Simpson's Paradox: extreme statistical confounding

 

Preamble

 

Simpson's Paradox is an extreme example of the effects of statistical confounding, which I discussed in last week's blog post, "Statistical Confounding: why it matters".

Simpson's Paradox can occur when an apparent association between two variables $X$ and $Y$ is affected by the presence of a confounding variable, $Z$. In Simpson's Paradox, the confounding is so extreme that the  association between $X$ and $Y$ actually disappears or reverses itself after conditioning on the confounder $Z$. 

Simpson's paradox can occur in count data or in continuous data. In this post, I'll talk about how to visualize Simpson's paradox for count data, and how to understand it as an example of statistical confounding.

It isn't actually a paradox; it makes complete sense, once you understand what's going on. It's just that it's not what our intuition tells us should happen. And whether it's 'wrong' depends on what goal you're shooting for. In the example below, if you want to make a choice for yourself based on understanding the relative effectiveness of the two treatments, you'd be best off choosing Treatment A. But if your goal is prediction -- who is likely to do better, a random patient who gets Treatment A or Treatment B? -- you're best off with Treatment B.

 

If that confuses you, keep reading.  

 

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Here's the entire 'statistical confounding' series:

- Part 1: Statistical confounding: why it matters: on the many ways that confounding affects statistical analyses 

- Part 2: Simpson's Paradox: extreme statistical confounding (this post): understanding how statistical confounding can cause you to draw exactly the wrong conclusion

- Part 3: Linear regression is trickier than you think: a discussion of multivariate linear regression models

- Part 4: A gentle introduction to causal diagrams: a causal analysis of fake data relating COVID-19 incidence to wearing protective goggles

- Part 5: How to eliminate confounding in multivariate regression: how to do a causal analysis to eliminate confounding in your regression analyses   

-Part 6: A simple example of omitted variable bias: an example of statistical confounding that can't be fixed, using only 4 variables.

 

A famous example: kidney stone treatments

Here is a famous example of Simpson's Paradox occurring in nature, in a medical study comparing the efficacy of kidney stone treatments (here's a link to the original study).

In this example, we are comparing two treatments for kidney stones. The data show that, over all patients, Treatment B is successful in 83% of cases, and Treatment A is successful in only 78% of cases.

However, if we consider only patients with large kidney stones, then Treatment A is successful in 73% of cases, whereas Treatment B is successful in only 69% of cases.

And if we consider only patients with small kidney stones, the Treatment A is successful in 93% of cases, where Treatment B is successful in only 87% of cases.

Suppose you're a kidney stone patient. Which treatment would you prefer? Since I'd presumably have either a small kidney stone or a large one, and Treatment A works better for either one, I'd prefer Treatment A. But looking at all patients overall, this result says Treatment B is better. Does this mean that if I don't know what size kidney stone I have, I should prefer Treatment B? (No). Why is this happening?

This is happening because the small-vs-large-kidney stone factor is a confounding variable, as discussed in this post on statistical confounding from last week.

The diagram below shows the causal relationships among three variables applying to every kidney stone patient. Either Treatment A or B is selected for the patient. Either the treatment is either considered successful, or it isn't. And the confounding variable is in red: either the patient has a large kidney stone, or they do not.

The size of the kidney stone, reasonably, has an impact on how successful the treatment is; similarly, we're assuming the treatment choice affects the success of the treatment. 

But here's the confounding factor: the stone size, in red, also affects the choice of treatment for the patient. Treatment A is more invasive (it's surgical), and so it's more likely than Treatment B to be applied to severe cases with larger kidney stones. Conversely, Treatment B is more likely to be applied to smaller kidney stone cases, which are lower risk to begin with. Since the size of the kidney stone is influencing the choice of Treatments A vs. B, the causal diagram has an arrow from the size variable to the Treatment variable. And this is the 'back door', from the stone size variable into the Treatment choice variable, that is causing the confounding.

To see what is actually happening, look at the total numbers of patients in each of the four kidney stone subgroups:

• Treatment A, large stones: 263
• Treatment A, small stones: 87
• Treatment B, large stones: 80
• Treatment B, small stones: 270
  

Clearly the size of the stone is impacting the treatment choice. 

But stone size is also a huge predictor for treatment success: the larger the stone size, the harder it is for any treatment to succeed. So a higher proportion of small stone, Treatment B cases succeed than of large stone, Treatment A cases. And that's what's causing Simpson's Paradox.

Visualizing Simpson's Paradox for count data

 

Suppose we're running an experiment to assess the effect of a variable $x$ on a 'coin flip' variable $Y$. Each time we flip the coin, we'll call that a trial T. Each time $Y$ comes up heads, we'll call that a success S. The graph above has T on the x axis, and S on the y axis. Many experiments are modeled this way. In the kidney stone example, the variable $x$ refers to the choice of treatment, and the variable $Y$ refers to whether it had a successful outcome.

During data analysis, we'll break down the total sample of kidney stone patients into subgroups by whether they got Treatment A or B. We can break it down further in any way we choose; for example, we can subset the data by age, by gender, or by both at once. Or we can further subset the patients based on whether they had a large kidney stone. This subsetting will result in groups which we'll denote by $g$. 

We can visualize subgroup $g$'s experimental results by placing it in the graph as a vector $\vec{g}$ from the origin to the point $(S_g,T_g)$, where $T_g$ is the number of patients in the group, and $S_g$ is the number of patients in the group with successful outcomes. 

 

The slope of $\vec{g}$ is $S_g/T_g$, so the slopes of the vectors therefore indicate the success rate within each subgroup (note that the slopes of these vectors can never be larger than 1, since you can't have more successes than trials). When you compare the success rates between groups in an experiment, you only need to look at the slopes of these vectors -- the sizes of the subgroups are not visible to you. But it's the disparities in subgroup sizes that cause Simpson's paradox to occur. 

 

The lengths of the vectors are a rough indicator of how many patients there were within each subgroup; the larger the number of patients in the group, $T_g$, the longer $\vec{g}$ will be.

In the diagram above, we see that the subgroups Treatment A for small stones, and Treatment B for large stones, were much smaller in length than the other two (because there were fewer trials in those subgroups). But their lengths do not matter when considering the per-group success rates $S_g/T_g$; all that matters is their slopes. Treatment A's slope for small stones is higher than Treatment B's slope for small stones; the same holds the large stone groups. So within each subgroup, Treatment A is more successful.

But if we restrict our attention to the two longest vectors in the middle, we can see that the Treatment B, small stones vector has a higher slope than the Treatment A, large stones vector. This is mainly due to the fact that people with large kidney stones generally have worse outcomes, regardless of how they are treated.

In the diagram below, we are looking at the resulting vectors when all the Treatment A and B patients are grouped together, regardless of stone size. 

We get the vector corresponding to the combined group in Treatment A by summing the two green Treatment A vectors. Similarly, we sum the two black Treatment B vectors to get the aggregated Treatment B vector. When we do this, we can see that the Treatment B vector has the higher slope. 

 

This happens because, when we add the green vectors together to get the total Treatment A vector, the result is only slightly different from the much longer Treatment A, small stones group vector. Similarly, the summed vector for Treatment B is only slightly different from the much longer Treatment B, large stones group vector. 

 

As a result, the combined Treatment A vector has a lower slope than the combined Treatment B vector, making it look less effective overall. This is Simpson's Paradox in visual form. 

Simpson's Paradox reversals don't occur often in nature, though there are a few examples (like this one). But subtler forms of statistical confounding definitely do occur, all the time, in settings where they affect the conclusions of observational studies.