Modeling Covariate-Adjusted Survival for Economic Evaluations in Oncology.

Pharmacoeconomics

Global Health Economics, Amgen (Europe) GmbH, Suurstoffi 22, 6343, Rotkreuz, Zug, Switzerland.

Published: May 2019

AI Article Synopsis

  • In oncology economic evaluations, it's crucial to adjust survival outcomes for imbalances in patient characteristics across treatment groups to ensure accurate results, but current guidelines on how to perform these adjustments are lacking.
  • This study assessed various survival modeling methods using data from the ENDEAVOR trial, which compared two treatments for multiple myeloma: carfilzomib with dexamethasone and bortezomib with dexamethasone.
  • The findings indicated that while different methods yielded similar survival outcome differences, some approaches led to skewed predictions or implausible long-term survival estimates, highlighting the importance of using appropriate adjustment methods in survival analysis.

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).

Download full-text PDF

Source
http://dx.doi.org/10.1007/s40273-018-0759-6DOI Listing

Publication Analysis

Top Keywords

economic evaluations
12
adjusted survival
12
survival modeling
12
survival
9
covariate-adjusted survival
8
evaluations oncology
8
modeling approaches
8
endeavor trial
8
weighted data
8
data approach
8

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!