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A utility-based design for randomized comparative trials with ordinal outcomes and prognostic subgroups.

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Medical Statistics

Background:

  • Patient heterogeneity can impact comparative trial outcomes.
  • Ordinal outcomes are common in clinical research but challenging to analyze.
  • Prognostic subgroups require specialized trial designs for accurate subgroup-specific conclusions.

Purpose of the Study:

  • To propose and evaluate novel designs for randomized comparative trials with ordinal outcomes and prognostic subgroups.
  • To account for patient heterogeneity by allowing for potentially different treatment effects within subgroups.
  • To compare designs that incorporate treatment-subgroup interactions against a homogeneous model.

Main Methods:

  • Development of a Bayesian probability model incorporating utilities for ordinal outcome levels.
  • Consideration of two models with treatment-subgroup interactions: proportional odds and a hierarchical non-proportional odds model.
  • Application of these designs to construct group sequential designs for a nutritional prehabilitation trial in esophageal cancer patients.
  • Simulation study to compare the performance of the three proposed designs.

Main Results:

  • The proposed designs effectively account for patient heterogeneity and allow for subgroup-specific comparative conclusions.
  • Simulation results evaluate within-subgroup type I and II error probabilities under various interaction scenarios.
  • The study demonstrates the application of these advanced designs in a real-world clinical trial context.

Conclusions:

  • The novel designs offer a robust framework for analyzing randomized comparative trials with ordinal outcomes and prognostic subgroups.
  • Accounting for treatment-subgroup interactions provides more nuanced and accurate conclusions compared to homogeneous models.
  • These methods enhance the ability to detect treatment effects within specific patient subgroups, optimizing trial interpretation.