A Flexible Ensemble Learning Method for Survival Extrapolation.

Journal: Therapeutic innovation & regulatory science
Published Date: Dec 19, 2022

Abstract

OBJECTIVES: Survival extrapolation is an important statistical concept for estimating long-term survival from short-term clinical trial data. It is widely used in health technology assessment (HTA). Survival extrapolation is often performed by fitting one or two parametric models selected based on experience or selecting a model based on some goodness of fit statistics from a predefined collection of models. The main challenge in survival extrapolation is that the result is sensitive to model misspecification. In this study, we aim to propose a new approach that has a robust performance for survival extrapolation.

Authors

  • Ran Dai
    Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
  • Jihyun Ma
    Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE, USA.
  • Meijing Wu
    Biostatistics & Statistical Programming, Sanofi US Services Inc., 450 Water Street, Cambridge, MA, 02141, USA. meijing.wu@sanofi.com.
  • Yabing Mai
    Biostatistics and Data Sciences TCM, Boehringer Ingelheim, Shanghai, China.
  • Weili He
    Data and Statistical Sciences, Abbvie Inc., North Chicago, Illinois, USA.

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