Autologous Stem Cell Transplant Study in Lymphoma Patients: Statistical Analysis of Multi-State Models

Abstract

Survival analysis is a collection of statistical methods for inference on time-to- event data.If several different events occur, specific methods have to be used in order to capture the complex structure of the data. A multi-state model is used for modeling a stochastic process which at any time occupies one out of a discrete set of possible states (e.g. healthy, ill, dead).Transitions between the states represent the events. The multi-state model is most commonly used in the form of a Markov model, where the transitions follow a Markov process. Recent papers have shown that this approach is very flexible and offers broad applications in biomedical research. The aim of this paper is to recall the Markov multi-state model and to apply the multi-state methods to a real data set obtained from an Autologous Stem Cell Transplant Study in lymphoma patients carried out at the Clinic of Haemato-oncology of the University Hospital in Olomouc (Czech Republic). States observed in this study are healthy (i.e. in remission), relapsed and dead. The influence of several clinically relevant risk factors (especially quality of the transplanted graft) is analyzed with respect to their effect on the occurrence of the events of interest. The results obtained by means of multi-state models are compared to those of standard survival analysis methods. The work on this project was partially supported by institutional grant RVO:67985807 and ESF project CZ.1.07/2.4.00/174.0117.