1. The process starts with the biomarker profile assessment of all eligible patients and then according to the profile of each individual, the study population will be assigned to the different biomarker groups. Due to the fact that at the beginning of the trial we do not know the true disease control rate (i.e., the proportion of patients who demonstrate response to a treatment) the trial begins with equal randomization so that each treatment by biomarker subgroup is composed of at least one individual with a known disease control status (whether the patient will experience progression given a certain treatment).
2. Next, the trial continues with adaptive randomization of patients; this is achieved by using the Bayesian probit model to calculate the posterior disease control rate. After the posterior rate is found, we define the randomization rate as the posterior mean of the disease control rate of each treatment in each biomarker-defined subgroup.
3. The adaptive randomization process continuous until the last individual is enrolled and can stop early only in case that all treatments are dropped due to inefficacy.

Note: Whereas in many trial designs the baseline covariate (in this case the biomarker) is considered as prognostic, the design proposed by Zhou et al. (2008) allows for modelling the treatment by biomarker interactions where the biomarker is in fact predictive. The incorporation of the above hierarchical Bayesian structure allows ‘borrowing strength’ or information-sharing across patients receiving the same treatment but with different biomarker profiles, Zhou et al. (2008).