imcid {MCID}

Point and interval estimation for the MCID at the individual level

Description

We formulate the individualized MCID as a linear function of the patients' clinical profiles. imcid returns the point estimate for the linear coefficients of the MCID at the individual level

Usage

imcid(x, y, z, n, lambda, 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

z

a vector or matrix denoting the patient's clinical profiles

n

the sample size

lambda

the selected tuning parameter \lambda, can be returned by cv.imcid

delta

the selected tuning parameter \delta, can be returned by cv.imcid

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 estimates for the linear coefficients of the individualized MCID and their standard errors, and the corresponding confidence intervals based on the asymptotic normality

Examples

n <- 500
lambdaseq <- 10 ^ seq(-3, 3, 0.1)
deltaseq <- seq(0.1, 0.3, 0.1)
a <- 0.1
b <- 0.55
c <- -0.1
d <- 0.45
### True linear coefficients of the individualized MCID: ###
### beta0=0, beta1=0.5 ###

set.seed(115)
p <- 0.5
y <- 2 * rbinom(n, 1, p) - 1
z <- rnorm(n, 1, 0.1)
y_1 <- which(y == 1)
y_0 <- which(y == -1)
x <- c()
x[y_1] <- a + z[y_1] * b + rnorm(length(y_1), 0, 0.1)
x[y_0] <- c + z[y_0] * d + rnorm(length(y_0), 0, 0.1)
sel <- cv.imcid(x = x, y = y, z = z, lamseq = lambdaseq,
         delseq = deltaseq, k = 5, maxit = 100, tol = 1e-02)
lamsel <- sel$'Selected lambda'
delsel <- sel$'Selected delta'
result <- imcid(x = x, y = y, z = z, n = n, lambda = lamsel,
         delta = delsel, maxit = 100, tol = 1e-02, alpha = 0.05)
result$'Point estimates'
result$'Standard errors'
result$'Confidence intervals'