Sample size calculations for logistic and Poisson regression models

Gwowen Shieh*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

30 Scopus citations

Abstract

A method is proposed for improving sample size calculations for logistic and Poisson regression models by incorporating the limiting value of the maximum likelihood estimates of nuisance parameters under the composite null hypothesis. The method modifies existing approaches of Whittemore (1981) and Signorini (1991) and provides explicit formulae for determining the sample size needed to test hypotheses about a single parameter at a specified significance level and power. Simulation studies assess its accuracy for various model configurations and covariate distributions. The results show that the proposed method is more accurate than the previous approaches over the range of conditions considered here.

Original languageAmerican English
Pages (from-to)1193-1199
Number of pages7
JournalBiometrika
Volume88
Issue number4
DOIs
StatePublished - 1 Dec 2001

Keywords

  • Generalised linear model
  • Information matrix
  • Logistic regression
  • Maximum likelihood estimator
  • Poisson regression
  • Power
  • Sample size
  • Wald statistic

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