Exact Stratified Linear Rank Tests for Binary Data

Abstract

We present an efficient network algorithm for generating exact permutational distributions for linear rank tests defined on stratified 2 x c contingency tables. The algorithm can evaluate exact one and two sided p-values, and compute exact confidence intervals for trend parameters arising from certain log linear and logistic models embedded in these contingency tables. It is especially efficient for highly imbalanced categorical data, a situation where the asymptotic theory is unreliable. Part of the algorithm can be adapted to evaluating the conditional maximum likelihood and its derivatives for the logistic regression model, with grouped data. We illustrate the techniques with an analysis of two data sets; the leukemia data on the Hiroshima atomic bomb survivors, and data from a clinical trial of bone marrow transplant.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007135

Entities

People

  • Cyrus R. Mehta
  • Nitin Patel
  • Pralay Senchaudhuri

Organizations

  • Harvard T.H. Chan School of Public Health

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies
  • Weapons Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Asymptotic Normality
  • Binomials
  • Computer Science
  • Convolution
  • Data Analysis
  • Data Mining
  • Data Science
  • Data Sets
  • Equations
  • Information Science
  • Network Science
  • Probability
  • Probability Distributions
  • Public Health
  • Random Variables
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Immunology and Pathology
  • Neural Network Machine Learning.
  • Regression Analysis.

Technology Areas

  • Biotechnology