Literature DB >> 22544992

Change point-cure models with application to estimating the change-point effect of age of diagnosis among prostate cancer patients.

Megan Othus1, Yi Li, Ram Tiwari.   

Abstract

Previous research on prostate cancer survival trends in the United States National Cancer Institute's Surveillance Epidemiology and End Results database has indicated a potential change-point in the age of diagnosis of prostate cancer around age 50. Identifying a change-point value in prostate cancer survival and cure could have important policy and health care management implications. Statistical analysis of this data has to address two complicating features: (1) change-point models are not smooth functions and so present computational and theoretical difficulties; and (2) models for prostate cancer survival need to account for the fact that many men diagnosed with prostate cancer can be effectively cured of their disease with early treatment. We develop a cure survival model that allows for change-point effects in covariates to investigate a potential change-point in the age of diagnosis of prostate cancer. Our results do not indicate that age under 50 is associated with increased hazard of death from prostate cancer.

Entities:  

Year:  2012        PMID: 22544992      PMCID: PMC3337711          DOI: 10.1080/02664763.2011.626849

Source DB:  PubMed          Journal:  J Appl Stat        ISSN: 0266-4763            Impact factor:   1.404


  18 in total

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4.  The Cox proportional hazards model with change point: an epidemiologic application.

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5.  A Cox-type regression model with change-points in the covariates.

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Review 6.  Racial differences in prostate cancer treatment outcomes: a systematic review.

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Journal:  Cancer Nurs       Date:  2005 Mar-Apr       Impact factor: 2.592

7.  Exact percentage points of the likelihood-ratio test for a change-point hazard-rate model.

Authors:  K J Worsley
Journal:  Biometrics       Date:  1988-03       Impact factor: 2.571

8.  The use of mixture models for the analysis of survival data with long-term survivors.

Authors:  V T Farewell
Journal:  Biometrics       Date:  1982-12       Impact factor: 2.571

Review 9.  Early prostate cancer: clinical decision-making.

Authors:  Ashesh B Jani; Samuel Hellman
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Review 10.  Cancer disparities by race/ethnicity and socioeconomic status.

Authors:  Elizabeth Ward; Ahmedin Jemal; Vilma Cokkinides; Gopal K Singh; Cheryll Cardinez; Asma Ghafoor; Michael Thun
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  2 in total

1.  Bayesian random threshold estimation in a Cox proportional hazards cure model.

Authors:  Lili Zhao; Dai Feng; Emily L Bellile; Jeremy M G Taylor
Journal:  Stat Med       Date:  2013-09-06       Impact factor: 2.373

2.  Determination of Cut Point in the Age of Colorectal Cancer Diagnosis Using a Survival Cure Model.

Authors:  Mahbobe Abdollahi; Nayereh Kasiri; Mohamad Amin Pourhoseingholi; Ahmad Reza Baghestani; Habibollah Esmaily
Journal:  Asian Pac J Cancer Prev       Date:  2019-09-01
  2 in total

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