Showing posts with label Big Data. Show all posts
Showing posts with label Big Data. Show all posts

Tuesday, March 18, 2014

These Words Have (Temporarily) Relocated

Near the end of last year, I had the bright idea of starting a second blog, Placebo Lead-In, to capture a lot of smaller items that I found interesting but wasn't going to work up into a full-blown, 1000 word post.

According to Murphy’s Law, or the Law of Unintended Consequences, or the Law of Biting Off More Than You Can Chew, or some such similar iron rule of the universe, what happened next should have been predictable.

First, my team at CAHG Trials launched a new blog, First Patient In. FPI is dedicated to an open discussion of patient recruitment ideas, and I’m extremely proud of what we've published so far.

Next, I was invited to be a guest blogger for the upcoming Partnerships in Clinical Trials Conference.

Suddenly, I've gone from 1 blog to 4. And while my writing output appears to have increased, it definitely hasn't quadrupled. So this blog has been quiet for a bit too long as a result.

The good news is that the situation is temporary - Partnerships will actually happen at the end of this month. (If you’re going: drop me a line and let’s meet. If you’re not: you really should come and join us!) My contributions to FPI will settle into a monthly post, as I have a fascinating and clever team to handle most of the content.

In case you've missed it, then, here is a brief summary of my posts elsewhere over the past 2 months.

First Patient In


Partnerships in Clinical Trials



Please take a look, and I will see you back here soon.

[Photo credit: detour sign via Flikr user crossley]

Thursday, December 19, 2013

Patient Recruitment: Taking the Low Road

The Wall Street Journal has an interesting article on the use of “Big Data” to identify and solicit potential clinical trial participants. The premise is that large consumer data aggregators like Experian can target patients with certain diseases through correlations with non-health behavior. Examples given include “a preference for jazz” being associated with arthritis and “shopping online for clothes” being an indicator of obesity.
We've seen this story before.

In this way, allegedly, clinical trial patient recruitment companies can more narrowly target their solicitations* for patients to enroll in clinical trials.

In the spirit of full disclosure, I should mention that I was interviewed by the reporter of this article, although I am not quoted. My comments generally ran along three lines, none of which really fit in with the main storyline of the article:

  1. I am highly skeptical that these analyses are actually effective at locating patients
  2. These methods aren't really new – they’re the same tactics that direct marketers have been using for years
  3. Most importantly, the clinical trials community can – and should – be moving towards open and collaborative patient engagement. Relying on tactics like consumer data snooping and telemarketing is an enormous step backwards.

The first point is this: certainly some diseases have correlates in the real world, but these correlates tend to be pretty weak, and are therefore unreliable predictors of disease. Maybe it’s true that those struggling with obesity tend to buy more clothes online (I don’t know if it’s true or not – honestly it sounds a bit more like an association built on easy stereotypes than on hard data). But many obese people will not shop online (they will want to be sure the clothes actually fit), and vast numbers of people with low or average BMIs will shop for clothes online.  So the consumer data will tend to have very low predictive value. The claims that liking jazz and owning cats are predictive of having arthritis are even more tenuous. These correlates are going to be several times weaker than basic demographic information like age and gender. And for more complex conditions, these associations fall apart.

Marketers claim to solve this by factoring a complex web of associations through a magical black box – th WSJ article mentions that they “applied a computed algorithm” to flag patients. Having seen behind the curtain on a few of these magic algorithms, I can confidently say that they are underwhelming in their sophistication. Hand-wavy references to Big Data and Algorithms are just the tools used to impress pharma clients. (The down side to that, of course, is that you can’t help but come across as big brotherish – see this coverage from Forbes for a taste of what happens when people accept these claims uncritically.)

But the effectiveness of these data slice-n-dicing activities is perhaps beside the point. They are really just a thin cover for old-fashioned boiler room tactics: direct mail and telemarketing. When I got my first introduction to direct marketing in the 90’s, it was the exact same program – get lead lists from big companies like Experian, then aggressively mail and call until you get a response.

The limited effectiveness and old-school aggressiveness of these programs comes is nicely illustrated in the article by one person’s experience:
Larna Godsey, of Wichita, Kan., says she received a dozen phone calls about a diabetes drug study over the past year from a company that didn't identify itself. Ms. Godsey, 63, doesn't suffer from the disease, but she has researched it on the Internet and donated to diabetes-related causes. "I don't know if it's just a coincidence or if they're somehow getting my information," says Ms. Godsey, who filed a complaint with the FTC this year.
The article notes that one recruitment company, Acurian, has been the subject of over 500 FTC complaints regarding its tactics. It’s clear that Big Data is just the latest buzzword lipstick on the telemarketing pig. And that’s the real shame of it.

