Literature DB >> 10861774

Cumulative cause-specific mortality for cancer patients in the presence of other causes: a crude analogue of relative survival.

K A Cronin1, E J Feuer.   

Abstract

A common population-based cancer progress measure for net survival (survival in the absence of other causes) of cancer patients is relative survival. Relative survival is defined as the ratio of a population of observed survivors in a cohort of cancer patients to the proportion of expected survivors in a comparable set of cancer-free individuals in the general public, thus giving a measure of excess mortality due to cancer. Relative survival was originally designed to address the question of whether or not there is evidence that patients have been cured. It has proven to be a useful survival measure in several areas, including the evaluation of cancer control efforts and the application of cure models. However, it is not representative of the actual survival patterns observed in a cohort of cancer patients. This paper suggests a measure for cumulative crude (in the presence of other causes) cause-specific probability of death for a population diagnosed with cancer. The measure does not use cause of death information which can be unreliable for population cancer registries. Point estimates and variances are derived for crude cause-specific probability of death using relative survival instead of cause of death information. Examples are given for men diagnosed with localized prostate cancer over the age of 70 and women diagnosed with regional breast cancer using Surveillance, Epidemiology and End Results (SEER) Program data. The examples emphasize the differences in crude and net mortality measures and suggest areas where a crude measure is more informative. Estimates of this type are especially important for older patients as new screening modalities detect cancers earlier and choice of treatment or even 'watchful waiting' become viable options. Published in 2000 by John Wiley & Sons, Ltd.

Entities:  

Mesh:

Year:  2000        PMID: 10861774     DOI: 10.1002/1097-0258(20000715)19:13<1729::aid-sim484>3.0.co;2-9

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


  33 in total

1.  Differences in Cancer Survival with Relative versus Cause-Specific Approaches: An Update Using More Accurate Life Tables.

Authors:  Gonçalo Forjaz de Lacerda; Nadia Howlader; Angela B Mariotto
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2019-06-20       Impact factor: 4.254

2.  Assessing non-cancer-related health status of US cancer patients: other-cause survival and comorbidity prevalence.

Authors:  Hyunsoon Cho; Angela B Mariotto; Bhupinder S Mann; Carrie N Klabunde; Eric J Feuer
Journal:  Am J Epidemiol       Date:  2013-07-03       Impact factor: 4.897

3.  Semiparametric Bayesian approaches to joinpoint regression for population-based cancer survival data.

Authors:  Pulak Ghosh; Lan Huang; Binbing Yu; Ram C Tiwari
Journal:  Comput Stat Data Anal       Date:  2009-10-01       Impact factor: 1.681

4.  Long-term projections of the harm-benefit trade-off in prostate cancer screening are more favorable than previous short-term estimates.

Authors:  Roman Gulati; Angela B Mariotto; Shu Chen; John L Gore; Ruth Etzioni
Journal:  J Clin Epidemiol       Date:  2011-12       Impact factor: 6.437

5.  Estimating the personal cure rate of cancer patients using population-based grouped cancer survival data.

Authors:  Ram C Tiwari; Eric J Feuer
Journal:  Stat Methods Med Res       Date:  2010-02-24       Impact factor: 3.021

6.  Cancer survival: an overview of measures, uses, and interpretation.

Authors:  Angela B Mariotto; Anne-Michelle Noone; Nadia Howlader; Hyunsoon Cho; Gretchen E Keel; Jessica Garshell; Steven Woloshin; Lisa M Schwartz
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

7.  Providing clinicians and patients with actual prognosis: cancer in the context of competing causes of death.

Authors:  Nadia Howlader; Angela B Mariotto; Steven Woloshin; Lisa M Schwartz
Journal:  J Natl Cancer Inst Monogr       Date:  2014-11

8.  Comparative Effectiveness of Biomarkers to Target Cancer Treatment: Modeling Implications for Survival and Costs.

Authors:  Jeanette K Birnbaum; Foluso O Ademuyiwa; Josh J Carlson; Leslie Mallinger; Mark W Mason; Ruth Etzioni
Journal:  Med Decis Making       Date:  2015-08-24       Impact factor: 2.583

9.  Annual Report to the Nation on the status of cancer, 1975-2010, featuring prevalence of comorbidity and impact on survival among persons with lung, colorectal, breast, or prostate cancer.

Authors:  Brenda K Edwards; Anne-Michelle Noone; Angela B Mariotto; Edgar P Simard; Francis P Boscoe; S Jane Henley; Ahmedin Jemal; Hyunsoon Cho; Robert N Anderson; Betsy A Kohler; Christie R Eheman; Elizabeth M Ward
Journal:  Cancer       Date:  2013-12-16       Impact factor: 6.860

10.  Life tables adjusted for comorbidity more accurately estimate noncancer survival for recently diagnosed cancer patients.

Authors:  Angela B Mariotto; Zhuoqiao Wang; Carrie N Klabunde; Hyunsoon Cho; Barnali Das; Eric J Feuer
Journal:  J Clin Epidemiol       Date:  2013-09-10       Impact factor: 6.437

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.