# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```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.