Statistical Methods in HSCT and Cellular Therapies

  • Simona IacobelliEmail author
  • Liesbeth C. de Wreede


The analysis of data describing the outcomes of patients who have received an HSCT is not only fundamental to assessing the effectiveness of the treatment but can provide invaluable information on the prognostic role of disease and patient factors. Thus, the appropriate analysis and understanding of such data are of paramount importance. This document provides an overview of the main and well-established statistical methods, as well as a brief introduction of more novel techniques. More insight is provided in the EBMT Statistical Guidelines (Iacobelli 2013).



We thank Myriam Labopin, Richard Szydlo and Hein Putter for their contributions to this chapter.

Key Points

  • Survival and competing risk endpoints need specific methods.

  • Survival analysis methods: Kaplan-Meier, Log-Rank test, Cox model.

  • Competing risks methods: Cumulative incidence curve, Gray test, Cox model, and Fine and Gray model.

  • Including events/changes of status occurring during follow-up in an analysis requires specific (advanced) methods, like multistate models.


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Authors and Affiliations

  1. 1.Department of BiologyUniversity of Rome Tor VergataRomeItaly
  2. 2.EBMTLeidenThe Netherlands
  3. 3.Department of Biomedical Data SciencesLeiden University Medical CenterLeidenThe Netherlands
  4. 4.DKMS Clinical Trials UnitDresdenGermany

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