In many haematological diseases, the survival probability is the key outcome. However, when the population of patients is rather old and the follow-up long, a significant proportion of deaths... Show moreIn many haematological diseases, the survival probability is the key outcome. However, when the population of patients is rather old and the follow-up long, a significant proportion of deaths cannot be attributed to the studied disease. This lessens the importance of common survival analysis measures like overall survival and shows the need for other outcome measures requiring more complex methodology. When disease-specific information is of interest but the cause of death is not available in the data, relative survival methodology becomes crucial. The idea of relative survival is to merge the observed data set with the mortality data in the general popu-lation and thus allow for an indirect estimation of the burden of the disease.In this work, an overview of different measures that can be of interest in the field of haema-tology is given. We introduce the crude mortality that reports the probability of dying due to the disease of interest; the net survival that focuses on excess hazard alone and presents the key measure in comparing the disease burden of patients from populations with different general population mortality; and the relative survival ratio which gives a simple comparison of the patients' and the general population survival. We explain the properties of each measure, and some brief notes are given on estimation. Furthermore, we describe how association with cova-riates can be studied. All the methods and their estimators are illustrated on a sub-cohort of older patients who received a first allogeneic hematopoietic stem cell transplantation for myelodys-plastic syndromes or secondary acute myeloid leukemia, to show how different methods can provide different insights into the data. Show less
Breeschoten, J. van; Eertwegh, A.J.M. van den; Hilarius, D.L.; Haanen, J.B.; Blank, C.U.; Aarts, M.J.B.; ... ; Wreede, L.C. de 2023
IntroductionWhen analysing patient survival, one is often interested in cause of death. Little is known about the presence of population mortality in advanced melanoma patients. The aim of this... Show moreIntroductionWhen analysing patient survival, one is often interested in cause of death. Little is known about the presence of population mortality in advanced melanoma patients. The aim of this study was to assess population mortality after different response states in advanced melanoma patients in the Netherlands, and analyse the contribution of disease and population mortality for different age groups.MethodsWe selected patients diagnosed between 2013 and 2019 with unresectable IIIC or stage IV melanoma, registered in the Dutch Melanoma Treatment Registry. A multi-state model with response states integrating population mortality was fitted. One-year landmark analyses were performed to assess outcomes after each response state.ResultsOverall, 5119 patients were selected. Five-year probabilities of melanoma-related mortality in patients alive in complete response at one year after diagnosis increased with age, and was 17.2% (95% confidence interval: 13.0–21.4) for patients aged <65 years and 28.7% (95% confidence interval: 24.3–33.1) in patients aged ≥80 years. Population mortality only played a large role for older patients (75 years and above) alive at 1 year after diagnosis with a partial or complete response.ConclusionEven though survival outcomes of advanced melanoma patients have improved over the last decade, the vast majority of patients still die due to melanoma-related mortality. Show less
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... Show moreMulti-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. Show less
