Description Usage Arguments Value Examples
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
1 | imcid(x, y, z, n, lambda, delta, maxit = 100, tol = 0.01, alpha = 0.05)
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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 λ, can be returned by |
delta |
the selected tuning parameter δ, can be returned by |
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 |
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
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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'
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