Limits...
Dichotomization: 2 x 2 (x2 x 2 x 2...) categories: infinite possibilities.

Heavner KK, Phillips CV, Burstyn I, Hare W - BMC Med Res Methodol (2010)

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health, University of Alberta, Edmonton, Alberta T6G 2L9, Canada. karynkh@aol.com

ABSTRACT

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.

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Related in: MedlinePlus

Sensitivity analysis of the relationship between BMI and high cholesterol in the NHANES sample, 1999-2006: Area under the curve for each BMI cutoff.
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Related In: Results  -  Collection

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Figure 2: Sensitivity analysis of the relationship between BMI and high cholesterol in the NHANES sample, 1999-2006: Area under the curve for each BMI cutoff.

Mentions: Figures 1 and 2 illustrate the ROC curve and graph of the area under the curve by cutoff for BMI cutoffs between the 25th and 75th percentiles. There is little difference in the area under the curve for cutoffs between 24 and 26 (with corresponding ORs varying from 1.8 to 1.6). The area under the curve decreases for cutoffs greater than 26. The area under the curve is greatest when the cutoff is 25.55, which would likely be the cutoff chosen based on this sensitivity analysis. (25.55 is also the cutoff that maximizes the Youden J statistic.) The cutoffs selected based on these four methods will likely be different for each dataset, making the results of studies that use these four dichotomization strategies difficult to compare.


Dichotomization: 2 x 2 (x2 x 2 x 2...) categories: infinite possibilities.

Heavner KK, Phillips CV, Burstyn I, Hare W - BMC Med Res Methodol (2010)

Sensitivity analysis of the relationship between BMI and high cholesterol in the NHANES sample, 1999-2006: Area under the curve for each BMI cutoff.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2902492&req=5

Figure 2: Sensitivity analysis of the relationship between BMI and high cholesterol in the NHANES sample, 1999-2006: Area under the curve for each BMI cutoff.
Mentions: Figures 1 and 2 illustrate the ROC curve and graph of the area under the curve by cutoff for BMI cutoffs between the 25th and 75th percentiles. There is little difference in the area under the curve for cutoffs between 24 and 26 (with corresponding ORs varying from 1.8 to 1.6). The area under the curve decreases for cutoffs greater than 26. The area under the curve is greatest when the cutoff is 25.55, which would likely be the cutoff chosen based on this sensitivity analysis. (25.55 is also the cutoff that maximizes the Youden J statistic.) The cutoffs selected based on these four methods will likely be different for each dataset, making the results of studies that use these four dichotomization strategies difficult to compare.

Bottom Line: 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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Public Health, University of Alberta, Edmonton, Alberta T6G 2L9, Canada. karynkh@aol.com

ABSTRACT

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.

Show MeSH
Related in: MedlinePlus