Access

You are not currently logged in.

Access JSTOR through your library or other institution:

login

Log in through your institution.

Journal Article

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
DOI: 10.2307/2685723
Stable URL: http://www.jstor.org/stable/2685723
Page Count: 5
Were these topics helpful?
See something inaccurate? Let us know!

Select the topics that are inaccurate.

  • Download ($14.00)
  • Add to My Lists
  • Cite this Item
When Log-Normal and Gamma Models Give Different Results: A Case Study
Preview not available

Abstract

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.

Page Thumbnails

  • Thumbnail: Page 
89
    89
  • Thumbnail: Page 
90
    90
  • Thumbnail: Page 
91
    91
  • Thumbnail: Page 
92
    92
  • Thumbnail: Page 
93
    93