REMOTE Redux: DTP trials are still hard

Maybe those pesky sites are good for something after all. 

It's been six years since Pfizer boldly announced the launch of its "clinical trial in a box". The REMOTE trial was designed to be entirely online, and involved no research sites: study information and consent was delivered via the web, and medications and diaries were shipped directly to patients' homes.

Despite the initial fanfare, within a month REMOTE's registration on ClinicalTrials.gov was quietly reduced from 600 to 283. The smaller trial ended not with a bang but a whimper, having randomized only 18 patients in over a year of recruiting.

Still, the allure of direct to patient clinical trials remains strong, due to a confluence of two factors. First, a frenzy of interest in running "patient centric clinical trials". Sponsors are scrambling to show they are doing something – anything – to show they have shifted to a patient-centered mindset. We cannot seem to agree what this means (as a great illustration of this, a recent article in Forbes on "How Patients Are Changing Clinical Trials" contained no specific examples of actual trials that had been changed by patients), but running a trial that directly engages patients wherever they are seems like it could work.

The less-openly-discussed other factor leading to interest in these DIY trials is sponsors' continuing willingness to heap almost all of the blame for slow-moving studies onto their research sites. If it’s all the sites’ fault – the reasoning goes – then cutting them out of the process should result in trials that are both faster and cheaper. (There are reasons to be skeptical about this, as I have discussed in the past, but the desire to drop all those pesky sites is palpable.)

However, while a few proof-of-concept studies have been done, there really doesn't seem to have been another trial to attempt a full-blown direct-to-patient clinical trial. Other pilots have been more successful, but had fairly lightweight protocols. For all its problems, REMOTE was a seriously ambitious project that attempted to package a full-blown interventional clinical trial, not an observational study.

In this context, it's great to see published results of the TAPIR Trial in vasculitis, which as far as I can tell is the first real attempt to run a DIY trial of a similar magnitude to REMOTE.

TAPIR was actually two parallel trials, identical in every respect except for their sites: one trial used a traditional group of 8 sites, while the other was virtual and recruited patients from anywhere in the country. So this was a real-time, head-to-head assessment of site performance.

And the results after a full two years of active enrollment?

• Traditional sites: 49 enrolled
• Patient centric: 10 enrolled

Even though we’re six years later, and online/mobile communications are even more ubiquitous, we still see the exact same struggle to enroll patients.

Maybe it’s time to stop blaming the sites? To be fair, they didn’t exactly set the world on fire – and I’m guessing the total cost of activating the 8 sites significantly exceeded the costs of setting up the virtual recruitment and patient logistics. But still, the site-less, “patient centric” approach once again came up astonishingly short.

Krischer J, Cronholm PF, Burroughs C, McAlear CA, Borchin R, Easley E, Davis T, Kullman J, Carette S, Khalidi N, Koening C, Langford CA, Monach P, Moreland L, Pagnoux C, Specks U, Sreih AG, Ytterberg S, Merkel PA, & Vasculitis Clinical Research Consortium. (2017). Experience With Direct-to-Patient Recruitment for Enrollment Into a Clinical Trial in a Rare Disease: A Web-Based Study. Journal of medical Internet research, 19 (2) PMID: 28246067

Will Your Family Make You a Better Trial Participant?

It is becoming increasing accepted within the research community that patient engagement leads to a host of positive outcomes – most importantly (at least practically speaking) improved clinical trial recruitment and retention.

But while we can all agree that "patient engagement is good" in a highly general sense, we don't have much consensus on what the implications of that idea might be. There is precious little hard evidence about how to either attract engaged patients, or how we might effectively turn "regular patients" into "engaged patients".

That latter point - that we could improve trial enrollment and completion rates by converting the (very large) pool of less-engaged patient - is a central tenet of the mHealth movement in clinical trials. Since technology can now accompany us almost anywhere, it would seem that we have an unprecedented opportunity to reach out and connect with current and potential trial participants.

However, there are signs that this promised revolution in patient engagement hasn't come about. From the decline of new apps being downloaded to the startlingly high rate of people abandoning their wearable health devices, there's a growing body of evidence suggesting that we aren't in fact making very good progress towards increasing engagement. We appear to have underestimated the inertia of the disengaged patient.

So what can we do? We know people like their technology, but if they're not using it to engage with their healthcare decisions, we're no better off as a result.

Daniel Calvert, in a recent blog post at Parallel 6 offers an intriguing solution: he suggests we go beyond the patient and engage their wider group of loved ones. By engaging what Calvert calls the Support Circle - those people most likely to "encourage the health and well being of that patient as they undergo a difficult period of their life" - trial teams will find themselves with a more supported, and therefore more engaged, participant, with corresponding benefits to enrollment and retention. 

