Large Sample Theory for Sequential Analysis of the Proportional Hazards Model.

Abstract

An appropriate large sample theory for sequential analysis of the Cox proportional hazards model is developed. For clinical trials with simultaneous entry of patients, the efficient score process of the partial likelihood is easily seen to be a martingale. It follows that, in a time scale based on the observed Fisher information, the score process and the properly normalized maximum partial likelihood estimator behave asymptotically like Brownian motion. When entry is staggered, the efficient score process is no longer a martingale in general. However, if patients in a staggered-entry clinical trial are assumed to be independent and identically distributed, independently of entry time, then the score process is well approximated by a martingale. The asymptotic results involving weak convergence to Brownian motion hold as before. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1982
Accession Number
ADA120646

Entities

People

  • Thomas Sellke

Organizations

  • Stanford University

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Asymptotic Normality
  • Brownian Motion
  • Clinical Trials
  • Computational Science
  • Data Science
  • Gaussian Processes
  • Information Science
  • New York
  • Normal Distribution
  • Probability
  • Random Variables
  • Sequential Analysis
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Psychometric Testing or Psychological Assessment.
  • Statistical inference.