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Improved inference for MCP-Mod approach using time-to-event endpoints with small sample sizes.

Márcio A Diniz1, Diego I Gallardo2, Tiago M Magalhães3

  • 1Biostatistics Research Center, Samuel Oschin Comprehensive Cancer Center, Cedars-Sinai Medical Center, California, Los Angeles, USA.

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Summary
This summary is machine-generated.

Improved Maximum Likelihood (ML) estimators offer better bias control for small sample sizes in the Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework. These enhanced estimators ensure type I error control in phase II studies.

Keywords:
MCP-Mod approachWeibull modelbias correctioncovariance refinementsmall sample size

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Statistical Modeling

Background:

  • The Multiple Comparison Procedures with Modeling Techniques (MCP-Mod) framework is approved for phase II studies.
  • Current MCP-Mod relies on Maximum Likelihood (ML) estimators with asymptotic properties unsuitable for small sample sizes.

Purpose of the Study:

  • To derive improved ML estimators and covariance matrix corrections for the censored Weibull regression model.
  • To evaluate the performance of improved ML estimators within standard regression and MCP-Mod frameworks.

Main Methods:

  • Developed corrective and preventive approaches for improved ML estimators.
  • Conducted two simulation studies comparing ML and improved ML estimators.
  • Evaluated estimators and covariance matrices in regression and MCP-Mod settings.

Main Results:

  • Improved ML estimators demonstrated reduced bias compared to standard ML estimators.
  • Wald-type statistics derived from improved ML estimators effectively controlled type I error.
  • Type I error control was maintained without power loss across both tested frameworks.

Conclusions:

  • Improved ML estimators are recommended for the MCP-Mod approach, especially for small sample sizes (5-25 subjects per dose).
  • These estimators ensure type I error is controlled at the nominal level in phase II clinical trials using MCP-Mod.