Association of Uterine Leiomyoma with Type 2 Diabetes Mellitus in Young Women: A Population-Based Cohort Study (Diabetes Metab J 2024;48:1105-13)
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We appreciate the interest and thoughtful feedback of Qin and Xu on our study [1] suggesting the accelerated failure time (AFT) model. However, after careful consideration, we believe that the Cox proportional hazards model was the most appropriate choice for our research given its alignment with our objectives and the characteristics of our dataset.
Our study focused on mandatory health insurance enrollees in South Korea, utilizing health insurance claims data to assess the relative risk of diabetes. The primary goal was not to model the hazard function or survival time directly but to evaluate relative risks between groups. As such, we employed the Cox proportional hazards model, which is widely recognized for its ability to provide interpretable relative risk estimates in such contexts.
While the AFT model offers advantages in certain situations— particularly when the proportional hazards assumption does not hold or when dealing with various forms of censored data—it requires the assumption that survival times follow a specific parametric distribution [2,3]. In our case, survival data derived from health insurance claims exhibit high variability, and it is unlikely that they conform consistently to any particular parametric distribution [4]. The AFT model could lead to inaccurate estimates, potentially misrepresenting the true risk patterns in our data. The model assumes relatively constant risks over time, which may not be appropriate for capturing the time-varying risks observed in real-world data. On the other hand, the Cox model, which allows such time-varying risks, was better suited to accurately reflect the complex nature of the dataset.
Moreover, the AFT model is primarily designed to estimate survival times, but it provides limited insight into relative risk, which was the main focus of our study [5,6]. Our aim was not to predict the time of onset of diabetes but rather to compare the relative risks of diabetes occurrence across groups. The Cox model has long been considered the standard method for evaluating relative risk in survival analysis when the proportional hazards assumption holds, and it was a reliable and robust approach for the objectives of our study.
Additionally, while the AFT model is beneficial for handling a variety of censored data types, we did not encounter complex censoring in our dataset. Thanks to the nature of the South Korean health insurance system, interval censoring was not a significant issue, and our data were predominantly right-censored. This further justified our choice of the Cox model, which is well-suited for analyzing right-censored data and comparing relative risks. Furthermore, we extended our analysis using the Cox model to successfully conduct cause-specific hazard modeling, such as examining competing risks for mortality.
In conclusion, while we acknowledge the flexibility and potential utility of the AFT model in certain contexts, the Cox proportional hazards model was the more suitable choice for our study given its alignment with our research objectives and the nature of our data. The Cox model provided clear and reliable interpretations of the relative risk of diabetes onset, and it performed well under the proportional hazards assumption.
We greatly appreciate your thoughtful suggestions and hope this clarifies the rationale behind our methodological approach. Thank you again for your engagement and valuable input.
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CONFLICTS OF INTEREST
No potential conflict of interest relevant to this article was reported.