Research article
Dichotomization: 2 × 2 (×2 × 2 × 2...) categories: infinite possibilities
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* Corresponding author: Karyn K Heavner karynkh@aol.com
1 School of Public Health, University of Alberta, Edmonton, Alberta, T6G 2L9, Canada
2 TobaccoHarmReduction.org, Saint Paul, MN, 55104, USA
3 Department of Environmental and Occupational Health, Drexel University School of Public Health, Philadelphia, Pennsylvania, 19102, USA
4 Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Edmonton, Alberta, T6G 1K4, Canada
5 Department of Math, Statistics, and Physics, University of British Columbia, Okanagan, Kelowna, British Columbia, V1V1V7, Canada
BMC Medical Research Methodology 2010, 10:59 doi:10.1186/1471-2288-10-59
Published: 23 June 2010Abstract
Background
Consumers of epidemiology may prefer to have one measure of risk arising from analysis of a 2-by-2 table. However, reporting a single measure of association, such as one odds ratio (OR) and 95% confidence interval, from a continuous exposure variable that was dichotomized withholds much potentially useful information. Results of this type of analysis are often reported for one such dichotomization, as if no other cutoffs were investigated or even possible.
Methods
This analysis demonstrates the effect of using different theory and data driven cutoffs on the relationship between body mass index and high cholesterol using National Health and Nutrition Examination Survey data. The recommended analytic approach, presentation of a graph of ORs for a range of cutoffs, is the focus of most of the results and discussion.
Results
These cutoff variations resulted in ORs between 1.1 and 1.9. This allows investigators to select a result that either strongly supports or provides negligible support for an association; a choice that is invisible to readers. The OR curve presents readers with more information about the exposure disease relationship than a single OR and 95% confidence interval.
Conclusion
As well as offering results for additional cutoffs that may be of interest to readers, the OR curve provides an indication of whether the study focuses on a reasonable representation of the data or outlier results. It offers more information about trends in the association as the cutoff changes and the implications of random fluctuations than a single OR and 95% confidence interval.