When to use absolute vs relative effect measures

[f2harrell]

@tomasbencomo

Absolute vs relative risk for binary endpoints now makes sense, but I’m having trouble transferring your explanation to a continuous endpoint like blood pressure. Although a relative measure seems preferable because of its transferability, because blood pressure has a physical meaning and a limited range of values that can feasibly exist, my instinct tells me an absolute measure could also be used.

For a continuous response variable such as blood pressure this is largely a non-issue as there is only a few choices and these all use absolute blood pressure:

• difference in means
• difference in medians
• entire posterior distribution of difference in means
• concordance probability (c-index / Mann-Whiteney U-statistic): P(randomly chosen patient on tx B has a lower BP than a randomly chosen patient on tx A); for a normal distribution this is a simple function of the difference in means and the SD

@Jochen

I don’t subscribe to the statement that relative effect sizes are helpful to transfer findings to other populations. I would be thankful for a concrete example where this really makes any sense in practice.

The fact that relative measures when suitably chosen (ii.e. hazard ratio, odds ratio, not risk ratio) better transport to other populations has been extensively shown. I’ve touched on this in blog articles and course notes. But take a look at the myriad of forest plots shown in clinical trial reports, where you see amazing constancy of odds and hazard ratios. And absolute risk difference or difference in life expectancy vary wildly with baseline risk.