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The following content was supplied by the authors as supporting material and has not been copy-edited or verified by JBJS.

Appendix 1

An Algorithm was developed in R to simulate PROMs at Baseline and Follow Up and illustrate

how a portion of patients having a significant effect can hide poorer outcomes of the rest of the

cohort. The input is: number of patients having an effect from a procedure, mean and standard

deviation at both Baseline and Follow up. The algorithm then calculates the number of patients

having no effect (the same score at Baseline and Follow up, which it draws from the pool of

Baseline scores) which can be part of the cohort until 1. p-value <.05 is overturned and 2. Mean

difference between Baseline and Follow Up is > MCID.

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OPYRIGHT

©

BY

T

HE

J

OURNAL OF

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ONE AND

J

OINT

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URGERY

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NCORPORATED

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RR ET AL

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ALL FOR A

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TANDARDIZED

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http://dx.doi.org/10.2106/JBJS.21.00030 Page 2

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numOrigValues = 75 meanDF1 = 37 meanDF2 = 84

MCID = meanDF2 - meanDF1

timeZeroDF = rnorm(numOrigValues, mean = meanDF1, sd = 8) timeTwelveDF = rnorm(numOrigValues, mean = meanDF2, sd = 12)

CalculationDF = data.frame(mean1 = mean(timeZeroDF), sd1 = sd(timeZeroDF), n1 = length(timeZeroDF),

mean2 = mean(timeTwelveDF), sd2 = sd(timeTwelveDF), n2 = length(timeTwelveDF), MCID = MCID,

p_value = t.test(timeZeroDF, timeTwelveDF, paired = TRUE, alternative =

"two.sided")$p.value)

x = 1

while (CalculationDF$p_value[x] <= 0.05) {

x = x + 1

CalculationDF[nrow(CalculationDF) + 1,] = c(0, 0, 0, 0, 0, 0, MCID, 0)

timeZeroDF = append(timeZeroDF, sample(timeZeroDF, 1)) timeTwelveDF = append(timeTwelveDF, sample(timeZeroDF, 1)) CalculationDF$mean1[x] = mean(timeZeroDF)

CalculationDF$sd1[x] = sd(timeZeroDF)

CalculationDF$n1[x] = length(timeZeroDF) - numOrigValues