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Modeling Covariate-Adjusted Survival for Economic Evaluations in Oncology

  • Istvan M. MajerEmail author
  • Jean-Gabriel Castaigne
  • Stephen Palmer
  • Lucy DeCosta
  • Marco Campioni
Original Research Article

Abstract

Background and Objectives

In economic evaluations in oncology, adjusted survival should be generated if imbalances in prognostic/predictive factors across treatment arms are present. To date, no formal guidance has been developed regarding how such adjustments should be made. We compared various covariate-adjusted survival modeling approaches, as applied to the ENDEAVOR trial in multiple myeloma that assessed carfilzomib plus dexamethasone (Cd) versus bortezomib plus dexamethasone (Vd).

Methods

Overall survival (OS) data and baseline characteristics were used for a subgroup (bortezomib-naïve/one prior therapy). Four adjusted survival modeling approaches were compared: propensity score weighting followed by fitting a Weibull model to the two arms of the balanced data (weighted data approach); fitting a multiple Weibull regression model including prognostic/predictive covariates to the two arms to predict survival using the mean value of each covariate and using the average of patient-specific survival predictions; and applying an adjusted hazard ratio (HR) derived from a Cox proportional hazard model to the baseline risk estimated for Vd.

Results

The mean OS estimated by the weighted data approach was 6.85 years (95% confidence interval [CI] 4.62–10.70) for Cd, 4.68 years (95% CI 3.46–6.74) for Vd, and 2.17 years (95% CI 0.18–5.06) for the difference. Although other approaches estimated similar differences, using the mean value of covariates appeared to yield skewed survival estimates (mean OS was 7.65 years for Cd and 5.40 years for Vd), using the average of individual predictions had limited external validity (implausible long-term OS predictions with > 10% of the Vd population alive after 30 years), and using the adjusted HR approach overestimated uncertainty (difference in mean OS was 2.03, 95% CI − 0.17 to 6.19).

Conclusions

Adjusted survival modeling based on weighted or matched data approaches provides a flexible and robust method to correct for covariate imbalances in economic evaluations. The conclusions of our study may be generalizable to other settings.

Trial Registration

ClinicalTrials.gov identifier NCT01568866 (ENDEAVOR trial).

Notes

Acknowledgements

We would like to thank the two reviewers and the journal editor for the insightful comments that helped improve the manuscript.

Author Contributions

IM and MC designed the study. IM performed the analyses. All authors analyzed the data. All authors contributed to writing the paper by providing guidance and comments on its content.

Compliance with Ethical Standards

Funding

This study was supported by Amgen.

Conflict of interest

I Majer, J.G. Castaigne, L. DeCosta, and M. Campioni are employees of Amgen and hold Amgen stock. S. Palmer was a paid consultant to Amgen with regard to advising on this research. S. Palmer has no conflict of interest to report. We, the authors, attest that we have herein disclosed any and all financial or other relationships that could be construed as a conflict of interest and that all sources of financial support for this study have been disclosed and are indicated in the Funding section.

Supplementary material

40273_2018_759_MOESM1_ESM.docx (142 kb)
Supplementary material 1 (DOCX 137 kb)

