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American Journal of Epidemiology Vol. 152, No. 7 : 688-689
Copyright © 2000 by The Johns Hopkins University School of Hygiene and Public Health

LETTERS TO THE EDITOR

RE: "PROBLEMS DUE TO SMALL SAMPLES AND SPARSE DATA IN CONDITIONAL LOGISTIC REGRESSION ANALYSIS"

Beat E. Neuenschwander and Marcel Zwahlen

Swiss Federal Office of Public Health Division of Epidemiology and Infectious Diseases 3003 Berne, Switzerland

Greenland et al. (1Go) offer a critical discussion of problems arising in the conditional logistic regression model when information in the data is weak relative to background knowledge. They warn against an uncritical use of this model and recommend that "analysts need to inspect their data in detail in order to alert themselves to the possible danger [of statistical bias]..." (1, p. 537). We fully agree on this point but would like to add two remarks to their investigation.

Statistical inference, the methodology used most widely to tackle inference problems in the empirical sciences, is hampered by two major difficulties:

  1. To . . . [Full Text of this Article]

REFERENCES

Sander Greenland

Department of Epidemiology UCLA School of Public Health Department of Statistics UCLA College of Letters and Science Los Angeles, CA 90095–1772


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B. E. Neuenschwander, M. Zwahlen, and S. Greenland
RE: "PROBLEMS DUE TO SMALL SAMPLES AND SPARSE DATA IN CONDITIONAL LOGISTIC REGRESSION ANALYSIS"
Am. J. Epidemiol., October 1, 2000; 152(7): 688 - 689.
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