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Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells
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Semiparametric bayes multiple testing: applications to tumor data.

Lianming Wang1, David B Dunson

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina 29208, USA. wang99@mailbox.sc.edu

Biometrics
|August 14, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel statistical model for National Toxicology Program (NTP) studies to jointly analyze cancer risks across multiple tumor types. The approach improves carcinogenicity assessment by accounting for tumor correlations and estimating dose-response relationships.

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Area of Science:

  • Toxicology
  • Biostatistics
  • Cancer Research

Background:

  • National Toxicology Program (NTP) studies aim to identify carcinogens and their dose-response profiles.
  • Univariate analyses for each tumor type may miss complex correlations.
  • Joint inference is preferred for a comprehensive assessment of carcinogenicity.

Purpose of the Study:

  • To develop a statistical model for joint inference of carcinogenicity across multiple tumor types in NTP studies.
  • To estimate dose-response profiles while accounting for inter-tumor correlations.
  • To provide a framework for evaluating global and local hypotheses regarding carcinogenicity.

Main Methods:

  • A random effects logistic model with a coefficient matrix for log-odds ratios between adjacent dose groups.
  • Nonparametric priors are proposed for coefficients to capture correlations and enable information sharing.
  • Monte Carlo Markov chain (MCMC) methods for model fitting and hypothesis evaluation.
  • Two multiple testing procedures for local hypothesis testing based on posterior probabilities.

Main Results:

  • The proposed model allows for joint inference, improving the assessment of carcinogenicity.
  • The approach effectively characterizes correlations among different tumor types.
  • Information borrowing across dose groups and tumor types enhances estimation accuracy.
  • Simulation studies and analysis of an NTP dataset demonstrate the model's utility.

Conclusions:

  • The developed statistical model offers a robust method for analyzing carcinogenicity data in NTP studies.
  • Joint inference provides a more accurate and comprehensive understanding of test agent effects.
  • The approach facilitates efficient evaluation of complex hypotheses related to cancer risk.