The world has always been driven by science, and as we become more aware of that world through the increasingly easy access to information, science’s role in our collective transformation has increased exponentially. There are obviously different types of scientific research, and specifically, different types of people behind that research. One type that is incredibly significant in virtually all walks of life is industry-led research. The question is, can industry-led research help to unlock science’s potential? We spoke with Andrea Costantini, PMI’s Head of Scientific Engagement for Latin America and Canada, to get her views on the topic.
PMI’s journey towards a smoke-free future is an intricate one, not least because there are those who refuse to believe our science is sound, despite our commitment to projects like INTERVALS that increase scientific transparency. Many people mistakenly believe that because science is industry-led it follows that it must therefore be biased. When we asked her about this tendency, Andrea said, “If you use robust and transparent science, unbiased science, you are being unbiased as a company. By publishing all the results of your research, it doesn’t matter if it is positive or negative, you are being unbiased. There is a commitment. Many companies have the commitment to publish everything, irrespective of whether it’s positive or negative.”
It’s actually perfectly fair and perhaps wise to be suspicious; industry-led or otherwise, biases often do creep into scientific studies. This can be caused by a multitude of different factors, and ensuring that bias is avoided in science is no easy feat. “Something which is a challenge in research,” Andrea tells us, “is to have population large enough to represent what is happening in the real world. For sure, the idea would be to have the chance to test the new alternative… on everybody, but that is impossible… So what do you do? Whenever you are doing clinical research, which is to conduct research in human beings, you select a sample. But it is very important to ensure as much as possible that that sample is as representative as possible of the whole population.” Unfortunately, sampling is not the only thing that can make a study biased. Another way in which a study can potentially become biased is through confounder factors. Essentially, this happens when there is a question of whether the result of a study had to do with the testing or the manner of it. As Andrea explains, “For example, if I am evaluating a drug to decrease the blood pressure, and I have people with high blood cholesterol, and I am measuring the number of myocardial infarctions, it could be that maybe I do not see an effect of the drug in decreasing the number of myocardial infarctions. That is not because the drug is not working, but because I have another risk factor present there. That is a confounder factor.”