Multistate survival analysis usually involves a series of detailed regressional analyses describing transitions between various states. There is an often neglected need for the many estimates resulting from such an analysis to be re-synthesized into summary statements, such as prediction of various outcomes from specified patient histories. Arjas and Erola recently proposed a framework for dynamic probabilistic causality which has calculation of such prediction statements as a central tool. We illustrate these procedures on data from a multicentre bone marrow transplantation study, with death while in remission and relapse as terminal events and recovery of the patients's platelets to a normal level and the onset of acute graft-versus-host disease as intermediate events, using Cox regression models throughout. Among the features illustrated by the resulting plots is a strong effect on death while in remission if the platelets do not recover within the first three months.