Baseball analytics, arthritis, and the search for better health forecasts

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

It’s Fourth of July weekend in Seattle as I write this. Which means it’s overcast. This was predictable, just as it’s predictable that for the two months after July 4th the Pacific Northwest will be beautiful, sunny and warm. Mostly.

Too bad forecasting so many other things–baseball, earthquakes, health outcomes–isn’t nearly as easy. But that doesn’t mean people have given up. There’s a lot to be gained from better forecasting, even if the improvement is just by a little bit.

And so I was eager to see the results from a recent research competition in health forecasting. The challenge, which was organized as a crowdsourcing competition, was to find a classifier for whether and how rheumatoid arthritis (RA) patients will respond to a specific drug treatment. The winning methods are able to predict drug response to a degree significantly better than chance, which is a nice advance over previous research.

And imagine my surprise when I saw that the winning entries also have an algorithmic relationship to tools that have been used for forecasting baseball performance for years.

The best predictor was a first cousin of PECOTA. Continue reading

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The power law relationship in drug development

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

A few weeks ago a friend and I had the great opportunity to go see Nate Silver speak at the University of Washington. He’s a funny, engaging speaker, and for someone like me who makes his living generating and analyzing data, Silver’s work in sports, politics and other fields has been inspirational.  Much of his talk covered elements of his book, The Signal and the Noise, which I read over a year ago. It was good to get a refresher. One of the elements that particularly struck me this time around, to the point that I took a picture of his slide, was the concept of the power law and its empirical relationship to so many of the phenomena we deal with in life.

Nate Silver graph small

Figure 1: Slide from Nate Silver’s talk demonstrating the power law relationship in business–how often the last 20% of accuracy (or quality or sales or…) comes from the last 80% of effort.

Because I spend way too much time thinking about the business of drug development, I started thinking of how this concept applies to our industry and specifically the problem the industry is facing with creating innovative medicines.

Continue reading

The innovators dilemma in biopharma part 3. What would disruption look like?

All opinions are my own and do not necessarily reflect those of Novo Nordisk.

h/t to @Frank_S_David, @scientre, and the LinkedIn Group Big Ideas in Pharma Innovation and R&D Productivity for links and ideas

Part 1 is here.

Part 2 is here.

In the previous parts to this series I’ve covered both why the biopharma industry is ripe for disruption, and what the markets might be that could support a nascent, potentially disruptive technology until it matures enough to allow it to supplant the current dominant industry players.  In this final part I’d like to ask what disruption would look like and provide some examples of directions and companies that exemplify what are, to my mind, these sorts of disruptive technologies and approaches. With, I might add, the complete and utter knowledge that I’m wrong about who and what specifically will be disruptive! But in any case, before we can identify disruption, it’s worthwhile to ask what are the key elements of biopharma drug development that serve as real bottlenecks to affecting  human health, since these are the elements most likely to provide an avenue for disruption. Continue reading