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In “Designing robust N-of-1 studies for precision medicine: Simulation study and design recommendations” by Percha et al [

In Figure 4a, we see that for effect sizes of 0.1, 0.2, and 0.3, more than 100 samples are needed to obtain a power of 0.8 (at a standard 5% significance level). For an effect size of 0.4, at least 100 samples are needed. For effect sizes of 0.5, 0.6, 0.7, 0.8, 0.9, and 1.0, the numbers of samples needed to attain a power of 0.8 are approximately 65, 45, 35, 26, 21, and 18, respectively. [Figure 4]

Since Figure 4a is exactly equivalent to power curves from a two-sample, equal-variance

Figure 4c caption: “(ie, number of samples

Figure 4a and 4b: the label for the horizontal axis should be “Number of samples

Page 8: “In Figure 4a, we see that for effect sizes of 0.1, 0.2, and 0.3, more than 100 samples

Page 9: “For an effect size of 0.5 and σ_{p}=0.0, 0.4, 0.8, 1.2, 1.6, 2.0, the numbers of samples

Page 9: “For σ_{p}=0.0, 0.4, 0.8, 1.2, 1.6, 2.0 and α=0.1, the numbers of samples

For effect sizes ranging from 0.1 to 1.0, power of a 0.05 level two-sample

#--- R code for generating

library(pwr)

color <- c("#F8766D", "#D89000", "#A3A500", "#39B600", "#00BF7D", "#00BFC4",

"#00B0F6", "#9590FF", "#E76BF3", "#FF62BC")

#--- Producing the power plot (upper portion of figure)

plot(50, 1, type = 'n', xlim = c(0,100), ylim = c(0,1), axes = FALSE,

ylab = substitute(paste("2-sample ", italic('t'), " test power")),

xlab = substitute(paste(italic('n'), " = # of samples for 1 of 2 treatments") ) )

axis(1, at = seq(0, 100, 20)); axis(2, at = seq(0, 1, .2), las = 2)

abline(v = seq(10, 100, 10), col = 'gray80')

abline(h = seq(.1, 1, .1), col = 'gray85')

#--- Filling in the power plot

for(.d in 10:1/10){

tmp.power <- NULL

for( .n in 5:100){

#--- Computes power for a 2-sample t-test, each sample with n observations.

p <- pwr.t.test(n = .n, d = .d, sig.level = .05, power = NULL,

type = "two.sample", alternative = "two.sided")$power

tmp.power <- c(tmp.power, p)

}

lines(5:100, tmp.power, lwd=3, col=color[.d*10])

}

#--- Producing the the legend (lower portion of figure)

plot(50, 1, type = 'n', xlim = c(0,100), ylim = c(.25, .75),

axes = FALSE, xlab = 'Effect Size', ylab = '', cex.lab=1.5)

x <- 15

for(iter in seq(2,10,2)){

mult <- (iter/2)

.x <- x*mult

segments( .x-3, 0.67, .x+3, lwd=5, col= color[iter-1])

text(.x+3, .67, pos=4, (iter-1)/10, cex=1.25)

segments( .x-3, 0.33, .x+3, lwd=5, col= color[iter] )

text(.x+3, .33, pos=4, (iter)/10, cex=1.25)

}

Percha and colleagues have agreed to the above changes; these changes have been made to the original paper.

None declared.