Calvert outlines a number of potential mechanisms to get spouses, children, and other loved ones involved in the trial process:

During the consent process the patient can invite their support team in with them. A mobile application can be put on their phones enabling encouraging messages, emails, and texts to be sent. Loved ones can see if their companion or family member did indeed take today’s medication or make last Monday’s appointment. Gamification offers badges or pop-ups: “Two months of consecutive appointments attended” or “perfect eDiary log!” Loved ones can see those notifications, like/comment, and constantly encourage the patients. 

Supporting materials can also be included in the Support Circle application. There are a host of unknown terms to patients and their team. Glossaries, videos, FAQs, contact now, and so much more can be made available at their fingertips.

I have to admit I'm fascinated by Calvert's idea. I want him to be right: the picture of supportive, encouraging, loving spouses and children standing by to help a patient get through a clinical trial is an attractive one. So is the idea that they're just waiting for us to include them - all we need to do is a bit of digital communication with them to get them fully on board as members of the study team.

The problem, however, remains: we have absolutely no evidence that this approach will work. There is no data showing that it is superior to other approaches to engage trial patients.

(In fact, we may even have some indirect evidence that it may hinder enrollment: in trials that require active caregiver participation, such as those in Alzheimer's Disease, caregivers are believed to often contribute to the barriers to patient enrollment).

Calvert's idea is a good one, and it's worthy of consideration. More importantly, it's worthy of being rigorously tested against other recruitment and retention approaches. We have a lot of cool new technologies, and even more great ideas - we're not lacking for those. What we're lacking is hard data showing us how these things perform. What we especially need is comparative data showing how new tactics work relative to other approaches.

Over 5 years ago, I wrote a blog post bemoaning the sloppy approaches we take in trial recruitment - a fact made all the more painfully ironic by the massive intellectual rigor of the trials themselves. I'm not at all sure that we've made any real progress in those 5 years.

In my next post, I'll outline what I believe are some of the critical steps we need to take to improve the current situation, and start bringing some solid evidence to the table along with our ideas.

[Photo credit: Flikr user Matthew G, "Love (of technology)"]

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

• How to Catalyze a Clinical Trial - My inaugural post introducing the blog and its purpose
• Video: Predicting Referral Conversion in Clinical Trial Advertising - A somewhat technical but very important topic, how to visualize and model the “real time” results of recruitment advertising at the sites.
• The Crystal Ball is on the Fritz - What to do with a broken enrollment feasibility process, and how asking will never be as good as measuring

Partnerships in Clinical Trials

• The New Breed of Clinical Trial Matchmakers - A (hopefully pretty complete, thanks to knowledgeable commenters) listing of services looking to match interested patients to clinical trials
• Rethinking Patient Enrollment, in One Graphic - The perils of predictability in site-based enrollment
• Seize the Data! Will Big Data Save Us from Ourselves? - My take on what I consider to be the large and serious obstacles in the way of “Big Data” solutions for patient recruitment

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

[Photo credit: detour sign via Flikr user crossley]

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".)

Questionable Enrollment Math(s) - the Authors Respond

The authors of the study I blogged about on Monday were kind enough to post a lengthy comment, responding in part to some of the issues I raised. I thought their response was interesting, and so reprint it in its entirety below, interjecting my own reactions as well.

There were a number of points you made in your blog and the title of questionable maths was what caught our eye and so we reply on facts and provide context.

Firstly, this is a UK study where the vast majority of UK clinical trials take place in the NHS. It is about patient involvement in mental health studies - an area where recruitment is difficult because of stigma and discrimination.

I agree, in hindsight, that I should have titled the piece “questionable maths” rather than my Americanized “questionable math”. Otherwise, I think this is fine, although I’m not sure that anything here differs from my post.

1. Tripling of studies - You dispute NIHR figures recorded on a national database and support your claim with a lone anecdote - hardly data that provides confidence. The reason we can improve recruitment is that NIHR has a Clinical Research Network which provides extra staff, within the NHS, to support high quality clinical studies and has improved recruitment success.

To be clear, I did not “dispute” the figures so much as I expressed sincere doubt that those figures correspond with an actual increase in actual patients consenting to participate in actual UK studies. The anecdote explains why I am skeptical – it's a bit like I've been told there was a magnitude 8 earthquake in Chicago, but neither I nor any of my neighbors felt anything. There are many reasons why reported numbers can increase in the absence of an actual increase. It’s worth noting that my lack of confidence in the NIHR's claims appears to be shared by the 2 UK-based experts quoted by Applied Clinical Trials in the article I linked to.

