Hostname: page-component-cb9f654ff-rkzlw Total loading time: 0 Render date: 2025-09-07T18:24:18.492Z Has data issue: false hasContentIssue false
  • English
  • Français

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: d.gallacher@warwick.ac.uk
Get access Rights & Permissions [Opens in a new window]

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

Information

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 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

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.

References

1.Latimer, N (2011) NICE DSU Technical Support Document 14: Undertaking survival analysis for economic evaluations alongside clinical trials - Extrapolation with patient-level data.Google Scholar
2.NICE (2018) Technology appraisal processes. [cited October 17, 2018]. https://www.nice.org.uk/about/what-we-do/our-programmes/nice-guidance/nice-technology-appraisal-guidance/process.Google Scholar
3.Gallacher, D, Armoiry, X, Auguste, P, et al. (2019) Pembrolizumab for previously treated advanced or metastatic urothelial cancer: An evidence review group perspective of a NICE single technology appraisal. PharmacoEconomics 37: 19–27.Google Scholar
4.Liberati, A, Altman, DG, Tetzlaff, J, et al. (2009) The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: Explanation and elaboration. BMJ 339: b2700.Google Scholar
5.National Institute for Health and Care Excellence (2017) Cetuximab for treating recurrent or metastatic squamous cell cancer of the head and neck. Technology appraisal guidance (TA473). August 31.Google Scholar
6.National Institute for Health and Care Excellence (2017) Paclitaxel as albumin-bound nanoparticles with gemcitabine for untreated metastatic pancreatic cancer. Technology appraisal guidance (TA476). September 6.Google Scholar
7.National Institute for Health and Care Excellence(2017) Regorafenib for previously treated unresectable or metastatic gastrointestinal stromal tumours. Technology appraisal guidance (TA488). November 15.Google Scholar
8.National Institute for Health and Care Excellence(2017) Trastuzumab emtansine for treating HER2-positive advanced breast cancer after trastuzumab and a taxane. Technology appraisal guidance (TA458). July 19.Google Scholar
9.National Institute for Health and Care Excellence (2017) Everolimus for advanced renal cell carcinoma after previous treatment. Technology appraisal guidance (TA432). February 22.Google Scholar
10.National Institute for Health and Care Excellence (2017) Pembrolizumab for treating PD-L1-positive non-small-cell lung cancer after chemotherapy. Technology appraisal guidance (TA428). January 11.Google Scholar
11.National Institute for Health and Care Excellence (2017) Cabozantinib for previously treated advanced renal cell carcinoma. Technology appraisal guidance (TA463). August 9.Google Scholar
12.National Institute for Health and Care Excellence (2017) Palbociclib with an aromatase inhibitor for previously untreated, hormone receptor-positive, HER2-negative, locally advanced or metastatic breast cancer. Technology appraisal guidance (TA495). December 20.Google Scholar
13.National Institute for Health and Care Excellence (2017) Ribociclib with an aromatase inhibitor for previously untreated, hormone receptor-positive, HER2-negative, locally advanced or metastatic breast cancer. Technology appraisal guidance (TA496). December 20.Google Scholar
14.National Institute for Health and Care Excellence (2017) Pembrolizumab for untreated PD-L1-positive metastatic non-small-cell lung cancer. Technology appraisal guidance (TA447). June 28.Google Scholar
15.National Institute for Health and Care Excellence (2017) Nivolumab for treating relapsed or refractory classical Hodgkin lymphoma. Technology appraisal guidance (TA462). July 26.Google Scholar
16.National Institute for Health and Care Excellence (2017) Brentuximab vedotin for treating relapsed or refractory systemic anaplastic large cell lymphoma. Technology appraisal guidance (TA478). October 4.Google Scholar
17.National Institute for Health and Care Excellence (2017) Carfilzomib for previously treated multiple myeloma. Technology appraisal guidance (TA457). July 19.Google Scholar
18.National Institute for Health and Care Excellence (2017) Blinatumomab for previously treated Philadelphia-chromosome-negative acute lymphoblastic leukaemia. Technology appraisal guidance (TA450). June 28.Google Scholar
