The new discipline of ‘real-world data science’ has emerged over recent years. Expert
practitioners in this embryonic field are finding their feet, not just in terms of
role and remit, but also with regards to which questions they can answer and the methods
they can bring to bear [
[1]
]. But why are pharmaceutical companies employing real-world data scientists and seeking
real-world data opportunities on a scale not previously seen? The last two decades
saw a similar explosion in organisations with specialist expertise in the delivery
of randomised controlled trials (RCTs) and pharmacovigilance to the standards of licencing
authorities that include the Food and Drug Administration (FDA) and the European Medicines
Agency (EMA). But it is not these regulators that have driven the demand for real-world
data (RWD). Instead it is the appearance of the, so-called, ‘fourth hurdle’. It is
now necessary to demonstrate cost-effectiveness in addition to the traditional licencing
requirements of quality of manufacture, safety and efficacy prior to market access.
The clue to why RWD is an important component of any estimation of cost-effectiveness
is in the name: the definition of effectiveness, as opposed to efficacy, refers to
the measurement of effects in the real-world, rather than under the conditions of
experimentation required for the unbiased measurement of efficacy.To read this article in full you will need to make a payment
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References
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Article info
Publication history
Published online: May 15, 2017
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