Hostname: page-component-cd9895bd7-p9bg8 Total loading time: 0 Render date: 2024-12-23T13:34:11.043Z Has data issue: false hasContentIssue false
  • English
  • Français

A Non-inferiority Framework for Cost-Effectiveness Analysis

Part of: Special Collection - Methods

Published online by Cambridge University Press:  24 July 2019

Xuanqian Xie Open the ORCID record for Xuanqian Xie [Opens in a new window] ,
Lindsey Falk ,
James M. Brophy ,
Hong Anh Tu ,
Jennifer Guo ,
Olga Gajic-Veljanoski Open the ORCID record for Olga Gajic-Veljanoski [Opens in a new window] ,
Nancy Sikich ,
Irfan A. Dhalla  and
Vivian Ng
Xuanqian Xie*
Affiliation:
Health Quality Ontario, Toronto, Canada
Lindsey Falk
Affiliation:
Health Quality Ontario, Toronto, Canada
James M. Brophy
Affiliation:
Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada Department of Medicine, McGill University, Montreal, Canada
Hong Anh Tu
Affiliation:
Health Quality Ontario, Toronto, Canada
Jennifer Guo
Affiliation:
Health Quality Ontario, Toronto, Canada
Olga Gajic-Veljanoski
Affiliation:
Health Quality Ontario, Toronto, Canada
Nancy Sikich
Affiliation:
Health Quality Ontario, Toronto, Canada
Irfan A. Dhalla
Affiliation:
Health Quality Ontario, Toronto, Canada
Vivian Ng
Affiliation:
Health Quality Ontario, Toronto, Canada
*
Author for correspondence: Xuanqian Xie, E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Traditional decision rules have limitations when a new technology is less effective and less costly than a comparator. We propose a new probabilistic decision framework to examine non-inferiority in effectiveness and net monetary benefit (NMB) simultaneously. We illustrate this framework using the example of repetitive transcranial magnetic stimulation (rTMS) and electroconvulsive therapy (ECT) for treatment-resistant depression.

Methods

We modeled the quality-adjusted life-years (QALYs) associated with the new intervention (rTMS), an active control (ECT), and a placebo control, and we estimated the fraction of effectiveness preserved by the new intervention through probabilistic sensitivity analysis (PSA). We then assessed the probability of cost-effectiveness using a traditional cost-effectiveness acceptability curve (CEAC) and our new decision-making framework. In our new framework, we considered the new intervention cost-effective in each simulation of the PSA if it preserved at least 75 percent of the effectiveness of the active control (thus demonstrating non-inferiority) and had a positive NMB at a given willingness-to-pay threshold (WTP).

Results

rTMS was less effective (i.e., associated with fewer QALYs) and less costly than ECT. The traditional CEAC approach showed that the probabilities of rTMS being cost-effective were 100 percent, 39 percent, and 14 percent at WTPs of $0, $50,000, and $100,000 per QALY gained, respectively. In the new decision framework, the probabilities of rTMS being cost-effective were reduced to 23 percent, 21 percent, and 13 percent at WTPs of $0, $50,000, and $100,000 per QALY, respectively.

Conclusions

This new framework provides a different perspective for decision making with considerations of both non-inferiority and WTP thresholds.

Keywords

Cost-effectiveness analysisDecision-making frameworkEffectiveness-preserved thresholdNon-inferiorityProbabilistic sensitivity analysis
Type
Method
Information
International Journal of Technology Assessment in Health Care , Volume 35 , Issue 4 , 2019 , pp. 291 - 297
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.)

Footnotes

The opinions expressed in this publication do not necessarily represent the opinions of Health Quality Ontario. No endorsement is intended or should be inferred. We thank Kara Cowan from Health Quality Ontario for her help editing the manuscript and two anonymous reviewers for their valuable comments on the manuscript. Financial support: This research received no specific grant from any funding agency, commercial or not-for-profit sectors.

