Background: The life expectancy of cancer patients, and the loss in expectation of life as compared to the life expectancy without cancer, is a useful measure of cancer patient survival and complement the more commonly reported 5-year survival. The estimation of life expectancy and loss in expectation of life generally requires extrapolation of the survival function, since the follow-up is not long enough for the survival function to reach 0. We have previously shown that the survival of the cancer patients can be extrapolated by breaking down the all-cause survival into two component parts, the expected survival and the relative survival, and make assumptions for extrapolation of these functions independently. When extrapolating survival from a model including covariates such as calendar year, age at diagnosis and deprivation status, care has to be taken regarding the assumptions underlying the extrapolation. There are often different alternative ways for modelling covariate effects or for assumptions regarding the extrapolation.
Methods: In this paper we describe and discuss different alternative approaches for extrapolation of survival when estimating life expectancy and loss in expectation of life for cancer patients. Flexible parametric models within a relative survival setting are used, and examples are presented using data on colon cancer in England.
Results: Generally, the different modelling assumptions and approaches give small differences in the estimates of loss in expectation of life, however, the results can differ for younger ages and for conditional estimates.
Conclusion: Sensitivity analyses should be performed to evaluate the effect of the assumptions made when modelling and extrapolating survival to estimate the loss in expectation of life.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6617672 | PMC |
| http://dx.doi.org/10.1186/s12874-019-0785-x | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Life history is a key driver of temporal fluctuations in tropical tree abundances.
Proc Natl Acad Sci U S A
January 2025
Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL 61801.
The question of what mechanisms maintain tropical biodiversity is a critical frontier in ecology, intensified by the heightened risk of biodiversity loss faced in tropical regions. Ecological theory has shed light on multiple mechanisms that could lead to the high levels of biodiversity in tropical forests. But variation in species abundances over time may be just as important as overall biodiversity, with a more immediate connection to the risk of extirpation and biodiversity loss.
View Article and Find Full Text PDFBayesian Spatial Relative Survival Model to Estimate the Loss in Life Expectancy and Crude Probability of Death for Cancer Patients.
Stat Med
February 2025
School of Mathematical Science, Queensland University of Technology, Brisbane, Australia.
To date, there have not been any population-based cancer studies quantifying geographical patterns of the loss in life expectancy (LLE) and crude probability of death due to cancer ( ). These absolute measures of survival are complementary to the more typically used relative measures of excess mortality and relative survival, and, together, they provide a fuller understanding of geographical disparities in survival outcomes for cancer patients. We propose using a spatially flexible parametric relative survival model in the Bayesian framework, which allows for the inclusion of spatial effects in hazard-level model components.
View Article and Find Full Text PDFForensic sex classification by convolutional neural network approach by VGG16 model: accuracy, precision and sensitivity.
Int J Legal Med
January 2025
Centro de Estatística e Aplicações Universidade de Lisbao, CEAUL, Faculdade de Ciências da Universidade de Lisboa no Bloco C6 - Piso 4, Lisboa, 1749-016, Portugal.
Introduction: In the reconstructive phase of medico-legal human identification, the sex estimation is crucial in the reconstruction of the biological profile and can be applied both in identifying victims of mass disasters and in the autopsy room. Due to the inherent subjectivity associated with traditional methods, artificial intelligence, specifically, convolutional neural networks (CNN) may present a competitive option.
Objectives: This study evaluates the reliability of VGG16 model as an accurate forensic sex prediction algorithm and its performance using orthopantomography (OPGs).
Physician Perceptions of the Safety and Efficacy of GLP-1 Receptor Agonists: Underestimation of Cardiovascular Risk Reduction and Discrepancies with Clinical Evidence.
J Cardiovasc Dev Dis
January 2025
Cedars Sinai Medical Center, Los Angeles, CA 90048, USA.
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and glucose-dependent insulinotropic polypeptide/GLP-1 receptor agonists (GIP/GLP-1 RAs) are emerging as effective treatments for obesity and cardiometabolic disease. This study evaluated physician perceptions of the safety and efficacy of semaglutide and tirzepatide through a questionnaire administered to 165 attending physicians specializing in internal or family medicine, with 122 responses received. Physicians reported an average patient weight loss of 9.
View Article and Find Full Text PDFSquamate reptiles may have compensated for the lack of γδTCR with a duplication of the TRB locus.
Front Immunol
January 2025
Center for Evolutionary and Theoretical Immunology, Department of Biology, University of New Mexico, Albuquerque, NM, United States.
Squamate reptiles are amongst the most successful terrestrial vertebrate lineages, with over 10,000 species across a broad range of ecosystems. Despite their success, squamates are also amongst the least studied lineages immunologically. Recently, a universal lack of γδ T cells in squamates due to deletions of the genes encoding the T cell receptor (TCR) γ and δ chains was discovered.
View Article and Find Full Text PDFWant 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!