References

  1. 1.
    Latimer N. NICE DSU Technical Support Document 14: Undertaking survival analysis for economic evaluations alongside clinical trials–extrapolation with patient-level data. 2011. http://www.nicedsu.org.uk
  2. 2.
    Committee for Medicinal Products for Human Use (CHMP). Guideline on adjustment for baseline covariates in clinical trials. London: European Medicines Agency; 2015.Google Scholar
  3. 3.
    Guyot P, Welton NJ, Ouwens MJ, Ades AE. Survival time outcomes in randomized, controlled trials and meta-analyses: the parallel universes of efficacy and cost-effectiveness. Value Health. 2011;14(5):640–6.CrossRefGoogle Scholar
  4. 4.
    Signorovitch JE, Sikirica V, Erder MH, Xie J, Lu M, Hodgkins PS, et al. Matching-adjusted indirect comparisons: a new tool for timely comparative effectiveness research. Value Health. 2012;15(6):940–7.CrossRefGoogle Scholar
  5. 5.
    Ghali WA, Quan H, Brant R, van Melle G, Norris CM, Faris PD, et al. APPROACH (Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease) Investigators. Comparison of 2 methods for calculating adjusted survival curves from proportional hazards models. JAMA. 2001;286(12):1494–7.CrossRefGoogle Scholar
  6. 6.
    Austin PC. An introduction to propensity score methods for reducing the effects of confounding in observational studies. Multivar Behav Res. 2011;46(3):399–424.CrossRefGoogle Scholar
  7. 7.
    Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11(5):550–60.CrossRefGoogle Scholar
  8. 8.
    Cole SR, Hernan MA. Adjusted survival curves with inverse probability weights. Comput Methods Programs Biomed. 2004;75(1):45–9.CrossRefGoogle Scholar
  9. 9.
    Faria R, Hernandez Alava M, Manca A, Wailoo AJ. 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 17. London: NICE; 2015.Google Scholar
  10. 10.
    Dimopoulos MA, Moreau P, Palumbo A, Joshua D, Pour L, Hájek R, et al. Carfilzomib and dexamethasone versus bortezomib and dexamethasone for patients with relapsed or refractory multiple myeloma (ENDEAVOR): a randomised, phase 3, open-label, multicentre study. Lancet Oncol. 2016;17(1):27–38.CrossRefGoogle Scholar
  11. 11.
    Dimopoulos M, Goldschmidt H, Niesvizky R, Joshua D, Chng W-J, Oriol A, et al. Overall survival of patients with relapsed or refractory multiple myeloma treated with carfilzomib and dexamethasone versus bortezomib and dexamethasone: interim analysis from the randomized phase 3 ENDEAVOR trial [abstract]. In: 16th International Myeloma Workshop; 1–4 Mar 2017; New Delhi.Google Scholar
  12. 12.
    National Institute for Health and Care Excellence (NICE). Bortezomib monotherapy for relapsed multiple myeloma (TA129). 2007. http://www.nice.org.uk/TA129. Accessed May 2016.
  13. 13.
    Data on file. Clinical study report: ENDEAVOR. Amgen: 9 May 2017.Google Scholar
  14. 14.
    Pocock SJ, Assmann SE, Enos LE, Kasten LE. Subgroup analysis, covariate adjustment and baseline comparisons in clinical trial reporting: current practice and problems. Stat Med. 2002;21(19):2917–30.CrossRefGoogle Scholar
  15. 15.
    Dimopoulos MA, Orlowski RZ, Facon T, Sonneveld P, Anderson KC, Beksac M, et al. Retrospective matched-pairs analysis of bortezomib plus dexamethasone versus bortezomib monotherapy in relapsed multiple myeloma. Haematologica. 2015;100(1):100–6.CrossRefGoogle Scholar
  16. 16.
    Bringhen S, Mateos MV, Zweegman S, Larocca A, Falcone AP, Oriol A, et al. Age and organ damage correlate with poor survival in myeloma patients: meta-analysis of 1435 individual patient data from 4 randomized trials. Haematologica. 2013;98(6):980–7.CrossRefGoogle Scholar
  17. 17.
    Chen HF, Wu TQ, Li ZY, Shen HS, Tang JQ, Fu WJ, et al. Extramedullary plasmacytoma in the presence of multiple myeloma: clinical correlates and prognostic relevance. Onco Targets Ther. 2012;5:329–34.PubMedPubMedCentralGoogle Scholar
  18. 18.
    Barlogie B, Bolejack V, Schell M, Crowley J. Prognostic factor analyses of myeloma survival with intergroup trial S9321 (INT 0141): examining whether different variables govern different time segments of survival. Ann Hematol. 2011;90(4):423–8.CrossRefGoogle Scholar
  19. 19.
    Greipp PR, San Miguel J, Durie BG, Crowley JJ, Barlogie B, Bladé J, et al. International staging system for multiple myeloma. J Clin Oncol. 2005;23(15):3412–20.CrossRefGoogle Scholar
  20. 20.
    Rajkumar SV. Multiple myeloma: 2016 update on diagnosis, risk-stratification, and management. Am J Hematol. 2016;91(7):719–34.CrossRefGoogle Scholar
  21. 21.
    Chng WJ, Dispenzieri A, Chim CS, Fonseca R, Goldschmidt H, Lentzsch S, International Myeloma Working Group, et al. IMWG consensus on risk stratification in multiple myeloma. Leukemia. 2014;28(2):269–77.CrossRefGoogle Scholar
  22. 22.
    Clark TG, Bradburn MJ, Love SB, Altman DG. Survival analysis part IV: further concepts and methods in survival analysis. Br J Cancer. 2003;89(5):781–6.CrossRefGoogle Scholar