2. Large database: We have the largest database of detailed study information and patient involvement data - I have trawled the world for a bigger one and NIMH say there certainly isn't one in the USA. This means few places where patient impact can actually be measured

3. Number of studies: The database has 374 studies which showed among other results that service user involvement increased over time probably following changes by funders e.g. NIHR requests information in the grant proposal on how service users have been and will be involved - one of the few national funders to take this issue seriously.

As far as I can tell, neither of these points is in dispute.

4. Analysis of patient involvement involves the 124 studies that have completed. You cannot analyse recruitment success unless then.

I agree you cannot analyze recruitment success in studies that have not yet completed. My objection is that in both the KCL press release and the NIHR-authored Guardian article, the only number mentioned in 374, and references to the recruitment success findings came immediately after references to that number. For example:

Published in the British Journal of Psychiatry, the researchers analysed 374 studies registered with the Mental Health Research Network (MHRN).

Studies which included collaboration with service users in designing or running the trial were 1.63 times more likely to recruit to target than studies which only consulted service users.  Studies which involved more partnerships - a higher level of Patient and Public Involvement (PPI) - were 4.12 times more likely to recruit to target.

The above quote clearly implies that the recruitment conclusions were based on an analysis of 374 studies – a sample 3 times larger than the sample actually used. I find this disheartening.

The complexity measure was developed following a Delphi exercise with clinicians, clinical academics and study delivery staff to include variables likely to be barriers to recruitment. It predicts delivery difficulty (meeting recruitment & delivery staff time). But of course you know all that as it was in the paper.

Yes, I did know this, and yes, I know it because it was in the paper. In fact, that’s all I know about this measure, which is what led me to characterize it as “arbitrary and undocumented”. To believe that all aspects of protocol complexity that might negatively affect enrollment have been adequately captured and weighted in a single 17-point scale requires a leap of faith that I am not, at the moment, able to make. The extraordinary claim that all complexity issues have been accounted for in this model requires extraordinary evidence, and “we conducted a Delphi exercise” does not suffice.  

6. All studies funded by NIHR partners were included – we only excluded studies funded without peer review, not won competitively. For the involvement analysis we excluded industry studies because of not being able to contact end users and where inclusion compromised our analysis reliability due to small group sizes.

It’s only that last bit I was concerned about. Specifically, the 11 studies that were excluded due to being in “clinical groups” that were too small, despite the fact that “clinical groups” appear to have been excluded as non-significant from the final model of recruitment success.

(Also: am I being whooshed here? In a discussion of "questionable math" the authors' enumeration goes from 4 to 6. I’m going to take the miscounting here as a sly attempt to see if I’m paying attention...)

I am sure you are aware of the high standing of the journal and its robust peer review. We understand that our results must withstand the scrutiny of other scientists but many of your comments were unwarranted. This is the first in the world to investigate patient involvement impact. No other databases apart from the one held by the NIHR Mental Health Research Network is available to test – we only wish they were.

I hope we can agree that peer review – no matter how "high standing" the journal – is not a shield against concern and criticism. Despite the length of your response, I’m still at a loss as to which of my comments specifically were unwarranted.

In fact, I feel that I noted very clearly that my concerns about the study’s limitations were minuscule compared to my concerns about the extremely inaccurate way that the study has been publicized by the authors, KCL, and the NIHR. Even if I conceded every possible criticism of the study itself, there remains the fact that in public statements, you

1. Misstated an odds ratio of 4 as “4 times more likely to”
2. Overstated the recruitment success findings as being based on a sample 3 times larger than it actually was
3. Re-interpreted, without reservation, a statistical association as a causal relationship
4. Misstated the difference between the patient involvement categories as being a matter of merely “involving just one or two patients in the study team”

And you did these consistently and repeatedly – in Dr Wykes's blog post, in the KCL press release, and in the NIHR-written Guardian article.

To use the analogy from my previous post: if a pharmaceutical company had committed these acts in public statements about a new drug, public criticism would have been loud and swift.

Your comment on the media coverage of odds ratios is an issue that scientists need to overcome (there is even a section in Wikipedia).

It's highly unfair to blame "media coverage" for the use of an odds ratio as if it were a relative risk ratio. In fact, the first instance of "4 times more likely" appears in Dr Wykes's own blog post. It's repeated in the KCL press release, so you yourselves appear to have been the source of the error.

