We May Not Understand Control Groups

It’s well known that randomized trials are some of the most efficient ways to make causal inferences and to determine how much something (an intervention) differs from the comparator (some sort of control). Random assignment helps make these goals easier by minimizing selection bias and making the distribution of prognostic factors between groups random (not balanced). [1] Discussions (similar to the one above) praising the efficiency of randomized trials are widespread, however, few of these discussions take a close look at some of the common assumptions that individuals hold regarding randomized trials. Read More

The Bradford Hill Criteria Don't Hold Up

In 1965, the epidemiologist, Austin Bradford Hill, who helped link smoking to lung cancer, gave a speech where he presented his viewpoints/criteria on how we can arrive at causation from correlation. This lecture was a bit of a game changer at the time given that the tobacco industry was employing statisticians, medical doctors, and even popular science writers to push the idea that the relationship between smoking and lung cancer was merely a correlation, not a causal one. Read More

Exercise, Mental Health, and Big Data

Recently, a large cross-sectional study that investigated the relationship between exercise frequency and mental health was published in The Lancet Psychiatry and also happened to set Twitter on fire. I want to discuss the good and the not so good. First, some good! Some Good The study, which included 1,237,194 adults from the US, found a significant relationship between physical exercise and self-reported mental health burden. Here’s what the authors reported, Read More

Seductive Surrogates Can Be Deadly

In clinical trials, it’s not always possible to measure hard endpoints like cardiovascular disease events and cancer remission rates. Studies that use clinical outcomes often dichotomize these variables, and as a result, they need to have a large number of participants and be long in duration to detect differences between groups. Again, this type of research is expensive and not always feasible. In many scenarios, a more practical alternative is to focus on intermediate markers. Read More

How Useful Is Nutritional Epidemiology?

Nutritional epidemiological findings are often the studies that generate the most buzz, but they’re also the ones that get harshly criticized. Some folks will even go out of their way to say that the entire field produces findings that are mostly useless. Here’s what one of the leading meta researchers has to say about nutritional epidemiology: “Nutritional Epidemiology is a scandal. It should just go to the waste bin. Read More

Myth: Covariates Need to Be Balanced in RCTs

Before I get into the nitty-gritty, I want to remind everyone why we randomize in the first place. It’s to reduce selection bias and to get rid of systematic variation amongst groups, which allows us to come to more precise and efficient causal inferences. Many critics claim that we can’t make valid causal inferences if there’s an imbalance in covariates between the groups. Here’s an example. Say, hypothetically, we had two groups, and we wanted to see the effect of a statin on all-cause mortality and compare it to placebo. Read More

How Did We Figure Out Smoking Causes Lung Cancer?

Proving a cause and effect relationship isn’t easy. Causality is a complex subject, and there are thousands of texts on it, involving philosophical and mathematical arguments that are beyond my understanding. However, I do understand a bit of causality to discuss how we arrive at cause and effect relationships in the sciences. One of the first things often drilled into students in a research methods course is that correlation does not equal causation. Read More