  23. 23.
    Judd CM, McClelland GH, Ryan CS. Data analysis: a model comparison approach. 2nd ed. New York: Taylor & Francis; 2011.CrossRefGoogle Scholar
  24. 24.
    National Institute for Health and Care Excellence (NICE). Carfilzomib for treated multiple myeloma in people who have received at least one prior therapy (ID 934). Evidence Review Group report. https://www.nice.org.uk/guidance/GID-TA10005/documents/committee-papers. Accessed Nov 2016.
  25. 25.
    Venables WN, Ripley BD. Modern applied statistics with S. New York: Springer; 2002.CrossRefGoogle Scholar
  26. 26.
    Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.CrossRefGoogle Scholar
  27. 27.
    Tanner-Smith EE, Lipsey MW. Identifying baseline covariates for use in propensity scores: a novel approach illustrated for a non-randomized study of recovery high schools. Peabody J Educ. 2014;89(2):183–96.CrossRefGoogle Scholar
  28. 28.
    Brookhart MA, Schneeweiss S, Rothman KJ, Glynn RJ, Avorn J, Stürmer T. Variable selection for propensity score models. Am J Epidemiol. 2006;163(12):1149–56.CrossRefGoogle Scholar
  29. 29.
    Latimer NR. Survival analysis for economic evaluations alongside clinical trials—extrapolation with patient-level data: inconsistencies, limitations, and a practical guide. Med Decis Making. 2013;33(6):743–54.CrossRefGoogle Scholar
  30. 30.
    Orlowski RZ, Nagler A, Sonneveld P, Bladé J, Hajek R, Spencer A, et al. Final overall survival results of a randomized trial comparing bortezomib plus pegylated liposomal doxorubicin with bortezomib alone in patients with relapsed or refractory multiple myeloma. Cancer. 2016;122(13):2050–6.CrossRefGoogle Scholar
  31. 31.
    National Institute for Health and Care Excellence (NICE). Single Technology Appraisal. Carfilzomib for previously treated multiple myeloma [ID934] Committee Papers. 2017. https://www.nice.org.uk/guidance/ta457/documents/committee-papers. Accessed Jul 2017.
  32. 32.
    Jakubowiak A, Majer IM, Houisse I, Benedict A, Campioni M, Panjabi S, et al. Economic evaluation of carfilzomib + dexamethasone (Kd) vs bortezomib + dexamethasone (Vd) in relapsed or refractory multiple myeloma (R/RMM) [abstract]. Blood. 2016;128(22):3582.Google Scholar
  33. 33.
    Rizzo ML. Statistical computing with R. New York: Taylor & Francis; 2007.CrossRefGoogle Scholar
  34. 34.
    R Development Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2010.Google Scholar
  35. 35.
    McCaffrey DF, Griffin BA, Almirall D, Slaughter ME, Ramchand R, Burgette LF. A tutorial on propensity score estimation for multiple treatments using generalized boosted models. Stat Med. 2013;32(19):3388–414.CrossRefGoogle Scholar
  36. 36.
    National Institute for Health and Care Excellence (NICE), Multiple myeloma—lenalidomide (post bortezomib) (part rev TA171) [ID667]. Evidence review group report. https://www.nice.org.uk/guidance/GID-TAG452/documents/multiple-myeloma-lenalidomide-post-bortezomib-part-rev-ta171-evaluation-report2. Accessed Nov 2016.
  37. 37.
    Austin PC. Balance diagnostics for comparing the distribution of baseline covariates between treatment groups in propensity-score matched samples. Stat Med. 2009;28(25):3083–107.CrossRefGoogle Scholar
  38. 38.
    Sekhon JS, Grieve RD. A matching method for improving covariate balance in cost-effectiveness analyses. Health Econ. 2012;21(6):695–714.CrossRefGoogle Scholar
  39. 39.
    Diamond A, Sekhon JS. Genetic matching for estimating causal effects: a general multivariate matching method for achieving balance in observational studies. Rev Econ Stat. 2013;95(3):932–45.CrossRefGoogle Scholar
  40. 40.
    National Institute for Health and Care Excellence (NICE). Single Technology Appraisal. Blinatumomab for treating Philadelphiachromosome-negative relapsed or refractory acute lymphoblastic leukaemia [ID804] Committee Papers. 2017. https://www.nice.org.uk/guidance/ta450/documents/committee-papers. Accessed Jul 2017.
  41. 41.
    National Institute for Health and Care Excellence (NICE). Single Technology Appraisal. Osimertinib for treating metastatic EGFR and T790M mutation-positive non-small-cell lung cancer [ID874]. Committee Papers. 2016. https://www.nice.org.uk/guidance/ta416/documents/committee-papers-2. Accessed Jul 2017.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Istvan M. Majer
    • 1
    Email author
  • Jean-Gabriel Castaigne
    • 2
  • Stephen Palmer
    • 3
  • Lucy DeCosta
    • 4
  • Marco Campioni
    • 1
  1. 1.Global Health Economics, Amgen (Europe) GmbHRotkreuzSwitzerland
  2. 2.Amgen, Amgen OncologyCambridgeUK
  3. 3.University of York, Centre for Health EconomicsYorkUK
  4. 4.Amgen Ltd., Global Biostatistical ScienceUxbridgeUK

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