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A Bayesian zero-inflated binomial regression and its application in dose-finding study.

Puntipa Wanitjirattikal1, Chenyang Shi2

  • 1Department of Statistics, King Mongkut'sInstitute of Technology Ladkrabang, Bangkok, Thailand.

Journal of Biopharmaceutical Statistics
|November 7, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new Bayesian model for early phase clinical trials to find the maximum-tolerated dose (MTD). It addresses challenges with zero dose-limiting toxicities (DLTs) and sudden increases in DLTs, improving drug safety and efficacy assessments.

Keywords:
Dose-limiting toxicitymaximum-tolerated dosemetropolis algorithmzero-inflated binomial regression

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Area of Science:

  • Clinical Pharmacology
  • Biostatistics
  • Drug Development

Background:

  • Accurate maximum-tolerated dose (MTD) determination is crucial for drug efficacy and safety in early phase clinical trials.
  • Conventional dose-finding trials often face challenges with an excess of "0 dose-limiting toxicities" (DLTs) and sudden, rapid increases in DLT rates at higher doses.
  • Existing MTD models inadequately address the complexities of zero DLT observations and abrupt DLT escalations.

Purpose of the Study:

  • To develop an advanced statistical model for robust MTD identification in early phase clinical trials.
  • To specifically account for and analyze cohorts with zero observed DLTs.
  • To accurately model the potential for sudden increases or "jumps" in DLT rates as dose escalates.

Main Methods:

  • Development of a Bayesian zero-inflated binomial regression model tailored for dose-finding studies.
  • The model analyzes dose-finding data by considering both the probability of zero DLTs and the binomial distribution of observed DLTs.
  • This approach allows for the assessment of underlying DLT risk even when no DLTs are initially observed.

Main Results:

  • The proposed Bayesian model effectively handles dose-finding data characterized by numerous zero DLT observations.
  • It provides a more accurate estimation of MTD by accounting for the potential for DLTs in cohorts initially showing none.
  • The model successfully captures and fits the "jump" phenomenon in DLT rates observed in some dose-escalation studies.

Conclusions:

  • The Bayesian zero-inflated binomial regression offers a superior approach for MTD finding compared to existing methods, particularly when dealing with sparse DLT data.
  • This methodology enhances the precision and reliability of MTD determination, leading to improved drug safety and efficacy profiles.
  • The model's ability to analyze zero DLTs and DLT jumps contributes to more informed decision-making in early phase clinical trial design.