Application of quantitative bias analysis for unmeasured confounding in cost–effectiveness modelling
Publication: Journal of Comparative Effectiveness Research
Abstract
Due to uncertainty regarding the potential impact of unmeasured confounding, health technology assessment (HTA) agencies often disregard evidence from nonrandomized studies when considering new technologies. Quantitative bias analysis (QBA) methods provide a means to quantify this uncertainty but have not been widely used in the HTA setting, particularly in the context of cost–effectiveness modelling (CEM). This study demonstrated the application of an aggregate and patient-level QBA approach to quantify and adjust for unmeasured confounding in a simulated nonrandomized comparison of survival outcomes. Application of the QBA output within a CEM through deterministic and probabilistic sensitivity analyses and under different scenarios of knowledge of an unmeasured confounder demonstrates the potential value of QBA in HTA.
Supplementary Material
References
1.
National Institiute for Health and Care Excellence. Guide to the Processes of Technology Appraisal. London, UK, (2018).
2.
Leahy TP, Sammon C, Kent S, Ramagopalan S, Groenwold RH. Sensitivity analyses for unmeasured confounding in non-randomised studies: considerations for application in health technology assessment. J. Comp. Eff. Res. 11(12), (2022).
3.
Sammon CJ, Leahy TP, Gsteiger S, Ramagopalan S. Real-world evidence and nonrandomized data in health technology assessment: using existing methods to address unmeasured confounding? J. Comp. Eff. Res. 9(14), 969–972 (2020).
4.
Kent S, Salcher-Konrad M, Boccia S et al. The use of nonrandomized evidence to estimate treatment effects in health technology assessment. J. Comp. Eff. Res. 10(14), 1035–1043 (2021).
5.
Thorlund K, Dron L, Park JJ, Mills EJ. Synthetic and external controls in clinical trials – a primer for researchers. Clin. Epidemiol. 12, 457 (2020).
6.
Alexander M, Wolfe R, Ball D et al. Lung cancer prognostic index: a risk score to predict overall survival after the diagnosis of non-small-cell lung cancer. Br. J. Cancer 117(5), 744–751 (2017).
7.
Sehgal K, Gill RR, Widick P et al. Association of performance status with survival in patients with advanced non-small cell lung cancer treated with pembrolizumab monotherapy. JAMA Netw. Open 4(2), e2037120–e2037120 (2021).
8.
Cox DR. Regression models and life-tables. J. R. Stat. Soc. Series B Stat. Methodol. 34(2), 187–202 (1972).
9.
Huang R, Xu R, Dulai PS. Sensitivity analysis of treatment effect to unmeasured confounding in observational studies with survival and competing risks outcomes. Stat. Med. 39(24), 3397–3411 (2020).
10.
Ding P, VanderWeele TJ. Sensitivity analysis without assumptions. Epidemiology 27(3), 368–377 (2016).
11.
Carnegie NB, Harada M, Hill JL. Assessing sensitivity to unmeasured confounding using a simulated potential confounder. J. Res. Educ. Eff. 9(3), 395–420 (2016).
12.
Vanderweele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann. Intern. Med. 167(4), 268–274 (2017).
13.
Vanderweele T. On a square-root transformation of the odds ratio for a common outcome. Epidemiology (Cambridge, Mass) 28(6), e58 (2017).
14.
Sjölander A. A note on a sensitivity analysis for unmeasured confounding, and the related E-value. J. Causal Inference 8(1), 229–248 (2020).
15.
Greenland S. Basic methods for sensitivity analysis of biases. Int. J. Epidemiol. 25(6), 1107–1116 (1996).
16.
Mok T, Camidge D, Gadgeel S et al. Updated overall survival and final progression-free survival data for patients with treatment-naive advanced ALK-positive non-small-cell lung cancer in the ALEX study. Ann. Oncol. 31(8), 1056–1064 (2020).
17.
Ramalingam SS, Vansteenkiste J, Planchard D et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N. Engl. J. Med. 382(1), 41–50 (2020).
18.
Briggs AH, Weinstein MC, Fenwick EA et al. Model parameter estimation and uncertainty: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-6. Value Health 15(6), 835–842 (2012).
