A multistate model incorporating estimation of excess hazards and multiple time scales. (8th February 2021)

• Record Type: Journal Article
• Authors: Weibull, Caroline E.; Lambert, Paul C.; Eloranta, Sandra; Andersson, Therese M. L.; Dickman, Paul W.; Crowther, Michael J.
• Is Part Of: Statistics in medicine. Volume 40:Number 9(2021)
• Page Start: 2139
• Page End: 2154
• Publication Date: 2021-02-08
• Subjects: competing risks -- excess rate -- multistate models -- multiple timescales -- relative survival -- survival analysis; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8894
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population‐level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user‐friendly Stata software with accompanying example code.