Category Archives: Metrics

Sequencing in polio, baseball pitching and cancer: sometimes the order of events matters

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This piece originally appeared in the Timmerman Report.

What do the polio virus, baseball pitch choice and cancer have in common?

The answer, of course, is sequencing. But not in the “figure out the DNA” way (although that’s involved). Instead in the “what comes first” way. Confused? Read on!

A big perk of Seattle is proximity to great institutions of biomedical research like the University of Washington and the Fred Hutchinson Cancer Research Center. Ever since my graduate student days in genetics at UC-Berkeley I’ve enjoyed going to seminars–especially seminars that are outside my field of study. Very little beats a good seminar for giving you a quick, condensed view of the state of a field of research. A bad seminar…well…we all could use more sleep, right?

In early October, Raul Andino of UCSF came to the Hutch to talk about his work on viral evolution. His team has been examining a clever real-world system to track the evolution of viruses. The near-eradication of polio (one of the great public heath victories of the past century) has led to the curious problem that as of the middle of this year most new cases of polio arose as a result of vaccination efforts. The live, attenuated vaccine that’s used in the developing world can, in very rare cases, mutate in just the wrong ways in its host, leading to the creation of a virulent strain that can infect others. In the US we use an inactivated polio vaccine which requires several injections; in much of the developing world the oral polio virus is preferred due to its ease of administration, lower cost, and immunization profile. The Andino lab realized that by studying these isolated outbreaks, which all originated with the same, genetically identical progenitor, they could test a hypothesis about the adaptive landscape of virulence evolution. Continue reading →

An Open Standard for APIs Could Lead us to Better Health

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There’s a parable about the elephant and the rider that’s been used by Chip and Dan Heath, and that originated with Jonathan Haidt, to describe how humans make decisions. A person’s mind can be thought of as consisting of a rider, representing the rational part of human thinking, and the elephant she’s riding, representing emotion. Both of these play a role in how a person decides things, and many of us believe the rider–the rational part–is in charge. The rider taps the elephant with her guide stick, and the elephant obediently moves in that general direction or does a specific task, like carrying lumber from place to place.

Except that’s not how a lot of decisions actually get made. Instead, the elephant sees a bunch of bananas, or a herd of other elephants, or a nice cool river to bathe in, and goes that way instead. And the rider…well, the rider can’t do much about it except, after the fact, rationalize how she always wanted to go in that direction to begin with. Yeah, it was time for a bath, sure

This framing has stuck in my mind for years and it’s a really helpful way of looking at many of the odd things that people do or say, ranging from climate change denial, to believing genetically modified organisms are inherently evil, to smoking despite everything we know about the harms that result, to even saying that Paul Blart, Mall Cop II is really, you know, not that bad–really. And it also speaks to one of the more vexing problems we have in human health. Why do people keep doing things they really probably shouldn’t, and know they shouldn’t, if they want to stay healthy?

I’ve touched before on how the power of digital tools can help make it easier for us to make good decisions. OPower is doing this for power consumption and conservation, and with the advent of tools like Apple’s Healthkit and the proliferation of activity trackers, the time is right to do this for health. Continue reading →

Making Change

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And now for something completely different! Short fiction in honor of the recent unveiling of the Apple iWatch and Healthkit.

“I wouldn’t eat that if I were you.”

Sylvia paused, bacon cheeseburger halfway to her mouth, and peered at the neon green band wrapped around her wrist. The wraparound touchscreen was currently showing a cat emoji. It had a frowny face, expression halfway between puzzlement and alarm.

“What did you say?”

“I’m just saying,” said her Best Buddy wristband, “that when we met a few weeks ago, you mentioned wanting to keep your weight in a specific range.” The emoji shrugged. “Little friendly reminder. You know?”

Sylvia carefully put the burger back down and resisted the urge to lick grease off her fingers. She fumbled for her napkin, her fingers leaving translucent streaks on the thin, white paper.

“I–well, yeah. But, I mean, you’ve never said anything like this before like when–” She broke off, remembering the milkshake, the onion rings, the King-size Choconut bar…

“Well it’s not the first thing you do, is it? When you meet someone and you’re just getting to know them?” The cat had morphed into a light pink, animated mouse, standing on its hind legs, bashfully kicking one leg. “But now, we’re friends!” Continue reading →

Baseball, Bayes, Fisher and the problem of the well-trained mind

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One of the neat things about the people in the baseball research community is how willing many of them are to continually question the status quo. Maybe it’s because sabermetrics is itself a relatively new field, and so there’s a humility there. Assumptions always, always need to be questioned.

Case in point: a great post by Ken Arneson entitled “10 things I believe about baseball without evidence.” He uses the latest failure of the Oakland A’s in the recent MLB playoffs to highlight areas of baseball we still don’t understand, and for which we may not even be asking the right questions. Why, for example, haven’t the A’s advanced to the World Series for decades despite fielding good and often great teams? Yes there’s luck and randomness, but at some point the weight of the evidence encourages you to take a second look. Otherwise, you become as dogmatic as those who still point to RBIs as the measure of the quality of a baseball batter. Which they are not.

One of the thought-provoking things Arneson brings up is the question of whether the tools we use shape the way we study phenomena–really, the way we think–and therefore unconsciously limit the kinds of questions we choose to ask. His example is the use of SQL in creating queries and the inherent assumptions of that datatype that objects within a SQL database are individual events with no precedence or dependence upon others. And yet, as he points out, the act of hitting a baseball is an ongoing dialog between pitcher and batter. Prior events, we believe, have a strong influence on the outcome. Arneson draws an analogy to linguistic relativity, the hypothesis that the language a person speaks influences aspects of her cognition.

So let me examine this concept in the context of another area of inquiry–biological research–and ask whether something similar might be affecting (and limiting) the kinds of experiments we do and the questions we ask.

Continue reading →