Unleashed by bias and chance

Margarita Skopeliti
6 min readMay 29, 2020

When I entered research, among the first things to be taught was that when studying the effect of an intervention, e.g. a drug, bias is masking its “true” effect; to a beginner’s eye, this was picturing bias as something bad to allow to have in an experiment and that we had to eliminate it.

Later, of course I understood that not all sort of bias is equally bad. Some can be easily identified and predicted in advance. This kind of bias, such as selection bias, is important and contributing heavily to the outcome and needs to be excluded or minimized close zero during an experiment. Other forms of bias, it is alright to just have them minimized to an extent possible, such as information bias. Some form of bias while known, anticipated or expected, is unavoidable and we just need to cope with during experiments. Last it is also very likely that there is a type of bias neglected, overlooked or even not thought at all. This doesn’t stop experiments to be done or published.

Overall, to reach having truly meaningful and sound results around the effect of an action or factor, bias needs to be minimized, measured, taken under consideration, handled somehow, and this is the right thing to do during measurement and analysis of the results. But when we take an experimental effect and release it to the real-world, there is no experiment anymore. In this setting, it is widely expected, anticipated and accepted that the interventions perform somehow less than under the ideal experimental conditions. This has been observed in many cases for drugs when they move from clinical trials to prescription and the later still feeds our admiration, appreciation and research focus to clinical trials compared to real life data.

Adding to the value of clinical trial data, the collection and analysis of real-world data is rising extremely fast and in a quality way from many pharmaceutical companies and has already started shaping the drug approval procedures by FDA and EMA. We want to see that a new factor or action drives the change in a result, and this is noble. But are we truly seeing, paying attention to all insights the real-life setting is providing?

· How are we looking over the results at real-life?

· From what point of view are we making our observations?

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Margarita Skopeliti

In clinical research in the morning. In clarity research afterwards. Love reading and writing while drinking coffee. Grateful for your ko-fi.com/bymargarita