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Dr Steve Pearson on Data Needed to Evaluate Clinical and Economic Effects of New Therapies

Ideally, researchers determining the clinical and economic effects of a new treatment would have both short-term and long-term data, explained Steve Pearson, MD, MSc, president of the Institute for Clinical and Economic Review. If not all of this data is available, however, they may have to use surrogate outcomes or perform indirect comparisons.


Ideally, researchers determining the clinical and economic effects of a new treatment would have both short-term and long-term data, explained Steve Pearson, MD, MSc, president of the Institute for Clinical and Economic Review. If not all of this data is available, however, they may have to use surrogate outcomes or perform indirect comparisons.

Transcript (slightly modified)

What forms of evidence are ideal to balance long-term and short-term pros and cons of a new therapy?

In a sense, the ideal evidence that we would have would allow us to understand well what the short-term clinical benefits are and be able to fairly accurately project those into the future. That’s always going to involve some degree of uncertainty, though, and everybody’s aware of that.

We have to try to understand that we are looking at the shorter-term effects from the clinical trials and we want to make sure that we don’t overly focus on what we know about the short-term, because in many cases drugs will really show their greater value in the long-term by improving patient outcomes or preventing adverse events years and years downstream, and that has both clinical effects as well as economic effects.

Good data to us allows us to understand very clearly which patients are being treated in the short-term and how that generalizes to broader patient populations. It allows us to understand very clearly, even if we have to do an indirect comparison, how different treatment options line up. Then, hopefully, we have some longer-term data to show that the clinical effects and the economic effects continue through time, so that we can build a model that has fewer assumptions about long-term effects, and more data based on real studies.

 
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