x3 = rnorm(n, mean = 3, 2),
x4 = factor(sample(letters[1:3], size = n, replace = TRUE)),
stringsAsFactors = FALSE)
library(survival)
library(splines)
fit <- coxph(Surv(ds$ftime, ds$fstatus == 1) ~ x1 + ns(x2, 4) + x3 + strata(x4), data = ds)
printCrudeAndAdjustedModel(fit, add_references = TRUE)| Crude | Adjusted | ||||
|---|---|---|---|---|---|
| Variable | HR | 2.5 % to 97.5 % | HR | 2.5 % to 97.5 % | |
| x1 | |||||
| A | 1.00 | ref | 1.00 | ref | |
| B | 0.86 | 0.60 to 1.21 | 0.88 | 0.62 to 1.25 | |
| C | 0.78 | 0.56 to 1.09 | 0.78 | 0.56 to 1.09 | |
| D | 1.23 | 0.86 to 1.74 | 1.28 | 0.90 to 1.82 | |
| x3 | 1.01 | 0.94 to 1.07 | 1.01 | 0.94 to 1.07 | |
# Note that the crude is with the strata
a <- getCrudeAndAdjustedModelData(fit)
a["x3", "Crude"] == exp(coef(coxph(Surv(ds$ftime, ds$fstatus == 1) ~ x3 + strata(x4), data = ds)))## x3
## TRUE