In addition to the lines mentioned in “Study Design and Participants” in the original article, the randomization process and the sample size calculations are further elaborated in the text below:

According to the CONSORT guidelines (14), a table showing baseline demographic characteristics for each study group should be included. Even though the randomization process prevents selection bias, it does not automatically assume that the study groups are equivalent at baseline (2). Since the number of schools in each study group was the same (eight schools in the intervention group and eight schools in the control group) and as the number of schools per location was also equal (eight schools from the urban region and eight schools from the rural one), our focus turned to evaluating the demographic characteristics at the individual level. This was further proved by the statistical tests conducted by the authors of the commentary as there was no significant difference in any variable of Table 1 related to demographics (1).

To evaluate the appropriateness of the randomization process related to the demographic characteristics of the sample, both Chi-square and independent t-test were performed. As was mentioned in the paper (see Statistical Analyses), Chi-square tests were used to analyse the differences in frequency of the categorical background characteristics (such as gender) between study groups, and the independent t-test was applied to compare the variation between study groups at baseline for continuous variables (such as the BMI z-score). The same tests were also used for these purposes in other cRCT (5, 15–18).

The current study took into consideration the clustered design of the study by applying either the multilevel analysis or other tests as indicated below.

In the current statistical analysis, clustering included adolescents, schools, and school location. The grade or class (grades 10 and 11) was considered as an independent variable and not a cluster for the following reasons: (i) class size: the number of students per class in grade 10 is higher compared to grade 11. This means that the intervention may be more effective in smaller classes compared to larger ones; (ii) age category: to be able to evaluate the effectiveness of the intervention among older vs. younger adolescents later on. This explains the adjustment of the analysis for grade/class in Table 2 and the inclusion of this particle variable in the subgroup analysis in Table 3 (as indicated in the Statistical Analyses on page 5).

Concerning the other tests, some references state that there are cases that do not require running a multilevel analysis, and a multiple regression test could be used instead (19). For instance, when the random intercept is non-significant, it makes sense not to run the multilevel analysis, especially when the intercept does not vary from one school to another.

It is worth noting that after running the multilevel analyses for the unhealthy items score, we obtained a B value of (−1.40) with a 95% Confidence Interval of (−1.88) to (−0.93), and p-value of < 0.001. The direction, magnitude, and significance of the results were similar as with the multi-regression analyses.

As reproducibility is the base of any scientific work, we thank the authors for their time in reproducing the statistical analyses. The authors reproduced the results based on the shared data, the codebook, and the described methodology in the paper, which suggests that the methodology related to running the tests is thoroughly and sufficiently described allowing for a sound reproduction of the results.

It is important to note that adolescents, schools, and school location were considered in the multilevel analysis, whereas the authors of the commentary have only relied on adolescents and schools. Concerning the reproduced tests in Table 1 of the commentary, the small differences related to the confidence of interval are due to adding one additional covariate (i.e., age) which was not considered in the initial analysis. As mentioned in the footnote of Table 2, all models were adjusted for gender, class/grade, type of school, school location, BMI z-score, and obtained scores at baseline. Age was not among them because we already included “class/grade” which takes into consideration older and younger adolescents.

As for the reproduced tests in Table 2 of the commentary, the significant differences in the B values of the total dietary knowledge score based on the baseline score are due to dividing the sample according to the healthy items score at baseline, and not according to the knowledge score at baseline (pre-intervention), which is not what was done in the analysis shown in the paper. This is indicated in the attached code (see “if healthy_score_category_pre = = 1”).

It is also important to note that some variables were counted more than once in the reproduced results of Siddique et al. (1). For instance, in the initial analysis shown in the paper, when dividing the sample into two groups: grade 10 and grade 11, the grade/class variable was not added to the analysis, as it was already taken into consideration. The same goes for weight status and knowledge score at baseline. In other words, when dividing the sample based on the weight status, there is no need to adjust the model for BMI z-score, and when dividing the sample based on the baseline knowledge score, there is no need to adjust for the baseline knowledge score once again.

Additionally, the significant differences in the healthy items adherence score based on the location (Table 3 of the commentary) are also due to adding the wrong variable. The dependent variable is the healthy items score post-intervention, and not the unhealthy items score post-intervention.

Concerning the revised tests, the same comments apply as mentioned above.

Finally, we would like to thank the authors for engaging in the discussion and appreciate the opportunity to provide further details with regard to our original paper. We believe Siddique and colleagues have presented a different approach for the data analysis, presenting the reader with a valuable alternative analytical approach to consider. In this response, we have provided a thorough explanation of why certain choices in our analytical approach were made. We feel it is important to stress that both approaches can be argued for, and both result in the same conclusion.

LS: Writing – review & editing, Writing – original draft. JG: Writing – review & editing. SK: Writing – review & editing.