EurekaMag.com logo
+ Site Statistics
References:
53,869,633
Abstracts:
29,686,251
+ Search Articles
+ Subscribe to Site Feeds
EurekaMag Most Shared ContentMost Shared
EurekaMag PDF Full Text ContentPDF Full Text
+ PDF Full Text
Request PDF Full TextRequest PDF Full Text
+ Follow Us
Follow on FacebookFollow on Facebook
Follow on TwitterFollow on Twitter
Follow on LinkedInFollow on LinkedIn

+ Translate

Estimating cancer prevalence using mixture models for cancer survival



Estimating cancer prevalence using mixture models for cancer survival



Statistics in Medicine 21(9): 1257-1270



Knowledge of cancer prevalence is useful for estimating the ongoing level of resources utilized in the treatment of disease and is of some public health interest. Cancer prevalence is estimated first as the proportion of persons previously diagnosed (PD) with cancer that are still alive; and second as the proportion of individuals in the population who were previously diagnosed with cancer and who have not been cured (NC). The proportion of cases that are cured is estimated by assuming that the cured and uncured cases have distinct survival patterns. The hazard for cured cases is assumed to be a multiple of the hazard from causes other than cancer in the general population. The hazard for uncured cases is assumed to have two independent components: one corresponding to the disease-specific hazard, and the other a multiple of the population hazard from 'other causes'. Future prevalence estimates are obtained by projecting the survival of current prevalent cases as well as the survival of future incident cases.

(PDF emailed within 0-6 h: $19.90)

Accession: 010604912

Download citation: RISBibTeXText

PMID: 12111877

DOI: 10.1002/sim.1101



Related references

Kernel mixture survival models for identifying cancer subtypes, predicting patient's cancer types and survival probabilities. Genome Informatics. International Conference on Genome Informatics 15(2): 201-210, 2005

Survival prediction models for estimating the benefit of post-operative radiation therapy for gallbladder cancer and lung cancer. AMIA ... Annual Symposium Proceedings. AMIA Symposium: 348-352, 2008

Mixture models for cancer survival analysis: Application to population-based data with covariates. Statistics in Medicine 18(4): 441-454, Feb 28, 1999

Cure models for estimating hospital-based breast cancer survival. Asian Pacific Journal of Cancer Prevention 11(2): 387-391, 2011

Estimating the crude probability of death due to cancer and other causes using relative survival models. Statistics in Medicine 29(7-8): 885-895, 2010

Estimating the loss in expectation of life due to cancer using flexible parametric survival models. Statistics in Medicine 32(30): 5286-5300, 2014

Application of mixture models for estimating the prevalence of cigarette smoking in hamadan, iran. Journal of Research in Health Sciences 10(2): 110-115, 2010

Estimating cancer incidence, prevalence, and the number of cancer patients treated with antitumor therapy in 2015 and 2020 -  analysis of the Czech National Cancer Registry. Klinicka Onkologie 28(1): 30-43, 2015

Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models. Journal of the American Statistical Association 98(464): 1063-1078, 2003

Combining assays for estimating prevalence of human herpesvirus 8 infection using multivariate mixture models. Biostatistics 9(1): 137-151, 2007

Estimating and modelling cure in population-based cancer studies within the framework of flexible parametric survival models. Bmc Medical Research Methodology 11: 96-96, 2011

Comparison of efficacy and toxicity of traditional Chinese medicine (TCM) herbal mixture LQ and conventional chemotherapy on lung cancer metastasis and survival in mouse models. Plos One 9(10): E109814-E109814, 2015

Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions. Quality of Life Research 23(10): 2909-2916, 2015

Estimating regional variation in cancer survival: a tool for improving cancer care. Cancer Causes & Control 15(6): 611-618, 2004