You point out the base rate issue but of course in a logistic regression you also take into account all the other variables that may impinge on the outcome prior to assessing the effects of our key variable patient involvement - as we did – and showed that the odds ratio is 4.12 - So no dispute about that. We have followed up our analysis to produce a statement that the public will understand. Using the following equations:

Model predicted recruitment lowest level of involvement exp(2.489-.193*8.8-1.477)/(1+exp(2.489-.193*8.8-1.477))=0.33

Model predicted recruitment highest level of involvement exp(2.489-.193*8.8-1.477+1.415)/(1+exp(2.489-.193*8.8-1.477+1.415)=0.67

For a study of typical complexity without a follow up increasing involvement from the lowest to the highest levels increased recruitment from 33% to 66% i.e. a doubling.

So then, you agree that your prior use of “4 times more likely” was not true? Would you be willing to concede that in more or less direct English?

This is important and is the first time that impact has been shown for patient involvement on the study success.

Luckily in the UK we have a network that now supports clinicians to be involved and a system for ensuring study feasibility.

The addition of patient involvement is the additional bonus that allows recruitment to increase over time and so cutting down the time for treatments to get to patients.

No, and no again. This study shows an association in a model. The gap between that and a causal relationship is far too vast to gloss over in this manner.

In summary, I thank the authors for taking the time to response, but I feel they've overreacted to my concerns about the study, and seriously underreacted to my more important concerns about their public overhyping of the study. 

I believe this study provides useful, though limited, data about the potential relationship between patient engagement and enrollment success. On the other hand, I believe the public positioning of the study by its authors and their institutions has been exaggerated and distorted in clearly unacceptable ways. I would ask the authors to seriously consider issuing public corrections on the 4 points listed above.

Questionable Enrollment Math at the UK's NIHR

There has been considerable noise coming out of the UK lately about successes in clinical trial enrollment.

First, a couple months ago came the rather dramatic announcement that clinical trial participation in the UK had "tripled over the last 6 years". That announcement, by the chief executive of the

Sweet creature of bombast: is Sir John
writing press releases for the NIHR?

National Institute of Health Research's Clinical Research Network, was quickly and uncritically picked up by the media.

That immediately caught my attention. In large, global trials, most pharmaceutical companies I've worked with can do a reasonable job of predicting accrual levels in a given country. I like to think that if participation rates in any given country had jumped that heavily, I’d have heard something.

(To give an example: looking at a quite-typical study I worked on a few years ago: UK sites were overall slightly below the global average. The highest-enrolling countries were about 2.5 times as fast. So, a 3-fold increase in accruals would have catapulted the UK from below average to the fastest-enrolling country in the world.)

Further inquiry, however, failed to turn up any evidence that the reported tripling actually corresponded to more human beings enrolled in clinical trials. Instead, there is some reason to believe that all we witnessed was increased reporting of trial participation numbers.

Now we have a new source of wonder, and a new giant multiplier coming out of the UK. As the Director of the NIHR's Mental Health Research Network, Til Wykes, put it in her blog coverage of her own paper:

Our research on the largest database of UK mental health studies shows that involving just one or two patients in the study team means studies are 4 times more likely to recruit successfully.

Again, amazing! And not just a tripling – a quadrupling!

Understand: I spend a lot of my time trying to convince study teams to take a more patient-focused approach to clinical trial design and execution. I desperately want to believe this study, and I would love having hard evidence to bring to my clients.

At first glance, the data set seems robust. From the King's College press release:

Published in the British Journal of Psychiatry, the researchers analysed 374 studies registered with the Mental Health Research Network (MHRN).

Studies which included collaboration with service users in designing or running the trial were 1.63 times more likely to recruit to target than studies which only consulted service users.  Studies which involved more partnerships - a higher level of Patient and Public Involvement (PPI) - were 4.12 times more likely to recruit to target.

But here the first crack appears. It's clear from the paper that the analysis of recruitment success was not based on 374 studies, but rather a much smaller subset of 124 studies. That's not mentioned in either of the above-linked articles.

And at this point, we have to stop, set aside our enthusiasm, and read the full paper. And at this point, critical doubts begin to spring up, pretty much everywhere.

First and foremost: I don’t know any nice way to say this, but the "4 times more likely" line is, quite clearly, a fiction. What is reported in the paper is a 4.12 odds ratio between "low involvement" studies and "high involvement" studies (more on those terms in just a bit).  Odds ratios are often used in reporting differences between groups, but they are unequivocally not the same as "times more likely than".