References

1.Thokala, P, Ochalek, J, Leech, AA, Tong, T (2018) Cost-effectiveness thresholds: The past, the present and the future. Pharmacoeconomics 36, 509–522.Google Scholar
2.Cameron, D, Ubels, J, Norstrom, F (2018) On what basis are medical cost-effectiveness thresholds set? Clashing opinions and an absence of data: A systematic review. Glob Health Action 11, 1447828.Google Scholar
3.Enns, MW, Reiss, JP, Chan, P (2009) Electroconvulsive therapy. Can J Psychiatry 55, 1–12.Google Scholar
4.Delva, NJ, Graf, P, Patry, S, et al. (2011) Access to electroconvulsive therapy services in Canada. J ECT 27, 300–309.Google Scholar
5.Knapp, M, Romeo, R, Mogg, A, et al. (2008) Cost-effectiveness of transcranial magnetic stimulation vs. electroconvulsive therapy for severe depression: A multi-centre randomised controlled trial. J Affect Disord 109, 273–285.Google Scholar
6.McLoughlin, DM, Mogg, A, Eranti, S, et al. (2007) The clinical effectiveness and cost of repetitive transcranial magnetic stimulation versus electroconvulsive therapy in severe depression: A multicentre pragmatic randomised controlled trial and economic analysis. Health Technol Assess 11, 1–54.Google Scholar
7.Slotema, CW, Blom, JD, Hoek, HW, Sommer, IE (2010) Should we expand the toolbox of psychiatric treatment methods to include repetitive transcranial magnetic stimulation (rTMS)? A meta-analysis of the efficacy of rTMS in psychiatric disorders. J Clin Psychiatry 71, 873–884.Google Scholar
8.Health Quality Ontario (2016) Repetitive transcranial magnetic stimulation for treatment-resistant depression: A systematic review and meta-analysis of randomized controlled trials. Ont Health Technol Assess Ser 16, 1–66.Google Scholar
9.Health Quality Ontario (2016) Repetitive transcranial magnetic stimulation for treatment-resistant depression: An economic analysis. Ont Health Technol Assess Ser 16, 1–51.Google Scholar
10.Walter, G, Martin, J, Kirkby, K, Pridmore, S (2001) Transcranial magnetic stimulation: Experience, knowledge and attitudes of recipients. Aust N Z J Psychiatry 35, 58–61.Google Scholar
11.El Shayeb, ME, Topfer, LA, Stafinski, T, Pawluk, L, Menon, D (2014) Diagnostic accuracy of level 3 portable sleep tests versus level 1 polysomnography for sleep-disordered breathing: A systematic review and meta-analysis. CMAJ 186, E25–E51.Google Scholar
12.Glick, HA, Briggs, AH, Polsky, D (2001) Quantifying stochastic uncertainty and presenting results of cost-effectiveness analyses. Expert Rev Pharmacoecon Outcomes Res 1, 25–36.Google Scholar
13.Kahneman, D, Tversky, A (1984) Choices, values, and frames. Am Psychol 39, 341.Google Scholar
14.Eckermann, S (2015) Kinky thresholds revisited: Opportunity costs differ in the NE and SW quadrants. Appl Health Econ Health Policy 13, 7–13.Google Scholar
15.O'Brien, BJ, Gertsen, K, Willan, AR, Faulkner, LA (2002) Is there a kink in consumers' threshold value for cost-effectiveness in health care? Health Econ 11, 175–180.Google Scholar
16.Claxton, K, Martin, S, Soares, M, et al. (2015) Methods for the estimation of the National Institute for Health and Care Excellence cost-effectiveness threshold. Health Technol Assess 19, 1–503, v–vi.Google Scholar
17.U.S. Food and Drug Administration (2016) Non-inferiority clinical trials to establish effectiveness: Guidance for industry [Internet]. Silver Spring, MD: The Administration. [cited October 7, 2017]. https://www.fda.gov/media/78504/download.Google Scholar
18.Wangge, G, Roes, KC, de Boer, A, Hoes, AW, Knol, MJ (2013) The challenges of determining noninferiority margins: A case study of noninferiority randomized controlled trials of novel oral anticoagulants. CMAJ 185, 222–227.Google Scholar
