We have devised an online calculator to predict quality of life in women with breast cancer when they finish systemic adjuvant therapy, which is potentially useful for decision making. To do so, we have fit a Bayesian ordinal model by means of software that was programmed for Covid-19 trials, to illustrate its usefulness in assessing cancer patients. The model enables some of the issues surrounding QoL analysis to be resolved with acceptable complexity and accurately captures the leading factors that affect global health status (type of surgery, interaction with age, or perceived risk of recurrence). The study exhibits the feasibility of the post hoc Bayesian analysis with QoL data that can be extrapolated to clinical trials. This Bayesian model is also a potentially useful tool in making decisions based on the foreseeable evolution of QoL, when facing equally beneficial treatments, such as mastectomy versus breast-conserving surgery. In this way, we show how the effect of surgery on quality of life depends largely on age and other modulating factors.