This is not a technical statistical quibble. The authors unfortunately don’t provide the actual success rates for different kinds of studies, but here is a quick example that, given other data they present, is probably reasonably close:

• A Studies: 16 successful out of 20 
• Probability of success: 80% 
• Odds of success: 4 to 1
• B Studies: 40 successful out of 80
• Probability of success: 50%
• Odds of success: 1 to 1

From the above, it’s reasonable to conclude that A studies are 60% more likely to be successful than B studies (the A studies are 1.6 times as likely to succeed). However, the odds ratio is 4.0, similar to the difference in the paper. It makes no sense to say that A studies are 4 times more likely to succeed than B studies.

This is elementary stuff. I’m confident that everyone involved in the conduct and analysis of the MHRN paper knows this already. So why would Dr Wykes write this? I don’t know; it's baffling. Maybe someone with more knowledge of the politics of British medicine can enlighten me.

If a pharmaceutical company had promoted a drug with this math, the warning letters and fines would be flying in the door fast. And rightly so. But if a government leader says it, it just gets recycled verbatim.

The other part of Dr Wykes's statement is almost equally confusing. She claims that the enrollment benefit occurs when "involving just one or two patients in the study team". However, involving one or two patients would seem to correspond to either the lowest ("patient consultation") or the middle level of reported patient involvement (“researcher initiated collaboration”). In fact, the "high involvement" categories that are supposed to be associated with enrollment success are studies that were either fully designed by patients, or were initiated by patients and researchers equally. So, if there is truly a causal relationship at work here, improving enrollment would not be merely a function of adding a patient or two to the conversation.

There are a number of other frustrating aspects of this study as well. It doesn't actually measure patient involvement in any specific research program, but uses just 3 broad categories (that the researchers specified at the beginning of each study). It uses an arbitrary and undocumented 17-point scale to measure "study complexity", which collapses and quite likely underweights many critical factors into a single number. The enrollment analysis excluded 11 studies because they weren't adequate for a factor that was later deemed non-significant. And probably the most frustrating facet of the paper is that the authors share absolutely no descriptive data about the studies involved in the enrollment analysis. It would be completely impossible to attempt to replicate its methods or verify its analysis. Do the authors believe that "Public Involvement" is only good when it’s not focused on their own work?

However, my feelings about the study and paper are an insignificant fraction of the frustration I feel about the public portrayal of the data by people who should clearly know better. After all, limited evidence is still evidence, and every study can add something to our knowledge. But the public misrepresentation of the evidence by leaders in the area can only do us harm: it has the potential to actively distort research priorities and funding.

Why This Matters

We all seem to agree that research is too slow. Low clinical trial enrollment wastes time, money, and the health of patients who need better treatment options.

However, what's also clear is that we lack reliable evidence on what activities enable us to accelerate the pace of enrollment without sacrificing quality. If we are serious about improving clinical trial accrual, we owe it to our patients to demand robust evidence for what works and what doesn’t. Relying on weak evidence that we've already solved the problem ("we've tripled enrollment!") or have a method to magically solve it ("PPI quadrupled enrollment!") will cause us to divert significant time, energy, and human health into areas that are politically favored but less than certain to produce benefit. And the overhyping those results by research leadership compounds that problem substantially. NIHR leadership should reconsider its approach to public discussion of its research, and practice what it preaches: critical assessment of the data.

[Update Sept. 20: The authors of the study have posted a lengthy comment below. My follow-up is here.]
 
[Image via flikr user Elliot Brown.]

Ennis L, & Wykes T (2013). Impact of patient involvement in mental health research: longitudinal study. The British journal of psychiatry : the journal of mental science PMID: 24029538

Preview of Enrollment Analytics: Moving Beyond the Funnel (Shameless DIA Self-Promotion, Part 2)

Are we looking at our enrollment data in the right way?

I will be chairing a session on Tuesday on this topic, joined by a couple of great presenters (Diana Chung from Gilead and Gretchen Goller from PRA).

Here's a short preview of the session:

Hope to see you there. It should be a great discussion.

Session Details:

June 25, 1:45PM - 3:15PM

• Session Number: 241
• Room Number: 205B

1. Enrollment Analytics: Moving Beyond the Funnel
Paul Ivsin
VP, Consulting Director
CAHG Clinical Trials

2. Use of Analytics for Operational Planning
Diana Chung, MSc
Associate Director, Clinical Operations
Gilead

3. Using Enrollment Data to Communicate Effectively with Sites
Gretchen Goller, MA
Senior Director, Patient Access and Retention Services
PRA

Clinical Trial Enrollment, ASCO 2013 Edition

Even by the already-painfully-embarrassingly-low standards of clinical trial enrollment in general, patient enrollment in cancer clinical trials is slow. Horribly slow. In many cancer trials, randomizing one patient every three or four months isn't bad at all – in fact, it's par for the course. The most
commonly-cited number is that only 3% of cancer patients participate in a trial – and although exact details of how that number is measured are remarkably difficult to pin down, it certainly can't be too far from reality.

