This series is just growing as I think more and more about issues that come up when addressing research and how it applies to the progress towards a cure for cancer. If you are interested, you can access Part I, II, and III to get caught up. Just so we’re on the same page, there is no cure for all cancer. Yes, some types of cancer and subtypes of different cancers get to the point where no cancer cells can be detected and those people are considered “cured.” I think a more accurate description (albeit less hopeful) is the moniker, No Evidence of Disease (NED) or No Evidence of Active Disease (NEAD) for those of us with bone metastases. As far as I understand it, in 2022, the medical testing just doesn’t have the ability to truly with 100% accuracy know that no more cancer cells are active anywhere in the body at all.
But that isn’t exactly what I sat down to write about today.
The difference between causation and correlation is something that plagues so many of the articles and studies and discussions about cancer and, particularly, breast cancer. So much of the risk factors that correlate (not cause) with a diagnosis of breast cancer can be used to shame a patient or cause some incredible anxiety in patients. Let’s start with the dictionary definitions of these terms:
1. the action of causing or producing.
3. anything that produces an effect; cause.https://www.dictionary.com/browse/causation
1, mutual relation of two or more things, parts, etc.: Studies find a positive correlation between severity of illness and nutritional status of the patients.
3. Statistics. the degree to which two or more attributes or measurements on the same group of elements show a tendency to vary together.
4. Physiology. the interdependence or reciprocal relations of organs or functions.
5. Geology. the demonstrable equivalence, in age or lithology, of two or more stratigraphic units, as formations or members of such.https://www.dictionary.com/browse/correlation
At a basic level, causation describes a situation where x causes y whereas correlation simply notes that x and y are next to one another consistently. Here’s an example, there is some data that shows people with breast cancer have low vitamin D levels. Vitamin D doesn’t cause or prevent breast cancer, it is often depleted in people with breast cancer, but those two things occurring together doesn’t mean that Vitamin D levels equal a risk for or causation of breast cancer. There are so many other examples like weight or activity levels that are often correlated with illnesses and may lower a person’s immune system but aren’t the cause of a disease.
So, how do we differentiate between causation and correlation?
The scientific method tells us that we come up with a hypothesis and then we test it with trials that can be reliably replicated. This is how scientific knowledge is obtained and how research advances. A suggestion can be confirmed or debunked with data and asking the right questions. Where trials can break down is if the question is too simple or too complex or perhaps the trial doesn’t break out the data into information that is helpful or reliable. The bottom line is that there is a tested method for answering the question of causation without making assumptions.
Where I see this issue become toxic for patients is when the correlative data, such as weight or vitamin levels, become a source of patient shaming. Sure, there are things we can all do to reduce our risk for cancer, but even doing everything “right” isn’t a magic bullet. Many of us do the right things and still get cancer, even terminal cancer. We have to be careful to identify information as correlation or causation because that distinction makes a world of difference for the people dealing with the diagnosis already.
No one can change the past, just learn and do better.