One way to improve clinical trial reporting: a Yelp-style rating system

This piece originally appeared in the Timmerman Report.

STAT recently published an in-depth report about the many research centers that don’t bother to publicly disclose the results of their clinical trials, even though they are required to do so. This follows on a New England Journal of Medicine article back in March that had a similar analysis of the lack of reporting and publication of clinical trial data to clinicaltrials.gov.

Most observers of biomedical research would agree that getting clinical trial data out about what happened in a trial is pretty important, whether the trial succeeded or failed. After all, biomedical translational research is most meaningful when done on human subjects and negative information can be quite informative and useful. Animal models are nice, but translation of results from animals to humans is a spotty proposition at best. We need to know what’s working, and what’s not, to know how to best allocate our research resources and how to treat patients.

The lack of reporting is an embarrassment for research. It’s also understandable, because so far the FDA hasn’t used its authority to punish anyone for delayed reporting. Nobody appears to have lost any research funding because they failed to post trial results in a timely manner. Universities told STAT their researchers were “too busy,” given other constraints on their time, to report their results. So what really seems to be going on is that reporting is prioritized below most other activities in clinical research.

It was interesting and eye-opening that industry fared better than academia in both the STAT story and the NEJM article with respect to how many studies have been reported. Having seen the industry process first-hand, I’d speculate that (at least for positive trials) there’s a much stronger incentive to get data out in public. Successful trial results can create buzz among clinicians and patients, revving up trial enrollment which can then help get a new drug on the market faster, and convince people to use it when it’s available. It may be that in academia the effort of getting trial results in the required format for clinicaltrials.gov is perceived as too much work, relative to the rewards. Academics are naturally going to spend more energy on directly rewarded activities like writing grant proposals and writing peer-reviewed scientific publications that help them win even more grants, promotions, and other accolades. Well okay. If this is the case, then figuring out new incentives may be key.

So what would work? Anyone who participates in a clinical trial is providing time, may be subject to risks and often is asked to provide samples that are biobanked to support future exploratory and translational research. It’s like when people donate to food banks. I’m pretty sure they mean that food to be eaten and not to sit on a shelf. These participants in clinical trials deserve to have their volunteerism rewarded.

This got me thinking about how to empower patients to get more of what they want. Patient-centered research is a buzzword these days, and for good reason. Patients have at times been an afterthought in the biomedical research enterprise. I thought of services like Yelp and Uber and Angie’s List and other peer-to-peer systems that allow users to get information, provide feedback and give ratings to specific providers. And I wondered: could this be a way to apply pressure to clinical trial researchers to improve their reporting? Continue reading

Should Basic Lab Experiments Be Blinded to Chip Away at the Reproducibility Problem?

An earlier version of this piece appeared on the Timmerman Report.

Note added 23Feb2016: Also realized that I was highly influenced by Regina Nuzzo’s piece on biases in scientific research (and solutions) in Nature, which has been nicely translated to comic form here.

Some people believe biology is facing a “Reproducibility Crisis.” Reports out of industry and academia have pointed to difficulty in replicating published experiments, and scholars of science have even suggested it may be expected that a majority of published studies might not be true. Even if you don’t think the lack of study replication has risen to the crisis point, what is clear is that lots of experiments and analyses in the literature are hard or sometimes impossible to repeat. I tend to take the view that in general people try their best and that biology is just inherently messy, with lots of variables we can’t control for because we don’t even know they exist. Or, we perform experiments that have been so carefully calibrated for a specific environment that they’re successful only in that time and place, and sometimes even just with that set of hands. Not to mention, on top of that, possible holes in how we train scientists, external pressures to publish or perish, and ever-changing technology.

Still, to keep biomedical research pushing ahead, we need to think about how to bring greater experimental consistency and rigor to the scientific enterprise. A number of people have made thoughtful proposals. Some have called for a clearer and much more rewarding pathway for reporting negative results. Others have created replication consortia to attempt confirmation of key experiments in an orderly and efficient way. I’m impressed by the folks at Retraction Watch and PubPeer who, respectively, call attention to retracted work, and provide a forum for commenting on published work. That encourages rigorous, continual review of the published literature. The idea that publication doesn’t immunize research from further scrutiny appeals to me. Still others have called for teaching scientists how to use statistics with greater skill and appropriateness and nuance. To paraphrase Inigo Montoya in The Princess Bride, “You keep using a p-value cutoff of 0.05. I do not think it means what you think it means.”

To these ideas, I’d like to throw out another thought rooted in behavioral economics and our growing understanding of cognitive biases. Would it help basic research take a lesson from clinical trials and introduce blinding in our experiments? Continue reading

Baseball, regression to the mean, and avoiding potential clinical trial biases

This post originally appeared on The Timmerman Report. You should check out the TR.

It’s baseball season. Which means it’s fantasy baseball season. Which means I have to keep reminding myself that, even though it’s already been a month and a half, that’s still a pretty short time in the long rhythm of the season and every performance has to be viewed with skepticism. Ryan Zimmerman sporting a 0.293 On Base Percentage (OBP)? He’s not likely to end up there. On the other hand, Jake Odorizzi with an Earned Run Average (ERA) less than 2.10? He’s good, but not that good. I try to avoid making trades in the first few months (although with several players on my team on the Disabled List, I may have to break my own rule) because I know that in small samples, big fluctuations in statistical performance in the end  are not really telling us much about actual player talent.

One of the big lessons I’ve learned from following baseball and the revolution in sports analytics is that one of the most powerful forces in player performance is regression to the mean. This is the tendency for most outliers, over the course of repeated measurements, to move toward the mean of both individual and population-wide performance levels. There’s nothing magical, just simple statistical truth.

And as I lift my head up from ESPN sports and look around, I’ve started to wonder if regression to the mean might be affecting another interest of mine, and not for the better. I wonder if a lack of understanding of regression to the mean might be a problem in our search for ways to reach better health.
Continue reading

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

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

Is Opower the model for getting us to wellness and health?

This is a post about nudges. And optimism.

There’s a story I read a long time ago by David Brin. It’s called “The Giving Plague,” and the protagonist is a virologist and epidemiologist who describes his life working on viruses and vectors. The Plague of the title is a virus that has evolved the ability to make infected people enjoy donating blood. Recipients keep giving blood, leading to an exponentially expanding network of people who find themselves giving blood regularly and even circumventing age and other restrictions to make sure they can give their pint every eight weeks.

The central twist of the story is that the protagonist’s mentor, who discovers this virus, realizes people who donate blood also perform other altruistic acts–that the act of giving blood changes their own self image. Makes them behave as better people. And so he suppresses the discovery, for the greater good of society. The protagonist, a rampant careerist, begins plotting murder to allow him to take credit. But before he can act, more diseases strike, the Giving Plague moves through the population, and the protagonist forgets about it in his efforts to cure newer diseases.

And if anyone thinks something like this is too outlandish, I encourage you to read this piece about Toxoplasma gondii and how it makes infected mice charge at cats, the better to be eaten so that T. gondii can spread. Yeah.

But what does this story have to do with the future of wellness and health?

Continue reading