cv.pmcid: Selection of the tuning parameter for determining the MCID at...

In zzhou0721/MCID: Estimating the Minimal Clinically Important Difference

Description

cv.pmcid returns the optimal tuning parameter δ selected from a given grid by using k-fold cross-validation. The tuning parameter is selected for determining the MCID at the population level

Usage

cv.pmcid(x, y, delseq, k = 5, maxit = 100, tol = 0.01)

Arguments

x	a continuous variable denoting the outcome change of interest
y	a binary variable indicating the patient-reported outcome derived from the anchor question
delseq	a vector containing the candidate values for the tuning parameter δ, where δ is used to control the difference between the 0-1 loss and the surrogate loss. We recommend selecting the possible values from the neighborhood of the standard deviation of x
k	the number of groups into which the data should be split to select the tuning parameter δ by cross-validation. Defaults to 5
maxit	the maximum number of iterations. Defaults to 100
tol	the convergence tolerance. Defaults to 0.01

Value

a list including the selected tuning parameter and the value of the corresponding target function

Examples

rm(list = ls())
n <- 500
deltaseq <- seq(0.1, 1, 0.1)
a <- 0.2
b <- -0.1
p <- 0.5

set.seed(115)
y <- 2 * rbinom(n, 1, p) - 1
y_1 <- which(y == 1)
y_0 <- which(y == -1)
x <- c()
x[y_1] <- rnorm(length(y_1), a, 0.1)
x[y_0] <- rnorm(length(y_0), b, 0.1)

sel <- cv.pmcid(x = x, y = y, delseq = deltaseq, k = 5,
         maxit = 100, tol = 1e-02)
sel$'Selected delta'
sel$'Function value'

zzhou0721/MCID documentation built on Aug. 22, 2020, 8:41 p.m.