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Area of Science:

  • Health Economics and Outcomes Research
  • Biostatistics
  • Oncology

Background:

  • Bevacizumab and similar oncology drugs are approved for multiple indications.
  • Health technology assessment (HTA) traditionally appraises drugs within single indications, limiting evidence scope.
  • Existing HTA methods may not fully leverage data from all drug indications.

Purpose of the Study:

  • To explore and evaluate multi-indication meta-analysis methods for synthesizing evidence across indications.
  • To assess the performance of different synthesis models in predicting overall survival (OS) in a target indication.

Main Methods:

  • Conducted a simulation study using a multistate disease progression model.
  • Evaluated univariate (mixture and non-mixture) and bivariate surrogacy models.
  • Simulated datasets with varying heterogeneity, outlier indications, and OS data availability.

Main Results:

  • Univariate multi-indication methods reduced uncertainty without increasing bias when OS data was available in the target indication.
  • Mixture models offered no significant performance improvement over univariate methods for HTA.
  • Bivariate surrogacy models showed potential for bias correction in scenarios lacking target OS data and with outlier indications.

Conclusions:

  • Multi-indication meta-analysis methods, though complex, can enhance HTA by reducing uncertainty.
  • Univariate methods are effective when target OS data is present.
  • Bivariate models warrant further investigation for complex HTA scenarios.