Literature DB >> 22675175

Cure models as a useful statistical tool for analyzing survival.

Megan Othus1, Bart Barlogie, Michael L Leblanc, John J Crowley.   

Abstract

Cure models are a popular topic within statistical literature but are not as widely known in the clinical literature. Many patients with cancer can be long-term survivors of their disease, and cure models can be a useful tool to analyze and describe cancer survival data. The goal of this article is to review what a cure model is, explain when cure models can be used, and use cure models to describe multiple myeloma survival trends. Multiple myeloma is generally considered an incurable disease, and this article shows that by using cure models, rather than the standard Cox proportional hazards model, we can evaluate whether there is evidence that therapies at the University of Arkansas for Medical Sciences induce a proportion of patients to be long-term survivors.

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Year:  2012        PMID: 22675175      PMCID: PMC3744099          DOI: 10.1158/1078-0432.CCR-11-2859

Source DB:  PubMed          Journal:  Clin Cancer Res        ISSN: 1078-0432            Impact factor:   12.531


  24 in total

1.  Estimation in a Cox proportional hazards cure model.

Authors:  J P Sy; J M Taylor
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  A semi-parametric accelerated failure time cure model.

Authors:  Chin-Shang Li; Jeremy M G Taylor
Journal:  Stat Med       Date:  2002-11-15       Impact factor: 2.373

3.  Cure fraction estimation from the mixture cure models for grouped survival data.

Authors:  Binbing Yu; Ram C Tiwari; Kathleen A Cronin; Eric J Feuer
Journal:  Stat Med       Date:  2004-06-15       Impact factor: 2.373

Review 4.  Bone marrow transplantation in the treatment of patients with lymphoma.

Authors:  J O Armitage
Journal:  Blood       Date:  1989-05-15       Impact factor: 22.113

5.  A general class of Bayesian survival models with zero and nonzero cure fractions.

Authors:  Guosheng Yin; Joseph G Ibrahim
Journal:  Biometrics       Date:  2005-06       Impact factor: 2.571

6.  Nonparametric estimation and testing in a cure model.

Authors:  E M Laska; M J Meisner
Journal:  Biometrics       Date:  1992-12       Impact factor: 2.571

7.  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

8.  Superior results of Total Therapy 3 (2003-33) in gene expression profiling-defined low-risk multiple myeloma confirmed in subsequent trial 2006-66 with VRD maintenance.

Authors:  Bijay Nair; Frits van Rhee; John D Shaughnessy; Elias Anaissie; Jackie Szymonifka; Antje Hoering; Yazan Alsayed; Sarah Waheed; John Crowley; Bart Barlogie
Journal:  Blood       Date:  2010-02-02       Impact factor: 22.113

Review 9.  Acute promyelocytic leukemia: from highly fatal to highly curable.

Authors:  Zhen-Yi Wang; Zhu Chen
Journal:  Blood       Date:  2008-03-01       Impact factor: 22.113

10.  Discrete strategies of cancer post-treatment surveillance. Estimation and optimization problems.

Authors:  A D Tsodikov; B Asselain; A Fourque; T Hoang
Journal:  Biometrics       Date:  1995-06       Impact factor: 2.571

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  48 in total

Review 1.  Do immune checkpoint inhibitors need new studies methodology?

Authors:  Roberto Ferrara; Sara Pilotto; Mario Caccese; Giulia Grizzi; Isabella Sperduti; Diana Giannarelli; Michele Milella; Benjamin Besse; Giampaolo Tortora; Emilio Bria
Journal:  J Thorac Dis       Date:  2018-05       Impact factor: 2.895

2.  Concordance measure and discriminatory accuracy in transformation cure models.

Authors:  Yilong Zhang; Yongzhao Shao
Journal:  Biostatistics       Date:  2018-01-01       Impact factor: 5.899

3.  Extension of a Cox proportional hazards cure model when cure information is partially known.

Authors:  Yu Wu; Yong Lin; Shou-En Lu; Chin-Shang Li; Weichung Joe Shih
Journal:  Biostatistics       Date:  2014-02-07       Impact factor: 5.899

Review 4.  The role of epithelial plasticity in prostate cancer dissemination and treatment resistance.

Authors:  Rhonda L Bitting; Daneen Schaeffer; Jason A Somarelli; Mariano A Garcia-Blanco; Andrew J Armstrong
Journal:  Cancer Metastasis Rev       Date:  2014-09       Impact factor: 9.264

5.  Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies.

Authors:  Aris Angelis; Huseyin Naci; Allan Hackshaw
Journal:  Pharmacoeconomics       Date:  2020-12       Impact factor: 4.981

6.  A Case Study Examining the Usefulness of Cure Modelling for the Prediction of Survival Based on Data Maturity.

Authors:  Tim S Grant; Darren Burns; Christopher Kiff; Dawn Lee
Journal:  Pharmacoeconomics       Date:  2020-04       Impact factor: 4.981

7.  [Subgroup identification based on an accelerated failure time model combined with adaptive elastic net].

Authors:  Pei Kang; Jun Xu; Fuqiang Huang; Yingxin Liu; Shengli An
Journal:  Nan Fang Yi Ke Da Xue Xue Bao       Date:  2019-10-30

8.  Using Cure Models to Estimate the Serial Interval of Tuberculosis With Limited Follow-up.

Authors:  Yicheng Ma; Helen E Jenkins; Paola Sebastiani; Jerrold J Ellner; Edward C Jones-López; Reynaldo Dietze; Charles R Horsburgh; Laura F White
Journal:  Am J Epidemiol       Date:  2020-11-02       Impact factor: 4.897

9.  Identifying Patients for Whom Lung Cancer Screening Is Preference-Sensitive: A Microsimulation Study.

Authors:  Tanner J Caverly; Pianpian Cao; Rodney A Hayward; Rafael Meza
Journal:  Ann Intern Med       Date:  2018-05-29       Impact factor: 25.391

10.  Immuno-oncology Trial Endpoints: Capturing Clinically Meaningful Activity.

Authors:  Valsamo Anagnostou; Mark Yarchoan; Aaron R Hansen; Hao Wang; Franco Verde; Elad Sharon; Deborah Collyar; Laura Q M Chow; Patrick M Forde
Journal:  Clin Cancer Res       Date:  2017-09-01       Impact factor: 12.531

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