Summary
Objectives:
In this article, we illustrate and compare exact simultaneous confidence sets with
various approximate simultaneous confidence intervals for multiple ratios as applied
to many-to-one comparisons. Quite different datasets are analyzed to clarify the points.
Methods:
The methods are based on existing probability inequalities (e.g., Bonferroni, Slepian
and Šidàk), estimation of nuisance parameters and re-sampling techniques. Exact simultaneous
confidence sets based on the multivariate t-distribution are constructed and compared with approximate simultaneous confidence
intervals.
Results:
It is found that the coverage probabilities associated with the various methods of
constructing simultaneous confidence intervals (for ratios) in many-to-one comparisons
depend on the ratios of the coefficient of variation for the mean of the control group
to the coefficient of variation for the mean of the treatments. If the ratios of the
coefficients of variations are less than one, the Bonferroni corrected Fieller confidence
intervals have almost the same coverage probability as the exact simultaneous confidence
sets. Otherwise, the use of Bonferroni intervals leads to conservative results.
Conclusions:
When the ratio of the coefficient of variation for the mean of the control group
to the coefficient of variation for the mean of the treatments are greater than one
(e.g., in balanced designs with increasing effects), the Bonferroni simultaneous confidence
intervals are too conservative. Therefore, we recommend not using Bonferroni for this
kind of data. On the other hand, the plug-in method maintains the intended confidence
coefficient quite satisfactorily; therefore, it can serve as the best alternative
in any case.
Keywords
Bonferroni - Fieller’s theorem - many-to-one comparisons - multivariate
t-distribution - simultaneous confidence sets