Generalized Norton‐Simon models of tumour growth

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

This paper considers the analysis of serial data on the growth of tumours in laboratory rodents. I propose a model ‐ a generalization of the tumour growth model of Norton and Simon ‐ that leads to a rich family of growth and decay curves. The model assumes that unperturbed growth follows the generalized logistic form; it accommodates time‐varying treatment effects through an effective dose function. I fit two such models to data on a human prostate tumour growing in nude mice and compare the fitted curves and dose functions with a non‐parametric curve and dose function estimated from a cubic spline model. All three models account for both random animal effects and autocorrelation. Monte Carlo results suggest that (a) maximum likelihood estimates of growth parameters are biased, although not severely, and (b) standard errors are conservative in small samples but become increasingly accurate in larger samples.

Original languageEnglish (US)
Pages (from-to)1075-1088
Number of pages14
JournalStatistics in Medicine
Volume10
Issue number7
DOIs
StatePublished - Jan 1 1991

Fingerprint

Tumor Growth
Growth
Dose
Neoplasms
Curve
Tumor
Likelihood Functions
Model
Cubic Spline
Treatment Effects
Standard error
Growth Model
Maximum Likelihood Estimate
Autocorrelation
Nude Mice
Small Sample
Logistics
Biased
Mouse
Prostate

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

Cite this

Generalized Norton‐Simon models of tumour growth. / Heitjan, Daniel F.

In: Statistics in Medicine, Vol. 10, No. 7, 01.01.1991, p. 1075-1088.

Research output: Contribution to journalArticle

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