“Alice asked the Cheshire Cat, who was sitting in a tree, ‘What road do I take?’ The cat asked, ‘Where do you want to go?’ ‘I don’t know,’ Alice answered. ‘Then,’ said the cat, ‘it really doesn’t matter, does it?’” – Lewis Carroll, Alice’s Adventures in Wonderland
This quote encapsulates the importance of clearly defining our goals and our vision of success as Statisticians. If we don’t chart a path to high performance by defining what good looks like, it’s impossible to achieve it. Yet, in my frequent discussions with industry Statisticians of all levels I have found varied perceptions of ‘what good looks like’ for Launch and Commercialisation statistics.
What explains these different views? Well, it’s clear that as Statisticians we are no more immune to bias than the average person. In fact, our vision of good practice may depend heavily on our location, the location of our company’s headquarters and our personal experiences during our career.
For example, UK based Statisticians working in the launch and commercialisation setting will tend to focus on health economics and prioritize NICE HTA submissions. Similarly, those working for companies with a strong German presence tend to major on the German HTA dossier and have little involvement in other markets.
It’s also true that Launch and Commercialisation work looks different depending on the indication, or whether it is treated by general practitioners or by specialists. Statisticians with the bulk of their experience in a single area may inadvertently carry a biased understanding of the overall picture.
Organizational background also plays a major role in our understanding of Launch and Commercialisation. Those who only have global experience may struggle to understand the practical needs of local affiliates. Likewise, Statisticians who have only worked in a local organisation may struggle to consider the bigger picture and the constraints of launching a new product globally.
The dynamics between local and global organisations within companies often affect the work of Statisticians too. In worst case examples, there may be no collaboration at all between local and global Statisticians or local work is completely outsourced without any statistical involvement from within the company.
With so much variability of experience, it’s therefore critical that we remain aware of our own biases and cultivate organisational empathy as we work towards Launch and Commercialisation success.
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