Modeling the labeling index distribution: An application of functional data analysis

Patricia M. Grambsch, Bryan L. Randall, Roberd M. Bostick, John D. Potter, Thomas A. Louis

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


This article presents exploratory data analytic methodology for visualizing and summarizing data that can be represented as individual-specific curves. We propose a simplified form of functional data analysis. A nonparametric scatterplot smooth is applied to each individual’s data, followed by a principal components analysis of the smoothed data. We then display the individual smooth curves in an array organized by principal component scores. The display suggests interpretable summary measures. The methodology is applied to the measurement of proliferative activity, a biomarker for colon cancer risk. We use the summary measures in the analysis of a pilot study clinical trial.

Original languageEnglish (US)
Pages (from-to)813-821
Number of pages9
JournalJournal of the American Statistical Association
Issue number431
StatePublished - Sep 1995
Externally publishedYes


  • Descriptive statistics
  • Principal components
  • Proliferative index
  • Scatterplot smoothing

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


Dive into the research topics of 'Modeling the labeling index distribution: An application of functional data analysis'. Together they form a unique fingerprint.

Cite this