**Confounding adjustment**

Please describe more clearly the selection of potential confounders and their roles in the statistical model. For example, could the results reflect residual confounding, overadjustment bias or collider stratification bias? See Shrier I, Platt RW. Reducing bias through directed acyclic graphs. BMC Med Res Methodol 2008;8:70.

Could Tables x-z reflect cases of the so-called Table 2 fallacy? See e.g. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177:292-298. Please describe the fitted models more clearly.

**Primary endpoint**

A primary endpoint is mentioned in the manuscript. This is usually something pre-defined in a randomised trial within the framework of a strategy for addressing multiplicity issues. What is the purpose of the primary endpoint in this study?

**Stepwise regression**

The multiple regression models are developed using stepwise regression, a statistical method that does not account for cause-effect relations, i.e. defining variables as independent, confounders, mediators and colliders. While the inclusion of a confounder in a statistical model is used for reducing bias, the inclusion of a mediator or collider increases bias. It is thus important to base the model development on what is known or assumed about cause-effect, see e.g. Westreich D, Greenland S. The table 2 fallacy: presenting and interpreting confounder and modifier coefficients. Am J Epidemiol 2013;177:292-298.

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