Methodological Considerations for a Risk Model Adopted into the Chronic Disease Prevention Policy of Taiwan. Comment on Chang et al. Developing and Validating Risk Scores for Predicting Major Cardiovascular Events Using Population Surveys Linked with Electronic Health Insurance Records. Int. J. Environ. Res. Public Health 2022, 19, 1319

Response to Reviewer 1 Comments

1. Summary

We sincerely thank the reviewer for this thoughtful and constructive feedback. We appreciate the reviewer’s endorsement of the need for transparent reporting of the prediction model developed by Chang, H-Y et al. (2022), as well as the insightful suggestions to improve the clarity and focus of our comments. Below, we provide a point-by-point response to the reviewer’s comments, and all corresponding revisions are highlighted in the Track Changes function of WORD in the resubmitted manuscript.

2. Questions for General Evaluation

Reviewer’s Evaluation

Response and Revisions

Does the introduction provide sufficient background and include all relevant references?

Yes

(No changes needed.)

Is the research design appropriate?

Not applicable

(No changes needed.)

Are the methods adequately described?

Not applicable

(No changes needed.)

Are the results clearly presented?

Not applicable

(No changes needed.)

Are the conclusions supported by the results?

Yes

(No changes needed.)

3. Point-by-point responses to Comments and Suggestions for Authors

Comment 1: HYC actually reported S₀(10) for MACE for both genders in the first paragraph of the results section. If these values of S(10) are legit, the predicted 10-year risk for MACE can actually be calculated from the reported information in the paper. However, in the paper S₀(10) is interpreted as "The 10-year (MACE) event probabilities for men/women," which does not fit the definition of baseline survival function in Cox regressions, i.e., the survival function when all regressors are zero. Therefore, I would be very doubtful about if S(10) is the genuine baseline survival function at year 10.

Author response 1: Thank you very much for this insightful clarification. We fully agree with the reviewer’s observation. In our revised manuscript (Paragraph 3), we clarify that the values reported by HYC—although referred to as “10-year event probabilities”—appear to correspond more closely to population-level, event-free survival estimates rather than to the baseline survival function as defined in Cox regression models. We now state this distinction explicitly and explain why the definition used by HYC introduces ambiguity in interpreting or replicating absolute risk estimates.

Revised Text (Paragraph 3):
“... The Chang paper reports the “S₀(10)” for MACE as 0.82 for men and 0.90 for women, but the description is inconsistent. The authors initially refer to these as “10-year event probabilities,” which typically denote 1 - S(10), yet subsequently clarify that “82% of men and 90% of women remained MACE-free,” implying S(10). While the latter is more consistent with survival probability, it remains unclear whether this estimate reflects the Cox model’s baseline survival (i.e., with all covariates set to zero), or a population-level figure.”

Comments 2: Since the goal of the HYC paper is to develop a prediction model rather than to interpret the regression coefficients, whether proportional hazards is satisfied would not be of significant importance (the prediction performance would be more important). I would suggest that the commentor tone down on this line of argument.

Author Response 2: We appreciate the reviewer’s important reminder about the distinction between explanatory and predictive modeling. In light of this, we have revised our comments to reflect a more appropriate tone. Specifically, in our discussion of statistical considerations (Paragraph 5), we have removed the emphasis on testing the proportional hazards assumption and instead focused on the need to describe the model-building process (e.g., time-varying effects, parameter estimation), which remains relevant even in prediction-focused models.

Revised Text (Paragraph 5):
“... While it is generally acknowledged that verifying assumptions like proportional hazards may be less critical for prediction-focused models, providing a clear description of how the model was developed, particularly regarding time-varying effects or parameter estimation methods, would greatly enhance transparency and credibility.”

Comments 3: The commentor may consider citing the TRIPOD or TRIPOD + AI statement to reinforce the requirement of transparent reporting in prediction models.

Author response 3: Thank you for this excellent suggestion. We have now cited the TRIPOD statement (Collins et al., 2015) in both the Introduction and the Conclusion to emphasize the importance of transparent reporting.

4. Response to Comments on the Quality of English Language

The reviewer did not raise any concerns about language quality. No changes were made.

5. Additional clarifications

We thank the reviewer again for the thoughtful and constructive input. We have implemented all suggestions with the aim of improving the clarity, tone, and relevance of our comments. We hope that the revised version provides a more balanced and constructive contribution to the academic discussion surrounding this influential model.