Analysis of cause of death: Competing risks or progressive illness-death model?

Biom J

Department of Epidemiology, Medical Statistics and Decision Making, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.

Published: March 2019

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.

Download full-text PDF

Source
http://dx.doi.org/10.1002/bimj.201700238DOI Listing

Publication Analysis

Top Keywords

progressive illness-death
16
competing risks
12
illness-death model
12
analysis death
8
risks progressive
8
analyzed competing
8
competing
4
death competing
4
progressive
4
illness-death
4

Similar Publications

Global Trend in Pancreatic Cancer Prevalence Rates Through 2040: An Illness-Death Modeling Study.

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.

Article Synopsis
  • Pancreatic cancer (PC) is becoming increasingly lethal, prompting a study using a multi-state illness-death model (IDM) to predict its prevalence globally until 2040.
  • The forecast indicates a 31.45% rise in PC prevalence since 1990, reaching about 6.093 cases per 100,000 people by 2040, with females showing a steeper increase compared to males.
  • The analysis highlights that regions with lower socioeconomic status will likely face the greatest risks for rising PC rates, emphasizing the need for targeted interventions and awareness efforts.
View Article and Find Full Text PDF
Article Synopsis
  • Parkinson's disease (PD) is a progressive neurodegenerative disorder with both motor and psychiatric symptoms; this study aimed to estimate its age- and sex-specific incidence in Germany using a mathematical model based on prevalence and mortality data.
  • The study utilized a partial differential equation (PDE) and bootstrapping techniques to accurately analyze data from 2010 to 2019, finding that men had consistently higher incidences of PD than women, particularly peaking at age 85.
  • The findings indicate the effectiveness of the illness-death model in estimating PD incidence, highlighting a reliable method that reduces the risk of bias in future research.
View Article and Find Full Text PDF

MS-CPFI: A model-agnostic Counterfactual Perturbation Feature Importance algorithm for interpreting black-box Multi-State models.

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 PDF

A Bayesian multivariate spatial approach for illness-death survival models.

Stat 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 PDF

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.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!