A major advantage of the proportional hazard model is that it is semi-parametric, and baseline survival function h0(t) does not need to be specified to estimate the parameters. However, sometimes the baseline survival function is useful to understand the change in survival over time and combined with the estimated coefficients and specific covariate values to estimate the survival experience of subgroups of subjects of particular interest. The baseline survival function is estimated using maximum-likelihood at β=0 for the coefficients. In many cases, the baseline survival function for β=0 isn't easily visualized, and some recommend that the mean be subtracted before continuous variables are analyzed. In these cases, the reference survival function at β =0 for categorical variables (equivalent to the reference group) and β =mean for continuous variables is usually used.
From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/fit-model/proportion-alhazards/baselinesurvivalfunction
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