Ultimately, the cost of slow enrollment is borne almost entirely by patients; their payment takes the form of fewer new therapies and less evidence to support their treatment decisions.

So when a couple dozen thousand of the world's top oncologists fly into Chicago to meet, you'd figure that improving accrual would be high on everyone’s agenda. You can't run your trial without patients, after all.

But every year, the annual ASCO meeting underdelivers in new ideas for getting more patients into trials. I suppose this a consequence of ASCO's members-only focus: getting the oncologists themselves to address patient accrual is a bit like asking NASCAR drivers to tackle the problems of aerodynamics, engine design, and fuel chemistry.

Nonetheless, every year, a few brave souls do try. Here is a quick rundown of accrual-related abstracts at this year’s meeting, conveniently sorted into 3 logical categories:

1. As Lord Kelvin may or may not have said, “If you cannot measure it, you cannot improve it.”

• Abstract e15572: Inadequate data availability on clinical trial accrual and its effect on progress in cancer research

Probably the most sensible of this year's crop, because rather than trying to make something out of nothing, the authors measure exactly how pervasive the nothing is. Specifically, they attempt to obtain fairly basic patient accrual data for the last three years' worth of clinical trials in kidney cancer. Out of 108 trials identified, they managed to get – via search and direct inquiries with the trial sponsors – basic accrual data for only 43 (40%).

That certainly qualifies as “terrible”, though the authors content themselves with “poor”.

Interestingly, exactly zero of the 32 industry-sponsored trials responded to the authors' initial survey. This fits with my impression that pharma companies continue to think of accrual data as proprietary, though what sort of business advantage it gives them is unclear. Any one company will have only run a small fraction of these studies, greatly limiting their ability to draw anything resembling a valid conclusion.

• Abstract TPS6645: Predictors of accrual success for cooperative group trials: The Cancer and Leukemia Group B (Alliance) experience

CALGB investigators look at 110 trials over the past 10 years to see if they can identify any predictive markers of successful enrollment. Unfortunately, the trials themselves are pretty heterogeneous (accrual periods ranged from 6 months to 8.8 years), so finding a consistent marker for successful trials would seem unlikely.

And, in fact, none of the usual suspects (e.g., startup time, disease prevalence) appears to have been significant. The exception was provision of medication by the study, which was positively associated with successful enrollment.

The major limitation with this study, apart from the variability of trials measured, is in its definition of “successful”, which is simply the total number of planned enrolled patients. Under both of their definitions, a slow-enrolling trial that drags on for years before finally reaching its goal is successful, whereas if that same trial had been stopped early it is counted as unsuccessful. While that sometimes may be the case, it's easy to imagine situations where allowing a slow trial to drag on is a painful waste of resources – especially if results are delayed enough to bring their relevance into question.

Even worse, though, is that a trial’s enrollment goal is itself a prediction. The trial steering committee determines how many sites, and what resources, will be needed to hit the number needed for analysis. So in the end, this study is attempting to identify predictors of successful predictions, and there is no reason to believe that the initial enrollment predictions were made with any consistent methodology.

2. If you don't know, maybe ask somebody?

• Abstract 8592: Strategies to overcome barriers to accrual (BtA) to NCI-sponsored clinical trials: A project of the NCI-Myeloma Steering Committee Accrual Working Group (NCI-MYSC AWG)
  
• Abstract 1596: Rapid online feedback to improve clinical trial accrual: CODEL anaplastic glioma (AG) (NCCTG/Alliance N0577) as a model

With these two abstracts we celebrate and continue the time-honored tradition of alchemy, whereby we transmute base opinion into golden data. The magic number appears to be 100: if you've got 3 digits' worth of doctors telling you how they feel, that must be worth something.

In the first abstract, a working group is formed to identify and vote on the major barriers to accrual in oncology trials. Then – and this is where the magic happens – that same group is asked to identify and vote on possible ways to overcome those barriers.

In the second, a diverse assortment of community oncologists were given an online survey to provide feedback on the design of a phase 3 trial in light of recent new data. The abstract doesn't specify who was initially sent the survey, so we cannot tell response rate, or compare survey responders to the general population (I'll take a wild guess and go with “massive response bias”).

Market research is sometimes useful. But what cancer clinical trial do not need right now are more surveys are working groups. The “strategies” listed in the first abstract are part of the same cluster of ideas that have been on the table for years now, with no appreciable increase in trial accrual.

