Extrapolation of Survival Curves from Cancer Trials Using External Information

Patricia Guyot, Tony Ades, Matthew Beasley, Bèranger Lueza, Jean Pierre Pignon, Nicky Welton

Research output: Contribution to journalArticle (Academic Journal)peer-review

24 Citations (Scopus)
763 Downloads (Pure)


Background: Estimates of life-expectancy are a key input to cost-effectiveness analysis (CEA) models for cancer treatments. Due to limited follow-up in Randomized Controlled Trials (RCTs), parametric models are frequently used to extrapolate survival outcomes beyond the RCT period.
However, different parametric models that fit the RCT data equally well may generate highly divergent predictions of treatment-related gain in life expectancy.

Objectives: We investigate the use of information external to the RCT data to inform model choice and estimation of life-expectancy.

Methods: We used Bayesian multi-parameter evidence synthesis to combine the RCT data with external information on general population survival, conditional survival from cancer registry databases, and expert opinion on the time course of the treatment effect. We illustrate with a 5 year follow-up RCT of cetuximab plus radiotherapy versus radiotherapy alone for head and neck cancer.

Results: Standard survival time distributions were insufficiently flexible to simultaneously fit both the RCT data and external data on general population survival. Using spline models we were able to estimate a model that was consistent with the trial data and all external data. A model integrating all sources of internal and external evidence achieved an adequate fit and predicted a 4.7 months (95% CrL: 0.4; 9.1) gain in life expectancy due to Cetuximab.

Conclusions: Long-term extrapolation using parametric models based on RCT data alone is highly unreliable and these models are unlikely to be consistent with external data. External data can be integrated with RCT data using spline models to enable long-term extrapolation. Conditional survival data could be used for many cancers and general population survival may have a role in other conditions. The use of external data should be guided by knowledge of natural history and treatment mechanisms.
Original languageEnglish
Pages (from-to)353-366
Number of pages14
JournalMedical Decision Making
Issue number4
Early online date28 Sep 2016
Publication statusPublished - May 2017

Structured keywords

  • ConDuCT-II


  • cost-effectiveness analysis
  • survival analysis
  • restricted cubic splines
  • external data
  • extrapolation


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