The proposed American Privacy Rights Act (APRA) has taken its first step U.S. House legislative process with several issue disagreements becoming more evident. On May 23, the U.S. House Committee on Energy and Commerce Subcommittee on Data, Innovation and Commerce approved the updated APRA, advancing the bill to full committee consideration. Just prior to the […]

On May 24, Governor Tim Walz signed into law Minnesota’s new comprehensive data privacy law, the Minnesota Consumer Data Privacy Act (HF 4757 referenced as the MCDPA). The MCDPA goes into effect on July 31, 2025, with some exceptions for colleges and universities (who have until 2029). The MCDPA is similar to other state privacy laws, […]

The New York legislature passed two bills on June 7, 2024 directed at children’s use of online technologies – the Stop Addictive Feeds Exploitation (SAFE) for Kids Act (S7694) that restricts access to addictive algorithmic feeds and the New York Child Data Protection Act (S7695) that bans sites from collecting, using, sharing or selling personal […]

On June 25th, Rhode Island Governor Daniel McKee transmitted without signature (effectively a pocket veto) the Rhode Island Data Transparency and Privacy Protection Act (SB 2500 / HB 7787). The act is based on the Washington Privacy Act model but diverges from the prevalent forms of that model in two ways. First, the act contains a unique […]

Agencies with healthcare clients in pharmaceuticals, healthcare services, digital health apps, or health-related connected devices such as wearables should take note that the Federal Trade Commission (FTC) final rule updating its Health Breach Notification Rule (HBNR) that took effect on July 29, 2024. The FTC considers a breach to include a covered entity’s unauthorized disclosure […]

Effective on October 21st of this year, the Federal Trade Commission (FTC) issued a new final rule that is intended to better combat ​“fake” reviews and testimonials by prohibiting the sale or purchase of “fake reviews” as well as granting the agency the opportunity to seek civil penalties against ​willful violators. The FTC made only […]

Two former board chairs of the Coalition for Healthcare Communication (CHC) were named as the Medical Advertising Hall of Fame (MAHF) 2025 inductees – Sharon Callahan, former Chief Client Officer at Omnicom Health Group (OHG), and Nick Colucci, former Chairman and CEO of Publicis Health/COO of Publicis Groupe North America. The inductees will be honored […]

The Royal Pharmaceutical Society and the British Pharmaceutical Students’ Association have called for all overseas candidates to sit the March 2021 registration assessment in their home countries.

The government implied wholesalers had more personal protective equipment in stock than was the case during the first wave of the COVID-19 pandemic, the Healthcare Distribution Association has said.

The Pharmaceutical Services Negotiating Committee is in talks with the government over potential plans to distribute COVID-19 treatments in primary care.

Community pharmacies should be reimbursed for their additional costs during the COVID-19 pandemic “as soon as possible”, the prime minister has told The Pharmaceutical Journal.

The General Pharmaceutical Council has said it is “double, treble, quadruple-checking” for any “flexibility” that would allow all overseas candidates to sit the March 2021 registration assessment exam in their countries of residence.

Starting COVID-19 patients on prophylactic anticoagulation within 24 hours of being admitted to hospital has been linked to a reduced risk of mortality.

Some 180 women were prescribed valproate, a medicine used to treat epilepsy and bipolar disorder, during their pregnancy within a 2.5 year interval, NHS data has revealed.

The latest information about the novel coronavirus identified in Wuhan, China, and advice on how pharmacists can help concerned patients and the public.

The type 2 diabetes mellitus drug semaglutide is effective for weight loss in non-diabetic overweight or obese adults, when taken alongside a reduced-calorie diet and exercise, researchers have found.

Consultations on the reclassification of two progestogen-only contraceptive pills from prescription-only to pharmacy medicines have been launched.

Community pharmacies will receive an estimated 12 referrals from the Discharge Medicines Service per year.

Researchers around the world are working at record speed to find the best ways to treat and prevent COVID-19, from investigating the possibility of repurposing existing drugs to searching for novel therapies against the virus.

The General Pharmaceutical Council has said most candidates living in countries with a two-hour or more time difference from the UK will be able to apply to sit the registration assessment at home.

Around half of Wales’ community pharmacies have expressed interest to health boards in providing COVID-19 vaccinations as part of the national programme.

Community pharmacies able to administer up to 400 COVID-19 vaccines per week can now apply to become designated vaccination sites, NHS England has said.

The UK’s first postal inhaler recycling scheme has been launched by pharmaceutical company Chiesi to support a more sustainable way of living for people with respiratory illnesses.

A new type of drug that targets chemotherapy directly to cancer cells reduces the risk of death from the most common type of bladder cancer by 30%, a phase III trial in the New England Journal of Medicine has suggested.

Pharmacy negotiators have discussed proposals to take “a patient registration-based approach” to the community pharmacy contractual framework.

More than half of patients with asthma in the UK are “potentially overusing” short-acting β2-agonists, according to research.

The Royal Pharmaceutical Society has expressed its sadness at the death of Joy Wingfield, honorary professor of Pharmacy Law and Ethics at the University of Nottingham.

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.]

PDUFA V commitments signal a strong commitment to tolerance of open debate in the face of uncertainty.

I can admit to a rather powerful lack of enthusiasm when reading about interpersonal squabbles. It’s even worse in the scientific world: when I read about debates getting mired in personal attacks I tend to simply stop reading and move on to something else.

However, the really interesting part of this week’s meeting of an FDA joint Advisory Committee to discuss the controversial diabetes drug Avandia – at least in the sense of likely long-term impact – is not the scientific question under discussion, but the surfacing and handling of the raging interpersonal battle going on right now inside the Division of Cardiovascular and Renal Products. So I'll have to swallow my distaste and follow along with the drama.

