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When Log-Normal and Gamma Models Give Different Results: A Case Study
Brian L. Wiens
The American Statistician
Vol. 53, No. 2 (May, 1999), pp. 89-93
Stable URL: http://www.jstor.org/stable/2685723
Page Count: 5
You can always find the topics here!Topics: Vaccination, Datasets, Antibodies, Modeling, Statistical models, Parametric models, Analytical estimating, Outliers, Generalized linear model, Statistical discrepancies
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The analysis of a simple dataset with two similar models is considered. A generalized linear model assuming a log-normal distribution and a generalized linear model assuming a gamma distribution are two models assuming constant coefficient of variation (CCV). Sources in the literature indicate that these two models are often interchangeable. However, in this real dataset-obtained from a clinical trial of a vaccine product-the two models do not agree. Reasons for this lack of agreement are explored. It is proposed that analyzing a dataset with both of the models may be an ad hoc robustness analysis of the dependence of the conclusions on the assumed model.
The American Statistician © 1999 American Statistical Association