Small-sample inference for incomplete longitudinal data with truncation and censoring in tumor xenograft models

Ming Tan, Hong Bin Fang, Guo Liang Tian, Peter J. Houghton

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

Abstract

In cancer drug development, demonstrating activity in xenograft models, where mice are grafted with human cancer cells, is an important step in bringing a promising compound to humans. A key outcome variable is the tumor volume measured in a given period of time for groups of mice given different doses of a single or combination anticancer regimen. However, a mouse may die before the end of a study or may be sacrificed when its tumor volume quadruples, and its tumor may be suppressed for some time and then grow back. Thus, incomplete repeated measurements arise. The incompleteness or missingness is also caused by drastic tumor shrinkage (<0.01 cm3) or random truncation. Because of the small sample sizes in these models, asymptotic inferences are usually not appropriate. We propose two parametric test procedures based on the EM algorithm and the Bayesian method to compare treatment effects among different groups while accounting for informative censoring. A real xenograft study on a new antitumor agent, temozolomide, combined with irinotecan is analyzed using the proposed methods.

Original languageEnglish (US)
Pages (from-to)612-620
Number of pages9
JournalBiometrics
Volume58
Issue number3
DOIs
StatePublished - Sep 2002
Externally publishedYes

Keywords

  • Bayesian analysis
  • EM algorithm
  • Informative censoring
  • Longitudinal data
  • T-Test
  • Truncation
  • Tumor xenograft models

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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