Literature DB >> 12111877

Estimating cancer prevalence using mixture models for cancer survival.

Norman Phillips1, Andrew Coldman, Mary L McBride.   

Abstract

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. Copyright 2002 John Wiley & Sons, Ltd.

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Year:  2002        PMID: 12111877     DOI: 10.1002/sim.1101

Source DB:  PubMed          Journal:  Stat Med        ISSN: 0277-6715            Impact factor:   2.373


  4 in total

1.  Prevalence estimates for primary brain tumors in the United States by age, gender, behavior, and histology.

Authors:  Kimberly R Porter; Bridget J McCarthy; Sally Freels; Yoonsang Kim; Faith G Davis
Journal:  Neuro Oncol       Date:  2010-02-08       Impact factor: 12.300

2.  Cancer prevalence in the United Kingdom: estimates for 2008.

Authors:  J Maddams; D Brewster; A Gavin; J Steward; J Elliott; M Utley; H Møller
Journal:  Br J Cancer       Date:  2009-06-30       Impact factor: 7.640

3.  Should relative survival be used with lung cancer data?

Authors:  S R Hinchliffe; M J Rutherford; M J Crowther; C P Nelson; P C Lambert
Journal:  Br J Cancer       Date:  2012-05-03       Impact factor: 7.640

4.  Development of visual predictive checks accounting for multimodal parameter distributions in mixture models.

Authors:  Usman Arshad; Estelle Chasseloup; Rikard Nordgren; Mats O Karlsson
Journal:  J Pharmacokinet Pharmacodyn       Date:  2019-04-09       Impact factor: 2.745

  4 in total

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