We have arrived at an unprecedented opportunity for patients, researchers, and private industry to come together and discuss, as equals, research priorities and goals. Online patient communities like Inspire and PatientsLikeMe have created new mechanisms to share clinical trial opportunities and even create new studies. Dedicated disease advocates have jumped right into the world of clinical research, with groups like the Cystic Fibrosis Foundation and Michael J. Fox Foundation no longer content with raising research funds, but actively leading the design and operations of new studies.

Some – not yet enough – pharmaceutical companies have embraced the opportunity to work more openly and honestly with patient groups. The scandal of stories like this is not the Wizard of Oz histrionics of secret computer algorithms, but that we as an industry continue to take the low road and resort to questionable boiler room tactics.

It’s past time for the entire patient recruitment industry to drop the sleaze and move into the 21st century. I would hope that patient groups and researchers will come together as well to vigorously oppose these kinds of tactics when they encounter them.

(*According to the article, Acurian "has said that calls related to medical studies aren't advertisements as defined by law," so we can agree to call them "solicitations".)

Monday, January 14, 2013

Magical Thinking in Clinical Trial Enrollment


The many flavors of wish-based patient recruitment.

[Hopefully-obvious disclosure: I work in the field of clinical trial enrollment.]

When I'm discussing and recommending patient recruitment strategies with prospective clients, there is only one serious competitor I'm working against. I do not tailor my presentations in reaction to what other Patient Recruitment Organizations are saying, because they're not usually the thing that causes me the most problems. In almost all cases, when we lose out on a new study opportunity, we have lost to one opponent:

Need patients? Just add water!
Magical thinking.

Magical thinking comes in many forms, but in clinical trial enrollment it traditionally has two dominant flavors:

  • We won’t have any problems with enrollment because we have made it a priority within our organization.
    (This translates to: "we want it to happen, therefore it has to happen, therefore it will happen", but it doesn't sound quite as convincing that way, does it?)
  • We have selected sites that already have access to a large number of the patients we need.
    (I hear this pretty much 100% of the time. Even from people who understand that every trial is different and that past site performance is simply not a great predictor of future performance.)

A new form of magical thinking burst onto the scene a few years ago: the belief that the Internet will enable us to target and engage exactly the right patients. Specifically, some teams (aided by the, shall we say, less-than-completely-totally-true claims of "expert" vendors) began to believe that the web’s great capacity to narrowly target specific people – through Google search advertising, online patient communities, and general social media activities – would prove more than enough to deliver large numbers of trial participants. And deliver them fast and cheap to boot. Sadly evidence has already started to emerge about the Internet’s failure to be a panacea for slow enrollment. As I and others have pointed out, online recruitment can certainly be cost effective, but cannot be relied on to generate a sizable response. As a sole source, it tends to underdeliver even for small trials.

I think we are now seeing the emergence of the newest flavor of magical thinking: Big Data. Take this quote from recent coverage of the JP Morgan Healthcare Conference:
For instance, Phase II, that ever-vexing rubber-road matchmaker for promising compounds that just might be worthless. Identifying the right patients for the right drug can make or break a Phase II trial, [John] Reynders said, and Big Data can come in handy as investigators distill mountains of imaging results, disease progression readings and genotypic traits to find their target participants. 
The prospect of widespread genetic mapping coupled with the power of Big Data could fundamentally change how biotech does R&D, [Alexis] Borisy said. "Imagine having 1 million cancer patients profiled with data sets available and accessible," he said. "Think how that very large data set might work--imagine its impact on what development looks like. You just look at the database and immediately enroll a trial of ideal patients."
Did you follow the logic of that last sentence? You immediately enroll ideal patients ... and all you had to do was look at a database! Problem solved!

Before you go rushing off to get your company some Big Data, please consider the fact that the overwhelming majority of Phase 2 trials do not have a neat, predefined set of genotypic traits they’re looking to enroll. In fact, narrowly-tailored phase 2 trials (such as recent registration trials of Xalkori and Zelboraf) actually enroll very quickly already, without the need for big databases. The reality for most drugs is exactly the opposite: they enter phase 2 actively looking for signals that will help identify subgroups that benefit from the treatment.

Also, it’s worth pointing out that having a million data points in a database does not mean that you have a million qualified, interested, and nearby patients just waiting to be enrolled in your trial. As recent work in medical record queries bears out, the yield from these databases promises to be low, and there are enormous logistic, regulatory, and personal challenges in identifying, engaging, and consenting the actual human beings represented by the data.

More, even fresher flavors of magical thinking are sure to emerge over time. Our urge to hope that our problems will just be washed away in a wave of cool new technology is just too powerful to resist.

However, when the trial is important, and the costs of delay are high, clinical teams need to set the wishful thinking aside and ask for a thoughtful plan based on hard evidence. Fortunately, that requires no magic bean purchase.

Magic Beans picture courtesy of Flikr user sleepyneko