
pmid: 23225642
This paper focuses on the development and study of the confidence interval procedures for mean difference between two treatments in the analysis of over‐dispersed count data in order to measure the efficacy of the experimental treatment over the standard treatment in clinical trials. In this study, two simple methods are proposed. One is based on a sandwich estimator of the variance of the regression estimator using the generalized estimating equations (GEEs) approach of Zeger and Liang (1986) and the other is based on an estimator of the variance of a ratio estimator (1977). We also develop three other procedures following the procedures studied by Newcombe (1998) and the procedure studied by Beal (1987). As assessed by Monte Carlo simulations, all the procedures have reasonably well coverage properties. Moreover, the interval procedure based on GEEs outperforms other interval procedures in the sense that it maintains the coverage very close to the nominal coverage level and that it has the shortest interval length, a satisfactory location property, and a very simple form, which can be easily implemented in the applied fields. Illustrative applications in the biological studies for these confidence interval procedures are also presented.
Parametric tolerance and confidence regions, Clinical Trials as Topic, Biometry, Phenyl Ethers, Reproduction, generalized estimating equations approach, Coronary Artery Disease, Cladocera, Applications of statistics to biology and medical sciences; meta analysis, asymptotic confidence interval, Hyperlipoproteinemia Type II, Treatment Outcome, Sample Size, Toxicity Tests, Confidence Intervals, Animals, Humans, mean difference, Monte Carlo Method, clustered count data, dispersion parameter
Parametric tolerance and confidence regions, Clinical Trials as Topic, Biometry, Phenyl Ethers, Reproduction, generalized estimating equations approach, Coronary Artery Disease, Cladocera, Applications of statistics to biology and medical sciences; meta analysis, asymptotic confidence interval, Hyperlipoproteinemia Type II, Treatment Outcome, Sample Size, Toxicity Tests, Confidence Intervals, Animals, Humans, mean difference, Monte Carlo Method, clustered count data, dispersion parameter
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