---
title: "Converting Between Probabilities, Odds (Ratios), and Risk Ratios"
output: 
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    toc: true
    fig_width: 10.08
    fig_height: 6
tags: [r, effect size, rules of thumb, guidelines, conversion]
vignette: >
  \usepackage[utf8]{inputenc}
  %\VignetteIndexEntry{Converting Between Probabilities, Odds (Ratios), and Risk Ratios}
  %\VignetteEngine{knitr::rmarkdown}
editor_options: 
  chunk_output_type: console
bibliography: bibliography.bib
---

```{r message=FALSE, warning=FALSE, include=FALSE}
library(knitr)
options(knitr.kable.NA = "")
knitr::opts_chunk$set(comment = ">")
options(digits = 3)
```

The `effectsize` package contains function to convert among indices of effect
size. This can be useful for meta-analyses, or any comparison between different
types of statistical analyses.

# Converting Between *p* and Odds

Odds are the ratio between a probability and its complement:

$$
Odds = \frac{p}{1-p}
$$

$$
p = \frac{Odds}{Odds + 1}
$$
Say your bookies gives you the odds of Doutelle to win the horse race at 13:4, what is the probability Doutelle's will win?

Manually, we can compute $\frac{13}{13+4}=0.765$. Or we can

Odds of 13:4 can be expressed as $(13/4):(4/4)=3.25:1$, which we can convert:

```{r}
library(effectsize)

odds_to_probs(13 / 4)

# or
odds_to_probs(3.25)

# convert back
probs_to_odds(0.764)
```

Will you take that bet?

## Odds are *not* Odds Ratios


Note that in logistic regression, the non-intercept coefficients represent the (log) odds ratios, not the odds.

$$
OR = \frac{Odds_1}{Odds_2} = \frac{\frac{p_1}{1-p_1}}{\frac{p_2}{1-p_2}}
$$
The intercept, however, *does* represent the (log) odds, when all other variables are fixed at 0.

# Converting Between Odds Ratios, Risk Ratios and Absolute Risk Reduction

Odds ratio, although popular, are not very intuitive in their interpretations.
We don't often think about the chances of catching a disease in terms of *odds*,
instead we instead tend to think in terms of *probability* or some event - or
the *risk*. Talking about *risks* we can also talk about the *change in risk*,
either as a *risk ratio* (*RR*), or a(n *absolute) risk reduction* (ARR).

For example, if we find that for individual suffering from a migraine, for every
bowl of brussels sprouts they eat, their odds of reducing the migraine
increase by an $OR = 3.5$ over a period of an hour. So, should people eat
brussels sprouts to effectively reduce pain? Well, hard to say... Maybe if we
look at *RR* we'll get a clue.

We can convert between *OR* and *RR* for the following formula
[@grant2014converting]:

$$
RR = \frac{OR}{(1 - p0 + (p0 \times OR))}  
$$

Where $p0$ is the base-rate risk - the probability of the event without the
intervention (e.g., what is the probability of the migraine subsiding within an
hour without eating any brussels sprouts). If it the base-rate risk is, say,
85%, we get a *RR* of:

```{r}
OR <- 3.5
baserate <- 0.85

(RR <- oddsratio_to_riskratio(OR, baserate))
```

That is - for every bowl of brussels sprouts, we increase the chances of
reducing the migraine by a mere 12%! Is if worth it? Depends on you affinity to
brussels sprouts...

Similarly, we can look at ARR, which can be converted via

$$
ARR = RR \times p0 - p0
$$
```{r}
riskratio_to_arr(RR, baserate)
```

Or directly:

```{r}
oddsratio_to_arr(OR, baserate)
```

Note that the base-rate risk is crucial here. If instead of 85% it was only 4%,
then the *RR* would be:

```{r}
oddsratio_to_riskratio(OR, 0.04)
```

That is - for every bowl of brussels sprouts, we increase the chances of
reducing the migraine by a whopping 318%! Is if worth it? I guess that still
depends on your affinity to brussels sprouts...

# References