Tag: study design

Red Pill or Blue Pill – Randomised Controlled Trials

Posted on by Prav

Remember that iconic scene in the matrix where Morpheus asks Neo to choose the red pill or the blue pill? That’s how I like to think of RCT’s.

Basically the researcher is “Morpheus” and the study subject is “Neo”, but instead of just 1 Neo choosing the red/blue pill we have a few. Each Neo is randomly assigned either the red or blue pill. Morpheus then sits back and watches what happens to the Neo’s and we see by the end of it who is more content – the Neo living life in blissful ignorance in the Matrix or the one steeped in reality.

Which is basically what RCT’s do. They assess how different interventions/treatments affect two different groups by following up participants. When done well, it provides the best evidence on the cause and effect relationship by studying it in real time. (temporal sequence)

There is an experimental group given the intervention/treatment (Neo leaving the Matrix) and the comparison group (controls) that gets a placebo or the current conventional treatment (Neo in the Matrix). They are then followed up to assess how effective the new intervention/treatment is in comparison to the current standard/placebo group. (Is Neo more content out of the Matrix or in?)

However unlike Neo in the Matrix, there is no choice involved and the individuals involved are randomly assigned.

Why? To avoid any selection bias by the testers choosing individuals who may have better outcomes and to ensure that both groups are as similar as possible therefore being able to distribute the confounding factors.

What are confounding factors?
It is basically any other variable that can affect your dependent variable.

For example, if we are doing a study on a sedentary lifestyle causing weight gain. The independent variable here is the sedentary lifestyle while the dependent variable is weight gain. And the confounding factors can be anything from stress, diet, food portions, genetics and metabolism – all of which can affect weight gain (dependent variable). The hope is that by randomising it, we will get an equal amount of these other confounding factors in every group therefore giving us a clearer picture on the intervention being studied.

Despite being the best method, RCT’s still has some short-comings. They tend to be expensive, time consuming and you could argue that if a person is in an RCT – they are probably more likely to be compliant therefore not really giving us a real world experience.

Study design – Observational

Posted on by Prav

One of the most important facets of public health is understanding epidemiological studies. I might also add that it is the one thing I repeatedly have to look over and a lot of my peers and colleagues find confusing and struggle with.

A quick recap of what epidemiology is – Put simply, it deals with figuring out the who, what, when, where, why and how certain diseases happen and telling us the way we can overcome/stop it.

Now epidemiology is actually made up of both Observational and Interventional studies, and to start we will talk about observational studies.

They are quite self-explanatory, as they are just that – observational. All we are doing is essentially just having an overall look. However, there are different ways to do this, and that is by being either descriptive or analytical.

So what are the main differences between these two types of research/studies we can carry out and what do we gain from it? I prefer the 5W’s and 1 H method of thinking about it.

Descriptive Analytical
Who?
What?
When?
Where?		 Why?
How?

So descriptive studies, look at an overall picture. It tells us what is going on, what is involved, who is involved, where it is happening and when without telling us Why or How. We basically go in without an idea for cause and effect. It essentially helps us identify them by examining patterns and by giving us an overall idea of the population, the distribution of health based on age, gender, location and time/over a period of time. It is from this, that we might identify a problem leading to ideas for new studies to figure out the why, how and perhaps even move on to an interventional study.

If let’s say I was selling chocolate and I wanted to know more about my customers, my initial descriptive study would tell me about the people who are buying my chocolates, where they live etc. I might learn, that only hipsters in their late 20’s buy my brand of chocolate but I still do not know why (well maybe because it’s unheard of?). My next task is to figure out why and perhaps how I can make my chocolate more appealing to different groups.

Examples of descriptive studies can be further broken down to cross-sectional study=ies like a health survey, ecological studies or even case reports/case series. Remember all it does, is present the facts for what they are and is a starting point for us to make associations and come up with new ideas.

Analytical studies then basically go into how this is happening and why? It is one of the ways to investigate causal relationships. So in these studies, I have a hypothesis/an idea. A health-related example would be that ‘smokers have a higher risk of lung cancer than non-smokers. We then investigate if this is true or not. How we go about this, is either with a case-control study, cohort study, cross-sectional study or an ecological study.

Something visual to help, the rest will be revealed as we move on.

How come cross-sectional and ecological studies are in both descriptive and analytical studies? Well, I will go into that when I talk more about the different types of studies mentioned in the next post.