3. The obligatory “What the What?” abstract

• Abstract 6564: Minority accrual on a prospective study targeting a diverse U.S. breast cancer population: An analysis of Wake Forest CCOP research base protocol 97609

The force with which my head hit my desk after reading this abstract made me concerned that it had left permanent scarring.

If this had been re-titled “Poor Measurement of Accrual Factors Leads to Inaccurate Accrual Reporting”, would it still have been accepted for this year’s meeting? That's certainly a more accurate title.

Let’s review: a trial intends to enroll both white and minority patients. Whites enroll much faster, leading to a period where only minority patients are recruited. Then, according to the authors, “an almost 4-fold increase in minority accrual raises question of accrual disparity.” So, sites will only recruit minority patients when they have no choice?

But wait: the number of sites wasn't the same during the two periods, and start-up times were staggered. Adjusting for actual site time, the average minority accrual rate was 0.60 patients/site/month in the first part and 0.56 in the second. So the apparent 4-fold increase was entirely an artifact of bad math.

This would be horribly embarrassing were it not for the fact that bad math seems to be endemic in clinical trial enrollment. Failing to adjust for start-up time and number of sites is so routine that not doing it is grounds for a presentation.

The bottom line

What we need now is to rigorously (and prospectively) compare and measure accrual interventions. We have lots of candidate ideas, and there is no need for more retrospective studies, working groups, or opinion polls to speculate on which ones will work best.  Where possible, accrual interventions should themselves be randomized to minimize confounding variables which prevent accurate assessment. Data needs to be uniformly and completely collected. In other words, the standards that we already use for clinical trials need to be applied to the enrollment measures we use to engage patients to participate in those trials.

This is not an optional consideration. It is an ethical obligation we have to cancer patients: we need to assure that we are doing all we can to maximize the rate at which we generate new evidence and test new therapies.

[Image credit: Logarithmic turtle accrual rates courtesy of Flikr user joleson.]

It's Not Them, It's You

Are competing trials slowing yours down? Probably not.

If they don't like your trial, EVERYTHING ELSE IN
THE WORLD is competition for their attention.

Rahlyn Gossen has a provocative new blog post up on her website entitled "The Patient Recruitment Secret". In it, she makes a strong case for considering site commitment to a trial – in the form of their investment of time, effort, and interest – to be the single largest driver of patient enrollment.

The reasoning behind this idea is clear and quite persuasive:

Every clinical trial that is not yours is a competing clinical trial. 

Clinical research sites have finite resources. And with research sites being asked to take on more and more duties, those resources are only getting more strained. Here’s what this reality means for patient enrollment. 

If research site staff are working on other clinical trials, they are not working on your clinical trial. Nor are they working on patient recruitment for your clinical trial. To excel at patient enrollment, you need to maximize the time and energy that sites spend recruiting patients for your clinical trial.

Much of this fits together very nicely with a point I raised in a post a few months ago, showing that improvements in site enrollment performance may often be made at the expense of other trials.

However, I would add a qualifier to these discussions: the number of active "competing" trials at a site is not a reliable predictor of enrollment performance. In other words, selecting sites who are not working on a lot of other trials will in no way improve enrollment in your trial.

This is an important point because, as Gossen points out, asking the number of other studies is a standard habit of sponsors and CROs on site feasibility questionnaires. In fact, many sponsors can get very hung up on competing trials – to the point of excluding potentially good sites that they feel are working on too many other things.

This came to a head recently when we were brought in to consult on a study experiencing significant enrollment difficulty. The sponsor was very concerned about competing trials at the sites – there was a belief that such competition was a big contributor to sluggish enrollment.

As part of our analysis, we collected updated information on competitive trials. Given the staggered nature of the trial's startup, we then calculated time-adjusted Net Patient Contributions for each site (for more information on that, see my write-up here).

We then cross-referenced competing trials to enrollment performance. The results were very surprising: the quantity of other trials had no effect on how the sites were doing.  Here's the data:

Each site's enrollment performance as it relates to number of other trials it's running.
Competitive trials do not appear to substantially impact rates of enrollment.

 Each site is a point. Good sites (higher up) and poor enrollers (lower) are virtually identical in terms of how many concurrent trials they were running.

Since running into this result, I've looked at the relationship between the number of competing trials in CRO feasibility questionnaires and final site enrollment for many of the trials we've worked on. In each case, the "competing" trials did not serve as even a weak predictor of eventual site performance.

