Literature DB >> 10223574

The shape of the hazard function in breast carcinoma: curability of the disease revisited.

A Y Yakovlev1, A D Tsodikov, K Boucher, R Kerber.   

Abstract

BACKGROUND: The question of curability of breast carcinoma remains controversial. Because the probability of cure essentially is an asymptotic notion, the corresponding estimation problems call for special statistical methods. Such methods should account for an intimate connection between the probability of cure and the shape of the hazard function.
METHODS: The study was performed on survival data for 13,166 women with breast carcinoma identified through the Utah Cancer Registry and stratified by clinical stage and age at diagnosis. For these patients, the follow-up period was 30 years. Three estimation procedures were used for estimating the hazard function from the data: the life table estimator, a kernel counterpart of the Nelson-Aalen estimator, and a parametric estimator specifically designed for two-component hazards. The parametric estimate of the hazard function was used to provide estimates of cure rates for each category of patients.
RESULTS: For all categories of patients under study, the estimated hazard functions passed through a clear-cut maximum, showing a tendency to decrease as time approached the end of a follow-up period. The hazards appeared to be nonproportional across the strata. The estimated values of the cure rate and the corresponding confidence intervals were determined for each stratum of patients with breast carcinoma.
CONCLUSIONS: The results of the current study strongly suggest that cure is a possible outcome of breast carcinoma treatment. The condition of proportionality of risks is not met in breast carcinoma survival data.

Entities:  

Mesh:

Year:  1999        PMID: 10223574

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  8 in total

1.  Estimating Cure Rates From Survival Data: An Alternative to Two-Component Mixture Models.

Authors:  A D Tsodikov; J G Ibrahim; A Y Yakovlev
Journal:  J Am Stat Assoc       Date:  2003-12-01       Impact factor: 5.033

Review 2.  Survivorship in untreated breast cancer patients.

Authors:  Carlos M Galmarini; Olivier Tredan; Felipe C Galmarini
Journal:  Med Oncol       Date:  2015-01-15       Impact factor: 3.064

3.  Multimodal hazard rate for relapse in breast cancer: quality of data and calibration of computer simulation.

Authors:  Michael Retsky; Romano Demicheli
Journal:  Cancers (Basel)       Date:  2014-11-27       Impact factor: 6.639

4.  Two-stage estimation to adjust for treatment switching in randomised trials: a simulation study investigating the use of inverse probability weighting instead of re-censoring.

Authors:  N R Latimer; K R Abrams; U Siebert
Journal:  BMC Med Res Methodol       Date:  2019-03-29       Impact factor: 4.615

5.  Causal inference for long-term survival in randomised trials with treatment switching: Should re-censoring be applied when estimating counterfactual survival times?

Authors:  N R Latimer; I R White; K R Abrams; U Siebert
Journal:  Stat Methods Med Res       Date:  2018-06-25       Impact factor: 3.021

6.  New concepts in breast cancer emerge from analyzing clinical data using numerical algorithms.

Authors:  Michael Retsky
Journal:  Int J Environ Res Public Health       Date:  2009-01-20       Impact factor: 3.390

7.  Mixtures of Polya trees for flexible spatial frailty survival modelling.

Authors:  Luping Zhao; Timothy E Hanson; Bradley P Carlin
Journal:  Biometrika       Date:  2009-06-01       Impact factor: 2.445

8.  Recurrence dynamics does not depend on the recurrence site.

Authors:  Romano Demicheli; Elia Biganzoli; Patrizia Boracchi; Marco Greco; Michael W Retsky
Journal:  Breast Cancer Res       Date:  2008-10-09       Impact factor: 6.466

  8 in total

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