Integrating relative survival in multi-state models—a non-parametric approach

Statistical Methods in Medical Research, vol. 31, pp. 997–1012

Abstract

Multi-state models provide an extension of the usual survival/event-history analysis setting. In the medical domain, multi-state models give the possibility of further investigating intermediate events such as relapse and remission. In this work, a further extension is proposed using relative survival, where mortality due to population causes (i.e. non-disease-related mortality) is evaluated. The objective is to split all mortality in disease and non-disease-related mortality, with and without intermediate events, in datasets where cause of death is not recorded or is uncertain. To this end, population mortality tables are integrated into the estimation process, while using the basic relative survival idea that the overall mortality hazard can be written as a sum of a population and an excess part. Hence, we propose an upgraded non-parametric approach to estimation, where population mortality is taken into account. Precise definitions and suitable estimators are given for both the transition hazards and probabilities. Variance estimating techniques and confidence intervals are introduced and the behaviour of the new method is investigated through simulations. The newly developed methodology is illustrated by the analysis of a cohort of patients followed after an allogeneic hematopoietic stem cell transplantation. The work has been implemented in the R package mstate.

Authors 6

1. Damjan Manevski corresponding

   University of Ljubljana

   Affiliation as printed

   Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Slovenia

2. Hein Putter Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, the Netherlands

3. Maja Pohar Perme

   University of Ljubljana

   Affiliation as printed

   Institute for Biostatistics and Medical Informatics, Faculty of Medicine, University of Ljubljana, Slovenia

4. Edouard F. Bonneville Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, the Netherlands

5. Johannes Schetelig
   Affiliation as printed

   Medical Clinic I, University Hospital, Germany

6. Liesbeth C. de Wreede Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, the Netherlands