Cure-Rate Survival Models and Their Application to Cancer Clinical Trials

Megan Othus (), Alan Mitchell (), Bart Barlogie (), Gareth Morgan () and John Crowley ()
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Megan Othus: Fred Hutchinson Cancer Research Center
Alan Mitchell: Allergan, USA Inc
Bart Barlogie: Mt Sinai School of Medicine
Gareth Morgan: Myeloma Institute at University of Arkansas for Medical Sciences
John Crowley: Cancer Research and Biostatistics

A chapter in Frontiers of Biostatistical Methods and Applications in Clinical Oncology, 2017, pp 165-178 from Springer

Abstract: Abstract Many patients with cancer can be long-term survivorsSurvivor curve/function of their disease and cure models can be a useful tool to analyze and describe cancer clinical trial survival data. This goal of this chapter is to: (i) review what a cure model is, (ii) explain when it can be appropriate to use cure models, and (iii) use cure models to describe multiple myeloma survival trends, including analyses that account for competing risks. This chapter will show that by using cure models, in addition to the standard Cox proportional hazardsProportional hazards model, we can evaluate whether there is evidence that some myeloma therapies induce a proportion of patients to be long-term survivors.

Keywords: Cure model; Survival analysis; Regression model; Clinical trials (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-10-0126-0_11

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DOI: 10.1007/978-981-10-0126-0_11

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