Patients undergoing stem cell transplantation may require transfusion of units (bags) of packed red blood cells (PRBCs). Modelling of PRBC usage is important not only for prediction of transfusion requirements in future patients but also for its use as an inverse surrogate for engraftment, that is transplantation success. Inspection of PRBC unit usage reveals a strong preference for even numbers, which is caused by behavioural preference on the part of prescribing physicians. Digit preference is a phenomenon observed more commonly with self-reported data: typically survey respondents round recalled quantities such as the age at which a life event occurred to multiples of 5 or 10. In all cases, we can conceive of a latent variable, which has a smooth distribution, which is transformed via stochastic rules to a discrete variable with probability spikes at preferred digits. We propose a modelling framework based on a latent variable specification and stochastic transformation to the spiked distribution. Loglinear models for the mean of the process are implemented. Specification of the stochastic rules is important to success in accurate modelling of the process.