I agree with Gossen's fundamental point that a site's interest and enthusiasm for your trial will help increase enrollment at that site. However, we need to do a better job of thinking about the best ways of measuring that interest to understand the magnitude of the effect that it truly has. And, even more importantly, we have to avoid reliance on substandard proxy measurements such as "number of competing trials", because those will steer us wrong in site selection. In fact, almost everything we tend to collect on feasibility questionnaires appears to be non-predictive and potentially misleading; but that's a post for another day.

[Image credit: research distractions courtesy of Flikr user ronocdh.]

Less than Jaw-Dropping: Half of Sites Are Below Average

Last week, the Tufts Center for the Study of Drug Development unleashed the latest in their occasional series of dire pronouncements about the state of pharmaceutical clinical trials.

One particular factoid from the CSDD "study" caught my attention:

Shocking performance stat:
57% of these racers won't medal!

* 11% of sites in a given trial typically fail to enroll a single patient, 37% under-enroll, 39% meet their enrollment targets, and 13% exceed their targets.

Many industry reporters uncritically recycled those numbers. Pharmalot noted:

Now, the bad news – 48 percent of the trial sites miss enrollment targets and study timelines often slip, causing extensions that are nearly double the original duration in order to meeting enrollment levels for all therapeutic areas.

(Fierce Biotech and Pharma Times also picked up the same themes and quotes from the Tufts PR.)

There are two serious problems with the data as reported.

One: no one – neither CSDD nor the journalists who loyally recycle its press releases – seem to remember this CSDD release from less than two years ago. It made the even-direr claim that

According to Tufts CSDD, two-thirds of investigative sites fail to meet the patient enrollment requirements for a given clinical trial.

If you believe both Tufts numbers, then it would appear that the number of under-performing sites has dropped almost 20% in just 20 months – from 67% in April 2011 to 48% in January 2013. For an industry as hidebound and slow-moving as drug development, this ought to be hailed as a startling and amazing improvement!

Maybe at the end of the day, 48% isn't a great number, but surely this would appear to indicate we're on the right track, right? Why would no one mention this?

Which leads me to problem two: I suspect that no one is connecting the 2 data points because no one is sure what it is we're even supposed to be measuring here.

In a clinical trial, a site's "enrollment target" is not an objectively-defined number. Different sponsors will have different ways of setting targets – in fact, the method for setting targets may vary from team to team within a single pharma company.

The simplest way to set a target is to divide the total number of expected patients by the number of sites. If you have 50 sites and want to enroll 500 patients, then viola ... everyone's got a "target" of 10 patients! But then as soon as some sites start exceeding their target, others will, by definition, fall short. That’s not necessarily a sign of underperformance – in fact, if a trial finishes enrollment dramatically ahead of schedule, there will almost certainly be a large number of "under target" sites.

Some sponsors and CROs get tricky about setting individual targets for each site. How do they set those? The short answer is: pretty arbitrarily. Targets are only partially based upon data from previous, similar (but not identical) trials, but are also shifted up or down by the (real or perceived) commercial urgency of the trial. They can also be influenced by a variety of subjective beliefs about the study protocol and an individual study manager's guesses about how the sites will perform.

If a trial ends with 0% of sites meeting their targets, the next trial in that indication will have a lower, more achievable target. The same will happen in the other direction: too-easy targets will be ratcheted up. The benchmark will jump around quite a bit over time.

As a result, "Percentage of trial sites meeting enrollment target" is, to put it bluntly, completely worthless as an aggregate performance metric. Not only will it change greatly based upon which set  of sponsors and studies you happen to look at, but even data from the same sponsors will wobble heavily over time.

Why does this matter?

There is a consensus that clinical development is much too slow -- we need to be striving to shorten clinical trial timelines and get drugs to market sooner. If we are going to make any headway in this effort, we need to accurately assess the forces that help or hinder the pace of development, and we absolutely must rigorously benchmark and test our work. The adoption of, and attention paid to unhelpful metrics will only confuse and delay our effort to improve the quality of speed of drug development.

[Photo of "underperforming" swimmers courtesy Boston Public Library on flikr.]

Holding Your Breath Also Might Work

Here's a fitting postscript to yesterday's article about wishful-thinking-based enrollment strategies: we received a note from a research site this morning. The site had opted out of my company's comprehensive recruitment campaign, telling the sponsor they preferred to recruit patients their own way.

Here's the latest update from the coordinator:

I've found one person and have called a couple of times, but no return calls.  I will be sending this potential patient a letter this week.  I'm keeping my fingers crossed in finding someone soon!

They don't want to participate in a broad internet/broadcast/advocacy group program, but it's OK -- they have their fingers crossed!

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