Confidence intervals for the risk ratio under inverse sampling

M. Tian, Man Lai TANG, H. K.T. Ng*, P. S. Chan

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

12 Citations (Scopus)

Abstract

In this paper, we investigate various confidence intervals for the risk ratio under inverse sampling (also known as negative binomial sampling). Three existing confidence intervals (namely, the confidence intervals that are based on Fieller's theorem, the delta method and the F-statistic) are reviewed and three new confidence intervals (namely, the score, likelihood ratio and saddlepoint approximation (SA)-based confidence intervals) are developed. Comparative studies among these confidence intervals through Monte Carlo simulations are evaluated in terms of their coverage probabilities and expected interval widths under different settings. Our simulation results suggest that the SA-based confidence interval is generally more appealing. We illustrate these confidence interval construction methods with real data sets from a drug comparison study and a congenital heart disease study.

Original languageEnglish
Pages (from-to)3301-3324
Number of pages24
JournalStatistics in Medicine
Volume27
Issue number17
DOIs
Publication statusPublished - 30 Jul 2008

Scopus Subject Areas

  • Epidemiology
  • Statistics and Probability

User-Defined Keywords

  • Coverage probability
  • Expected confidence width
  • Inverse sampling
  • Likelihood ratio statistic
  • Monte Carlo method
  • Saddlepoint approximation
  • Score statistic

Fingerprint

Dive into the research topics of 'Confidence intervals for the risk ratio under inverse sampling'. Together they form a unique fingerprint.

Cite this