dcurve_stability: Plot decision curve stability across bootstrap replicates

In pminternal: Internal Validation of Clinical Prediction Models

Plot decision curve stability across bootstrap replicates

Description

A decision curve (in)stability plot shows decision curves for bootstrap models evaluated on original outcome. A stable model should produce curves that differ minimally from the 'apparent' curve. See Riley and Collins (2023).

Usage

dcurve_stability(
  x,
  thresholds = seq(0, 0.99, by = 0.01),
  xlim,
  ylim,
  xlab,
  ylab,
  col,
  subset,
  plot = TRUE
)

Arguments

x	an object produced by validate with method = "boot_*" (or boot_optimism with method="boot")
thresholds	points at which to evaluate the decision curves (see dcurves::dca)
xlim	x limits (default = range of thresholds)
ylim	y limits (default = range of net benefit)
xlab	a title for the x axis
ylab	a title for the y axis
col	color of points (default = grDevices::grey(.5, .5))
subset	vector of observations to include (row indices). This can be used to select a random subset of observations.
plot	if FALSE just returns curves (see value)

Value

plots decision curve (in)stability. Invisibly returns a list containing data for each curve. These are returned from dcurves::dca. The first element of this list is the apparent curve (original model on original outcome).

References

Riley, R. D., & Collins, G. S. (2023). Stability of clinical prediction models developed using statistical or machine learning methods. Biometrical Journal, 65(8), 2200302. doi:10.1002/bimj.202200302

Examples

set.seed(456)
# simulate data with two predictors that interact
dat <- pmcalibration::sim_dat(N = 2000, a1 = -2, a3 = -.3)
mean(dat$y)
dat$LP <- NULL # remove linear predictor

# fit a (misspecified) logistic regression model
m1 <- glm(y ~ ., data=dat, family="binomial")

# internal validation of m1 via bootstrap optimism with 10 resamples
# B = 10 for example but should be >= 200 in practice
m1_iv <- validate(m1, method="boot_optimism", B=10)

dcurve_stability(m1_iv)

pminternal documentation built on April 4, 2025, 3:30 a.m.