19.National Institute for Health and Care Excellence (2017) Ibrutinib for previously treated chronic lymphocytic leukaemia and untreated chronic lymphocytic leukaemia with 17p deletion or TP53 mutation. Technology appraisal guidance (TA429). January 25.Google Scholar
20.National Institute for Health and Care Excellence (2017) Ponatinib for treating chronic myeloid leukaemia and acute lymphoblastic leukaemia. Technology appraisal guidance (TA451). June 28.Google Scholar
21.National Institute for Health and Care Excellence (2017) Pomalidomide for multiple myeloma previously treated with lenalidomide and bortezomib. Technology appraisal guidance (TA427). January 11.Google Scholar
22.National Institute for Health and Care Excellence (2017) Brentuximab vedotin for treating CD30-positive Hodgkin lymphoma. Technology appraisal guidance (TA446). June 28.Google Scholar
23.National Institute for Health and Care Excellence (2017) Atezolizumab for untreated PD-L1-positive locally advanced or metastatic urothelial cancer when cisplatin is unsuitable. Technology appraisal guidance (TA492). December 6.Google Scholar
24.National Institute for Health and Care Excellence (2017) Nivolumab for previously treated non-squamous non-small-cell lung cancer. Technology appraisal guidance (TA484). November 1.Google Scholar
25.National Institute for Health and Care Excellence (2017) Nivolumab for previously treated squamous non-small-cell lung cancer. Technology appraisal guidance (TA483). November 1.Google Scholar
26.National Institute for Health and Care Excellence (2017) Nivolumab for treating squamous cell carcinoma of the head and neck after platinum-based chemotherapy. Technology appraisal guidance (TA490). November 22.Google Scholar
27.National Institute for Health and Care Excellence (2017) Venetoclax for treating chronic lymphocytic leukaemia. Technology appraisal guidance (TA487). November 8.Google Scholar
28.National Institute for Health and Care Excellence (2017) Olaratumab in combination with doxorubicin for treating advanced soft tissue sarcoma. Technology appraisal guidance (TA465). August 9.Google Scholar
29.National Institute for Health and Care Excellence (2017) Obinutuzumab with bendamustine for treating follicular lymphoma refractory to rituximab. Technology appraisal guidance (TA472). August 30.Google Scholar
30.National Institute for Health and Care Excellence (2017) Ibrutinib for treating Waldenstrom's macroglobulinaemia. Technology appraisal guidance (TA491). November 22.Google Scholar
31.National Institute for Health and Care Excellence (2017) Vismodegib for treating basal cell carcinoma. Technology appraisal guidance (TA489). November 22.Google Scholar
32.National Institute for Health and Care Excellence (2017) Pegylated liposomal irinotecan for treating pancreatic cancer after gemcitabine. Technology appraisal guidance (TA440). April 26.Google Scholar
33.Betensky, RA (2015) Measures of follow-up in time-to-event studies: Why provide them and what should they be? Clinical trials (London, England). 12, 403–408.Google Scholar
34.Latimer, NR (2013) Survival analysis for economic evaluations alongside clinical trials—Extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making 33: 743–754.Google Scholar
35.Bagust, A, Beale, S (2014) Survival analysis and extrapolation modeling of time-to-event clinical trial data for economic evaluation: An alternative approach. Med Decis Making 34: 343–351.Google Scholar
36.Latimer, NR (2014) Response to “Survival analysis and extrapolation modeling of time-to-event clinical trial data for economic evaluation: An alternative approach” by Bagust and Beale. Med Decis Making 34: 279–282.Google Scholar
37.Tremblay, G, Haines, P, Briggs, A (2015) A criterion-based approach for the systematic and transparent extrapolation of clinical trial survival data. J Health Econ Outcomes Res 2: 147–160.Google Scholar
38.Demiris, N, Lunn, D, Sharples, LD (2015) Survival extrapolation using the poly-Weibull model. 24: 287–301.Google Scholar
39.Negrín, MA, Nam, J, Briggs, AH (2017) Bayesian solutions for handling uncertainty in survival extrapolation. Med Decis Making 37: 367–376.Google Scholar
40.Jackson, C, Stevens, J, Ren, S, et al. (2017) Extrapolating survival from randomized trials using external data: A review of methods. Med Decis Making 37: 377–390.Google Scholar