pmcid: Point and interval estimation for the MCID at the population...

In MCID: Estimating the Minimal Clinically Important Difference

Description

pmcid returns the point estimate for the MCID at the population level

Usage

pmcid(x, y, n, delta, maxit = 100, tol = 0.01, alpha = 0.05)

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
n	the sample size
delta	the selected tuning parameter δ, can be returned by cv.pmcid
maxit	the maximum number of iterations. Defaults to 100
tol	the convergence tolerance. Defaults to 0.01
alpha	nominal level of the confidence interval. Defaults to 0.05

Value

a list including the point estimate of the population MCID and its standard error, and the confidence interval based on the asymptotic normality

Examples

n <- 500
deltaseq <- seq(0.1, 1, 0.1)
a <- 0.2
b <- -0.1
p <- 0.5
### True MCID is 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)
delsel <- sel$'Selected delta'

result <- pmcid(x = x, y = y, n = n, delta = delsel,
            maxit = 100, tol = 1e-02, alpha = 0.05)
result$'Point estimate'
result$'Standard error'
result$'Confidence interval'

MCID documentation built on Sept. 10, 2021, 5:07 p.m.