Estimation and prediction in a multi-state model for breast cancer

H Putter*, J van der Hage, Geertruida H. de Bock, R Elgalta, CJH van de Velde

*Corresponding author for this work

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Abstract

An important aim in clinical studies in oncology is to study how treatment and prognostic factors influence the course of disease of a patient. Typically in these trials, besides overall survival, also other endpoints such as locoregional recurrence or distant metastasis are of interest. Most commonly in these situations, Cox regression models are applied for each of these endpoints separately or to composite endpoints such as disease-free survival. These approaches however fail to give insight into what happens to a patient after a first event. We re-analyzed data of 2795 patients from a breast cancer trial (EORTC 10854) by applying a multi-state model, with local recurrence, distant metastasis, and both local recurrence and distant metastasis as transient states and death as absorbing state. We used an approach where the clock is reset on entry of a new state. The influence of prognostic factors on each of the transition rates is studied, as well as the influence of the time at which intermediate events occur. The estimated transition rates between the states in the model are used to obtain predictions for patients with a given history. Formulas are developed and illustrated for these prediction probabilities for the clock reset approach.

Original languageEnglish
Pages (from-to)366-380
Number of pages15
JournalBIOMETRICAL JOURNAL
Volume48
Issue number3
DOIs
Publication statusPublished - Jun-2006
Externally publishedYes

Keywords

  • multi-state model
  • survival analysis
  • prognostic factors
  • prediction
  • BONE-MARROW TRANSPLANTATION
  • PERIOPERATIVE CHEMOTHERAPY
  • EUROPEAN ORGANIZATION
  • COMPETING RISKS
  • SURVIVAL
  • PROBABILITIES
  • REGRESSION

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