19.Xie, X, Wang, M, Ng, V, Sikich, N (2018) Some issues for the evaluation of noninferiority trials. J Comp Eff Res 7, 835–843.Google Scholar
20.Briggs, A, Sculpher, M, Claxton, K (2006) Decision modelling for health economic evaluation. Oxford: Oxford University Press.Google Scholar
21.Fenwick, E, O'Brien, BJ, Briggs, A (2004) Cost-effectiveness acceptability curves--facts, fallacies and frequently asked questions. Health Econ 13, 405–415.Google Scholar
22.Viechtbauer, W (2010) Conducting meta-analyses in R with the metafor package. J Stat Softw 36, 1–48.Google Scholar
23.Rucker, G (2012) Network meta-analysis, electrical networks and graph theory. Res Synth Methods 3, 312–324.Google Scholar
24.Zhao, YJ, Tor, PC, Khoo, AL, Teng, M, Lim, BP, Mok, YM (2018) Cost-effectiveness modeling of repetitive transcranial magnetic stimulation compared to electroconvulsive therapy for treatment-resistant depression in Singapore. Neuromodulation 21, 376–382.Google Scholar
25.Kozel, FA, George, MS, Simpson, KN (2004) Decision analysis of the cost-effectiveness of repetitive transcranial magnetic stimulation versus electroconvulsive therapy for treatment of nonpsychotic severe depression. CNS Spectr 9, 476–482.Google Scholar
26.Health Quality Ontario (2016) High-frequency repetitive transcranial magnetic stimulation for treatment-resistant depression: OHTAC recommendation [Internet]. Toronto, ON: Queen's Printer for Ontario [cited May 7, 2019]. http://www.hqontario.ca/evidence/publications-and-ohtac-recommendations/ontario-health-technology-assessment-series/ohtac-rtms.Google Scholar
27.Galletly, C, Clarke, P, Fitzgerald, P, et al. (2013) Repetitive transcranial magnetic stimulation (rTMS). MSAC application no. 1196, assessment report [Internet]. Canberra: Commonwealth of Australia. [cited cited May 7, 2019]. http://www.msac.gov.au/internet/msac/publishing.nsf/Content/5C7DBD43A4A57C73CA25801000123BAE/$File/1196_rTMS_AttA_FinalAssessmentReport-accessible.pdf.Google Scholar
28.European Network for Health Technology Assessment (2017) Repetitive transcranial magnetic stimulation for treatment-resistant major depression [Internet]. The Network. [cited May 7, 2019]. https://www.eunethta.eu/wp-content/uploads/2018/01/OTCA05_Repetitive-transcranial-magnetic-stimulation-for-TRD.pdf.Google Scholar
29.Milev, RV, Giacobbe, P, Kennedy, SH, et al. (2016) Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 clinical guidelines for the management of adults with major depressive disorder: Section 4. Neurostimulation treatments. Can J Psychiatry 61, 561–575.Google Scholar
30.Bosmans, JE, de Bruijne, MC, van Hout, HP, Hermens, ML, Ader, HJ, van Tulder, MW (2008) Practical guidelines for economic evaluations alongside equivalence trials. Value Health 11, 251–258.Google Scholar
31.Klok, RM, Postma, MJ (2004) Four quadrants of the cost-effectiveness plane: Some considerations on the south-west quadrant. Expert Rev Pharmacoecon Outcomes Res 4, 599–601.Google Scholar
32.Dowie, J (2004) Why cost-effectiveness should trump (clinical) effectiveness: The ethical economics of the south west quadrant. Health Econ 13, 453–459.Google Scholar
33.Kent, DM, Fendrick, AM, Langa, KM (2004) New and dis-improved: On the evaluation and use of less effective, less expensive medical interventions. Med Decis Making 24, 281–286.Google Scholar
Supplementary material: File

Xie et al. supplementary material

Xie et al. supplementary material 1

Download Xie et al. supplementary material(File)
File 583.1 KB