19.
Lash TL, Fox MP, Maclehose RF, Maldonado G, McCandless LC, Greenland S. Good practices for quantitative bias analysis. Int. J. Epidemiol. 43(6), 1969–1985 (2014).
20.
Oster E. Unobservable selection and coefficient stability: theory and evidence. J. Bus. Econ. Stat. 37(2), 187–204 (2019).
21.
Cinelli C, Hazlett C. Making sense of sensitivity: extending omitted variable bias. J. R. Stat. Soc. Series B Stat. Methodol. 82(1), 39–67 (2020).
22.
Faria R, Alava MH, Manca A, Wailoo AJ. National Institute for Health and Care Excellence. The Use of Observational Data to Inform Estimates of Treatment Effectiveness in Technology Appraisal: Methods for Comparative Individual Patient Data;NICE DSU Technical Support Document. London, UK (2015).
Information & Authors
Information
Published In
Pages: 861 - 870
PubMed: 35678168
Copyright
© 2022 The Authors. This work is licensed under the Creative Commons Attribution 4.0 License
History
Received: 14 February 2022
Accepted: 24 May 2022
Published online: 9 June 2022
Keywords:
Topics
Authors
Funding Information
Metrics & Citations
Metrics
Article Usage
Article usage data only available from February 2023. Historical article usage data, showing the number of article downloads, is available upon request.
Citations
How to Cite
Application of quantitative bias analysis for unmeasured confounding in cost–effectiveness modelling. (2022) Journal of Comparative Effectiveness Research. DOI: 10.2217/cer-2022-0030
Export citation
Select the citation format you wish to export for this article or chapter.
Citing Literature
- Eva Kagenaar, David Gibran Lugo-Palacios, Hannah Bromley, Ajay Aggarwal, Andrew Hutchings, Stephen O’Neill, Bernard Rachet, Jo Cresswell, Ananya Choudhury, Richard Grieve, Surgery or radiotherapy for early-stage cancer study protocol for an emulated target trial of radical radiotherapy versus radical cystectomy, with either following neoadjuvant chemotherapy, for organ-confined muscle-invasive bladder cancer, BMJ Open, 10.1136/bmjopen-2026-119596, 16, 6, (e119596), (2026).
- Sylwia Bujkiewicz, Oriana Ciani, Bart Heeg, Dawn Lee, Jeanette M. Kusel, Kristian Thorlund, Petros Pechlivanoglou, Stephen Stefani, Wanrudee Isaranuwatchai, Marc Buyse, Mario Ouwens, Methods for Evaluation of Surrogate Endpoints for Health Technology Assessment Decision Making: A Good Practices Report of an ISPOR Task Force, Value in Health, 10.1016/j.jval.2026.01.020, (2026).
- Manuel Gomes, Alex J. Turner, Cormac Sammon, Dalia Dawoud, Sreeram Ramagopalan, Alex Simpson, Uwe Siebert, Acceptability of Using Real-World Data to Estimate Relative Treatment Effects in Health Technology Assessments: Barriers and Future Steps, Value in Health, 10.1016/j.jval.2024.01.020, 27, 5, (623-632), (2024).
- Thomas P. Leahy, Isabelle Durand-Zaleski, Laura Sampietro-Colom, Seamus Kent, York Zöllner, Doug Coyle, Gianluigi Casadei, The role of quantitative bias analysis for nonrandomized comparisons in health technology assessment: recommendations from an expert workshop, International Journal of Technology Assessment in Health Care, 10.1017/S0266462323002702, 39, 1, (2023).
- Alex J. Turner, Cormac Sammon, Nick Latimer, Blythe Adamson, Brennan Beal, Vivek Subbiah, Keith R. Abrams, Joshua Ray, Transporting Comparative Effectiveness Evidence Between Countries: Considerations for Health Technology Assessments, PharmacoEconomics, 10.1007/s40273-023-01323-1, 42, 2, (165-176), (2023).
- Alex Simpson, Sreeram V Ramagopalan, R WE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 9, Journal of Comparative Effectiveness Research, 10.2217/cer-2022-0145, 11, 16, (1147-1149), (2022).