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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
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Bayesian single-arm phase II trial designs with time-to-event endpoints.

Jianrong Wu1, Haitao Pan2, Chia-Wei Hsu2

  • 1Biostatistics and Bioinformatics Shared Resource Facility, Markey Cancer Center, University of Kentucky, Lexington, Kentucky, USA.

Pharmaceutical Statistics
|June 4, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces novel Bayesian trial designs for cancer research, focusing on time-to-event endpoints in immunotherapy and targeted therapy trials. These methods offer practical tools for designing efficient clinical trials with robust statistical properties.

Keywords:
Bayesian designphase II trialproportional hazardssample size calculationtime-to-event endpoint

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Oncology

Background:

  • Time-to-event endpoints are crucial in cancer clinical trials, particularly for immunotherapy and molecularly targeted therapies.
  • Bayesian approaches offer advantages for single-arm Phase II trial designs, especially with small sample sizes.

Purpose of the Study:

  • To present an event-driven Bayesian approach for one-stage and two-stage single-arm Phase II trial designs.
  • To develop theoretical relationships between frequentist and Bayesian trial designs.
  • To provide practical tools for investigators designing cancer clinical trials.

Main Methods:

  • Developed Bayesian one-stage and two-stage trial designs.
  • Established theoretical links between frequentist and Bayesian design properties.
  • Utilized exact posterior distributions for small sample sizes.
  • Extended Simon's two-stage design for time-to-event endpoints.

Main Results:

  • Proposed executable algorithms for Bayesian one-stage designs.
  • Demonstrated how frequentist properties can be achieved with Bayesian designs.
  • Showcased the accommodation of small sample sizes in Phase II trials.
  • Comprehensive simulations evaluated the frequentist properties of the proposed Bayesian designs.

Conclusions:

  • The developed Bayesian designs provide a flexible and statistically sound framework for cancer clinical trials.
  • An R package, BayesDesign, is available for convenient implementation of these methods.
  • These advancements facilitate the design of efficient Phase II trials with time-to-event endpoints.