Multi-state models and outcome prediction in bone marrow transplantation.
Multi-state models have proved versatile and useful in the statistical analysis of the complicated course of events after bone marrow transplantation. Working from data from the International Bone Marrow Transplant Registry, we show that summary probability calculations may be useful to explore hypothetical scenarios where some transition intensities are set by the researcher. A multi-state Markov process model is specified with six states: the initial state 0; acute; chronic and both acute and chronic graft-versus-host disease A, C and AC; relapse R and death in remission D. Transition rates between the states are estimated using Nelson-Aalen estimators and Cox regression models and combined to transition probability estimators using Aalen-Johansen product integration. Besides the estimated transition probabilities to D and R we explore hypothetical probabilities obtained by artificially changing certain transition intensities, with the general purposes of getting summary views of the development for actual patients 'in this world' and of exploring the intrinsic information from real patients about consequences of various changed conditions.
Keiding
N
N
Department of Biostatistics, University of Copenhagen, Denmark. n.keiding@biostat.ku.dk
Klein
J P
JP
Horowitz
M M
MM
D016026:Bone Marrow Transplantation / Q000276:immunology*; D006086:Graft vs Host Disease / Q000209:etiology*; D006801:Humans; D007938:Leukemia / Q000628:therapy; D008390:Markov Chains; D008954:Models, Biological; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012008:Recurrence; D016896:Treatment Outcome