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Clinical Trial Design Using A Stopped Negative Binomial Distribution.

Michelle DeVeaux1, Michael J Kane2, Daniel Zelterman3

  • 1Regeneron Pharmaceuticals Inc., Tarrytown, NY, michelle.deveaux@regeneron.com.

Statistics and Its Interface
|January 19, 2019
PubMed
Summary
This summary is machine-generated.

We developed a new discrete distribution for early-stage clinical trials. This distribution models the minimum Bernoulli trials to reach a set number of successes or failures, offering a novel statistical tool.

Keywords:
curtailed clinical trialsdiscrete distributionsearly-stage clinical trialsstopped negative binomial distribution

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Area of Science:

  • Statistics
  • Clinical Trial Design
  • Probability Theory

Background:

  • Early-stage clinical trials often employ curtailed sampling rules.
  • Existing statistical distributions may not fully capture the complexities of these sampling methods.

Purpose of the Study:

  • To introduce a novel discrete distribution derived from curtailed sampling rules.
  • To provide a mathematical framework for analyzing trial outcomes based on success/failure thresholds.

Main Methods:

  • Derivation of a new probability distribution.
  • Analysis of independent and identically distributed Bernoulli trials.
  • Development of closed-form expressions for key statistical functions.

Main Results:

  • A new discrete distribution is formally defined.
  • Closed-form expressions for the mass function and moment generating function are provided.
  • Connections to established statistical distributions are established.

Conclusions:

  • The new distribution offers a valuable tool for statistical modeling in early-phase clinical trials.
  • This work enhances the understanding of probability distributions in sequential sampling scenarios.
  • The findings facilitate more precise analysis of trial stopping rules.