# Should one derive risk difference from the odds ratio?

**URL:** <https://discourse.datamethods.org/t/should-one-derive-risk-difference-from-the-odds-ratio/4403>\
**Category:** bayes\
**Tags:** absolute-risk\
**Created:** [May 13, 2021, 11:49pm UTC](https://discourse.datamethods.org/t/should-one-derive-risk-difference-from-the-odds-ratio/4403 "2021-05-13T23:49:41Z")\
**Posts on this page:** 1\
**Showing post:** 92

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**Author:** ![R\_cubed](https://discourse.datamethods.org/user_avatar/discourse.datamethods.org/r_cubed/32/1518_2.png) [@R\_cubed](https://discourse.datamethods.org/u/R_cubed)\
**Post date:** [June 25, 2021, 1:50am UTC](https://discourse.datamethods.org/t/should-one-derive-risk-difference-from-the-odds-ratio/4403/92 "2021-06-25T01:50:29Z")

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> [@Pavlos\_Msaouel](#):
>
> Non-collapsibility though is different than confounding. … (he is) unable to reconcile this with any causal mechanisms found in the natural world.

Any thoughts on this post by @f2harrell on the value of building up marginal effect estimates from reasonably adjusted conditional ones?

> **[Unadjusted Odds Ratios are Conditional – Statistical Thinking](https://www.fharrell.com/post/marg/)**
>
> This article discusses issues with unadjusted effect ratios such as odds ratios and hazard ratios, showing a simple example of non-generalizability of unadjusted odds ratios.

I’m somewhat surprised this issue hasn’t been approached from an information theoretic or Bayesian decision theoretic experimental design perspective.

Awhile back, I posted a number of links that describe Bayesian experimental design. In that framework, maximizing expected utility is equivalent to maximizing the information from the experiment.

> [@Who should define reasonable priors and how?](https://discourse.datamethods.org/t/who-should-define-reasonable-priors-and-how/2935/11):
>
> It is hard for me to imagine doing any experiment without considering the value of the information. Methodology scholars have long complained that experiments have been “under powered”, and that much clinical research is not useful [1]. IJ Good considered information as something he called a “quasi-utility.” [2, p.40-41] This was also seen by Lindley [3], Bernardo [4], Degroot [5], and others throughout the decades after Shannon wrote his classic on information theory. In terms of what you ha…

It seems clear to me that the methods described in BBR and RMS entail maximizing information from the entire design, collection, and analysis.

It also seems clear that observational research generally has more noise in the channel, and must take more effort at increasing the information rate.

I don’t see why it isn’t possible to borrow the framework of information theory to evaluate the channel capacity of the various proposals, and then rank them in terms of information rate and ease of implementation.

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