Sequential Medical Trials (Stopping Rules/Asymptotic Optimality).

Abstract

A model for sequential clinical trials is discussed. Three proposed stopping rules are studied by Monte Carlo for small patient horizons and mathematically for large patient horizons. They are shown to be about equally effective and asymptotically optimal from both Bayesian and frequentist points of view. Their advantage over any fixed sample size rule is emphasized. (Author)

Document Details

Document Type
Technical Report
Publication Date
Feb 04, 1980
Accession Number
ADA082920

Entities

People

  • Bruce Levin
  • David Siegmund
  • Herbert Robbins
  • T. L. Lai

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Clinical Trials
  • Cooperation
  • Data Science
  • Information Science
  • New York
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Neurotrauma and Rehabilitation Medicine.
  • Statistical inference.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms