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How Do Pharmaceutical Companies Model Survival of Cancer Patients? A Review of NICE Single Technology Appraisals in 2017

Published online by Cambridge University Press:  24 April 2019

Daniel Gallacher Open the ORCID record for Daniel Gallacher [Opens in a new window] ,
Peter Auguste  and
Martin Connock
Daniel Gallacher*
Affiliation:
Warwick Evidence, Warwick Medical School, University of Warwick
Peter Auguste
Affiliation:
Warwick Evidence, Warwick Medical School, University of Warwick
Martin Connock
Affiliation:
Warwick Evidence, Warwick Medical School, University of Warwick
*
Author for correspondence: Daniel Gallacher, E-mail: [email protected]
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Abstract

Objectives

Before an intervention is publicly funded within the United Kingdom, the cost-effectiveness is assessed by the National Institute of Health and Care Excellence (NICE). The efficacy of an intervention across the patients’ lifetime is often influential of the cost-effectiveness analyses, but is associated with large uncertainties. We reviewed committee documents containing company submissions and evidence review group (ERG) reports to establish the methods used when extrapolating survival data, whether these adhered to NICE Technical Support Document (TSD) 14, and how uncertainty was addressed.

Methods

A systematic search was completed on the NHS Evidence Search webpage limited to single technology appraisals of cancer interventions published in 2017, with information obtained from the NICE Web site.

Results

Twenty-eight appraisals were identified, covering twenty-two interventions across eighteen diseases. Every economic model used parametric curves to model survival. All submissions used goodness-of-fit statistics and plausibility of extrapolations when selecting a parametric curve. Twenty-five submissions considered alternate parametric curves in scenario analyses. Six submissions reported including the parameters of the survival curves in the probabilistic sensitivity analysis. ERGs agreed with the company's choice of parametric curve in nine appraisals, and agreed with all major survival-related assumptions in two appraisals.

Conclusions

TSD 14 on survival extrapolation was followed in all appraisals. Despite this, the choice of parametric curve remains subjective. Recent developments in Bayesian approaches to extrapolation are not implemented. More precise guidance on the selection of curves and modelling of uncertainty may reduce subjectivity, accelerating the appraisal process.

Keywords

Technology assessmentBiomedicalSurvival analysisModelsEconomic
Type
Assessment
Information
International Journal of Technology Assessment in Health Care , Volume 35 , Issue 2 , 2019 , pp. 160 - 167
Copyright
Copyright © Cambridge University Press 2019 

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Footnotes

This research received no specific grant from any funding agency, commercial or not-for-profit sectors. We thank Professor James Mason for his advice and support, and we are grateful to Rachel Court for her assistance with creating and implementing the search strategy. Author Contributions: D.G. generated the initial research idea and drafted the manuscript. D.G. and P.A. reviewed the eligibility of the submissions. D.G. extracted information from eligible submissions. All authors reviewed the final draft.

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