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Phase II trial design with growth modulation index as the primary endpoint

Jianrong Wu1,2, Li Chen1,2, Jing Wei3

  • 1Division of Cancer Biostatistics, University of Kentucky, Lexington, Kentucky.

Pharmaceutical Statistics
|November 21, 2018
PubMed

Insights

This study introduces a new sample size formula for clinical trials focused on the growth modulation index (GMI) in cancer patients. This method offers statistical guidance for phase II trials evaluating GMI as a primary efficacy endpoint.

Area of Science:

  • Oncology
  • Biostatistics
  • Clinical Trial Design

Background:

  • Advanced cancer treatments increasingly utilize molecularly targeted therapies, genomic-driven approaches, and immunotherapies.
  • The growth modulation index (GMI) is a key metric for assessing treatment efficacy in relapse or refractory cancer patients.
  • Limited literature exists on clinical trial designs specifically using GMI as a primary endpoint.

Purpose of the Study:

  • To derive a sample size formula for score tests within a log-linear model for GMI.
  • To provide statistically sound methods for phase II clinical trial designs with GMI as the primary endpoint.

Main Methods:

  • Derivation of a sample size formula for the score test under a log-linear model of GMI.
  • Illustration of study designs using the derived formula, incorporating bivariate exponential and Weibull frailty models.
  • Application to generalized treatment effect size for robust statistical analysis.

Main Results:

  • A novel sample size formula for GMI-based primary endpoint analysis in clinical trials has been developed.
  • The derived formula is applicable across various statistical models, including bivariate exponential and Weibull frailty models.
  • The proposed methods offer a statistically rigorous framework for single-arm phase II trials.

Conclusions:

  • The derived sample size formula provides a robust statistical foundation for designing phase II clinical trials where GMI is the primary endpoint.
  • This work addresses a gap in the literature by offering practical guidance for GMI-focused trial design.
  • The proposed statistical methods support the advancement of precision medicine in oncology by enabling better evaluation of novel cancer therapies.
Keywords:
accelerated failure time modelclinical trial designgrowth modulation Indexpaired time-to-progressionsample size

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