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Interv Pain Med
Interv Pain Med
Interventional Pain Medicine
2772-5944
Elsevier

S2772-5944(22)00153-4
10.1016/j.inpm.2022.100155
100155
Letters to the Editor
Reply to the:Letter to the Editor written by Hays and Peipert
Bogduk Nikolai nbogduk@bigpond.net.au

The Univeristy of Newcastle, Australia
14 11 2022
12 2022
14 11 2022
1 4 10015528 9 2022
3 10 2022
© 2022 The Author(s)
2022
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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pmcTo the Editor

I am pleased that Hays and Peipert have provided a detailed reaction to my article [1]. Perhaps their letter might bring both the article and this ensuing discussion to the attention of others.

On the matter of typographic errors, I do apologise to readers if they are confused by this. As a sole author I have difficulties checking my own work. However, the error in question, about of my article, does have a degree of internal control. Hays and Peipert correctly point out that the value for the 8th case should have been 7.9 instead of 9.9, and this is evident because the difference between scores before and after treatment is listed as 0.1. The score before was 8.0. So, the score after must be 7.9, not 9.9. This typographic error, however, does not compromise the thrust of the message, which is the illusion created by using paired t-tests.

Other matters raised by Hays and Peipert can be explained as differences in idiom and intent. It was not my objective to write a manual of statistics; nor was it to proclaim what statistical tests should be used for different types of data.

The example covered by Tables 1 and 2 of original article was contrived only to illustrate the differences between paired and unpaired t-tests, and the illusions created if and when authors use paired t-tests. That Wilcoxon rank tests should have been used for the data is a higher order discussion not immediately relevant to point that I was developing.

Similarly, the Tables and values pertaining to minimal clinically important changes (MCICs) serve only to show readers that such values exist and are available. The Tables do not serve to dictate which values should be used. Nor was my article a review of the vexations of MCICs. Fortunately, the letter of Hays and Peipert directs readers to the literature on difficulties involved in determining and using these values.

On one issue I part company with Hays and Peipert. In relation to Fig. 3 of my original article and its associated statistics, the graphic obviously shows an improvement in scores that is statistically significant; and the change does have a largish effect-size; but as a treating physician I would be disappointed if I was achieving this type of outcome. A lot of patients get a little bit better, but no-one is “fixed”, and the majority of patients still have pain scores that would render them eligible for enrolment in a trial of some other treatment. Although obvious, the improvements are mediocre, and fall short of convincing me that my treatment has worked well enough. That is why my article proceeds to advocate categorical data that show me, not how many patients I “helped”, but how many I “fixed”.

Declaration of competing interest

No conflict of interest. No financial support.
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References

1 Bogduk N. Criteria for determining if a treatment for pain works Interventional Pain Med 2022 10.1016/j.inpm.2022.100125 epub
