, Volume 37, Issue 5, pp 727–737 | Cite as

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


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).


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.


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).


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 identifier NCT01568866 (ENDEAVOR trial).



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


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)


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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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