NDLI: Confidence intervals for ratio of two Poisson rates using the method of variance estimates recovery

Content Provider	Springer Nature Link
Author	Li, Hui Qiong Tang, Man Lai Wong, Weng Kee
Copyright Year	2013
Abstract	Inference based on ratio of two independent Poisson rates is common in epidemiological studies. We study the performance of a variety of unconditional method of variance estimates recovery (MOVER) methods of combining separate confidence intervals for two single Poisson rates to form a confidence interval for their ratio. We consider confidence intervals derived from (1) the Fieller’s theorem, (2) the logarithmic transformation with the delta method and (3) the substitution method. We evaluate the performance of 13 such types of confidence intervals by comparing their empirical coverage probabilities, empirical confidence widths, ratios of mesial non-coverage probability and total non-coverage probabilities. Our simulation results suggest that the MOVER Rao score confidence intervals based on the Fieller’s theorem and the substitution method are preferable. We provide two applications to construct confidence intervals for the ratio of two Poisson rates in a breast cancer study and in a study that examines coronary heart diseases incidences among post menopausal women treated with or without hormones.
Starting Page	869
Ending Page	889
Page Count	21
File Format	PDF
ISSN	09434062
Journal	Computational Statistics
Volume Number	29
Issue Number	3-4
e-ISSN	16139658
Language	English
Publisher	Springer Berlin Heidelberg
Publisher Date	2013-12-03
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Agresti–Coull Binomial method Bayes interval Fieller’s theorem Jeffreys interval MOVER method Rao score Statistics Probability and Statistics in Computer Science Probability Theory and Stochastic Processes Economic Theory
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Statistics, Probability and Uncertainty Computational Mathematics

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