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Data in the MMWR

Comparison (4 of 12)

Data in the MMWR (§5.3): Compare quantities: differences and ratios
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2026-01-06

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Data in the MMWR (§5.3): Compare quantities: differences and ratios On this site (opens in a new tab)
[2026-01-06]

Principles: When a set of quantities is to be compared, it is best to contrast them directly, usually as a difference or a ratio, and to assess the contrast. In practice, the difference or ratio can be constructed either empirically, such as by taking the difference of 2 means or the ratio of 2 prevalence estimates, or in a model, such as the difference between 2 coefficients in a regression model. Formal inference procedures, if any, are then applied to the contrast.

Observations: Reports include several kinds of contrast, including rate ratios comparing rates between age groups and absolute change in proportion, relative change in rate, and relative change in rate ratio; absolute and percent changes in rate between time points. Many reports present measures of primary effects and statistical tests for whether those primary effects differ (beyond statistical variation), without directly quantifying the actual contrasts that are being tested. Some comparisons between values are described as statistically significant, but the numerical differences between values are not presented.

Recommendations:

Where analysis compares quantities, it is best to present differences or ratios directly, even if the components being compared are also presented. The practical significance of a result typically pertains to these quantified contrasts.

Ensure that differences, such as the (absolute) risk difference are labeled, described, and interpreted correctly. Adopt terminology that is unambiguous. The terms “risk ratio” and “relative risk” can be used for several kinds of ratios. A more specific term should be used, such as “prevalence ratio” or “hazard ratio”, where such usage aids clarity.

Ensure that ratios are labeled, described, and interpreted correctly.

Ratio: one number divided by another, typically capturing a relative measure, such as prevalence ratio, odds ratio, and hazard ratio.

Both a prevalence ratio and an odds ratio can be interpreted qualitatively as indicating that one event is more or less likely than another event. Only a prevalence ratio can be interpreted quantitatively as “Event X is 1.2 times more likely than event Y.”

Next topic: Adjust quantities: standardization and regression models (§5.4)

Data in the MMWR