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.

Show MeSH

Related in: MedlinePlus

Odds ratio curves for the relationship between high cholesterol and different BMU cutoffs in the NHANES sample, 1999-2006: The effect of changing the BMI cutoff between the 25th and 75th percentile of BMI on the OR.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2902492&req=5

Figure 4: Odds ratio curves for the relationship between high cholesterol and different BMU cutoffs in the NHANES sample, 1999-2006: The effect of changing the BMI cutoff between the 25th and 75th percentile of BMI on the OR.

Mentions: A representation of the OR curve that excludes the unstable tails is presented in Figure 4. This figure limits the ORs to those obtained from cutoffs between the 25th and 75th percentiles of BMI. The OR decreases from 1.9 to 1.1 as the cutoff increases within this range, consistent with the ROC curve. In other words, as the cutoff increases, the reference (lower BMI) group contains more people who have high cholesterol. Ideally the entire OR curve would be presented but researchers must weigh the additional information gained against the imprecise estimates and potentially more complex regression analysis (e.g., need for additional iterations or non-logistic models) in the tails of the exposure cutoff distribution.


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)

Odds ratio curves for the relationship between high cholesterol and different BMU cutoffs in the NHANES sample, 1999-2006: The effect of changing the BMI cutoff between the 25th and 75th percentile of BMI on the OR.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Odds ratio curves for the relationship between high cholesterol and different BMU cutoffs in the NHANES sample, 1999-2006: The effect of changing the BMI cutoff between the 25th and 75th percentile of BMI on the OR.
Mentions: A representation of the OR curve that excludes the unstable tails is presented in Figure 4. This figure limits the ORs to those obtained from cutoffs between the 25th and 75th percentiles of BMI. The OR decreases from 1.9 to 1.1 as the cutoff increases within this range, consistent with the ROC curve. In other words, as the cutoff increases, the reference (lower BMI) group contains more people who have high cholesterol. Ideally the entire OR curve would be presented but researchers must weigh the additional information gained against the imprecise estimates and potentially more complex regression analysis (e.g., need for additional iterations or non-logistic models) in the tails of the exposure cutoff distribution.

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