The analysis of cause of death is increasingly becoming a topic in oncology. It is usually distinguished between disease-related and disease-unrelated death. A frequently used approach is to define death as disease-related when a progression to advanced phases has occurred before, otherwise as disease-unrelated. The data are often analyzed as competing risks, while a progressive illness-death model might in fact describe the situation more precisely. In this study, we investigated under which circumstances this misspecification leads to biased estimations of the state occupation probabilities. We simulated data according to the progressive illness-death model in various settings, analyzed them with a competing risks model and with a progressive illness-death model and compared them to the true state occupation probabilities. Censoring was either added independently of the status or based on the patients' status. The simulations showed that the censoring mechanism was decisive for the bias while neither the progression hazard nor the Markov property was important. Further, we found a slightly increased standard deviation for the competing risk estimator when censoring was independent of the patients' status. For illustration, both methods were applied to two practical examples of chronic myeloid leukemia (CML): one randomized controlled trial and one registry data set. While in the first case both estimators yielded almost identical results, in the latter case, visible differences were found between both methods.
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http://dx.doi.org/10.1002/bimj.201700238 | DOI Listing |
Cancer Med
October 2024
Foodborne and Waterborne Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
BMC Neurol
June 2024
Faculty of Health/School of Medicine, Chair of Medical Biometry and Epidemiology, Witten/Herdecke University, Witten, 58448, Germany.
Artif Intell Med
January 2024
Université Paris Cité, France; HeKa team, INRIA, Paris, France; Medical Informatics, Biostatistics and Public Health Department, Georges Pompidou, Assistance Publique-Hôpitaux de Paris, France; Inserm, Centre d'Investigation Clinique 1418 (CIC1418) Epidémiologie Clinique, Paris, France.
Multi-state processes (Webster, 2019) are commonly used to model the complex clinical evolution of diseases where patients progress through different states. In recent years, machine learning and deep learning algorithms have been proposed to improve the accuracy of these models' predictions (Wang et al., 2019).
View Article and Find Full Text PDFStat Methods Med Res
September 2023
Health Services Research Unit, Foundation for the Promotion of Health and Biomedical Research of Valencia Region (FISABIO), Valencia, Spain.
Illness-death models are a class of stochastic models inside the multi-state framework. In those models, individuals are allowed to move over time between different states related to illness and death. They are of special interest when working with non-terminal diseases, as they not only consider the competing risk of death but also allow us to study the progression from illness to death.
View Article and Find Full Text PDFCancer Epidemiol Biomarkers Prev
May 2023
Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.
Metastases are the main cause of morbidity and mortality from solid tumors. Surprisingly, population-based cancer registries in various countries, including the National Cancer Institute's Surveillance, Epidemiology, and End Results program in the United States, only capture data on individuals diagnosed with cancers that are metastatic at diagnosis (M1). Metastatic recurrences of previously diagnosed, initially nonmetastatic tumors are missed.
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