Two words that make us mistrust Duke:  Anil Potti Christian Laettner

Not that the scientific question at hand – does Avandia pose significant heart risks? – isn't interesting. It is. But if there’s one thing that everyone seems to agree on, it’s that we don’t have good data on the topic. Despite the re-adjudication of RECORD, no one trusts its design (and, ironically, the one trial with a design to rigorously answer the question was halted after intense pressure, despite an AdComm recommendation that it continue).  And no one seems particularly enthused about changing the current status of Avandia: in all likelihood it will continue to be permitted to be marketed under heavy restrictions. Rather than changing the future of diabetes, I suspect the committee will be content to let us slog along the same mucky trail.

The really interesting question, that will potentially impact CDER for years to come, is how it can function with frothing, open dissent among its staffers. As has been widely reported, FDA reviewer Tom Marciniak has written a rather wild and vitriolic assessment of the RECORD trial, excoriating most everyone involved. In a particularly stunning passage, Marciniak appears to claim that the entire output of anyone working at Duke University cannot be trusted because of the fraud committed by Duke cancer researcher Anil Potti:

I would have thought that the two words “Anil Potti” are sufficient for convincing anyone that Duke University is a poor choice for a contractor whose task it is to confirm the integrity of scientific research. 

(One wonders how far Marciniak is willing to take his guilt-by-association theme. Are the words “Cheng Yi Liang” sufficient to convince us that all FDA employees, including Marciniak, are poor choices for deciding matter relating to publicly-traded companies? Should I not comment on government activities because I’m a resident of Illinois (my two words: “Rod Blagojevich”)?)

Rather than censoring or reprimanding Marciniak, his supervisors have taken the extraordinary step of letting him publicly air his criticisms, and then they have in turn publicly criticized his methods and approach.

I have been unable to think of a similar situation at any regulatory agency. The tolerance for dissent being displayed by FDA is, I believe, completely unprecedented.

And that’s the cliffhanger for me: can the FDA’s commitment to transparency extend so far as to accommodate public disagreements about its own approval decisions? Can it do so even when the disagreements take an extremely nasty and inappropriate tone?

• Rather than considering that open debate is a good thing, will journalists jump on the drama and portray agency leadership as weak and indecisive?
• Will the usual suspects in Congress be able to exploit this disagreement for their own political gain? How many House subcommittees will be summoning Janet Woodcock in the coming weeks?

I think what Bob Temple and Norman Stockbridge are doing is a tremendous experiment in open government. If they can pull it off, it could force other agencies to radically rethink how they go about crafting and implementing regulations. However, I also worry that it is politically simply not a viable approach, and that the agency will ultimately be seriously hurt by attacks from the media and legislators.

Where is this coming from?

As part of its recent PDUFA V commitment, the FDA put out a fascinating draft document, Structured Approach to Benefit-Risk Assessment in Drug Regulatory Decision-Making. It didn't get a lot of attention when first published back in February (few FDA documents do). However, it lays out a rather bold vision for how the FDA can acknowledge the existence of uncertainty in its evaluation of new drugs. Its proposed structure even envisions an open and honest accounting of divergent interpretations of data:

When they're frothing at the mouth, even Atticus doesn't let them publish a review

A framework for benefit-risk decision-making that summarizes the relevant facts, uncertainties, and key areas of judgment, and clearly explains how these factors influence a regulatory decision, can greatly inform and clarify the regulatory discussion. Such a framework can provide transparency regarding the basis of conflicting recommendations made by different parties using the same information.

(Emphasis mine.)

Of course, the structured framework here is designed to reflect rational disagreement. Marciniak’s scattershot insults are in many ways a terrible first case for trying out a new level of transparency.

The draft framework notes that safety issues, like Avandia, are some of the major areas of uncertainty in the regulatory process. Contrast this vision of coolly and systematically addressing uncertainties with the sad reality of Marciniak’s attack:

In contrast to the prospective and highly planned studies of effectiveness, safety findings emerge from a wide range of sources, including spontaneous adverse event reports, epidemiology studies, meta-analyses of controlled trials, or in some cases from randomized, controlled trials. However, even controlled trials, where the evidence of an effect is generally most persuasive, can sometimes provide contradictory and inconsistent findings on safety as the analyses are in many cases not planned and often reflect multiple testing. A systematic approach that specifies the sources of evidence, the strength of each piece of evidence, and draws conclusions that explain how the uncertainty weighed on the decision, can lead to more explicit communication of regulatory decisions. We anticipate that this work will continue beyond FY 2013.

I hope that work will continue beyond 2013. Thoughtful, open discussions of real uncertainties are one of the most worthwhile goals FDA can aspire to, even if it means having to learn how to do so without letting the Marciniaks of the world scuttle the whole endeavor.

[Fair Warning: I have generally tried to keep this blog separate from my corporate existence, but am making an exception for two quick posts about the upcoming DIA 2013 Annual Meeting.]

Improving Enrollment in Pediatric Clinical Trials

Logistically, ethically, and emotionally, involving children in medical research is greatly different from the same research in adults. Some of the toughest clinical trials I've worked on, across a number of therapeutic areas, have been pediatric ones. They challenge you to come up with different approaches to introducing and explaining clinical research – approaches that have to work for doctors, kids, and parents simultaneously.

On Thursday June 27, Don Sickler, one of my team members, will be chairing a session titled “Parents as Partners: Engaging Caregivers for Pediatric Trials”. It should be a good session.

Joining Don are 2 people I've had the pleasure of working with in the past. Both of them combine strong knowledge of clinical research with a massive amount of positive energy and enthusiasm (no doubt a big part of what makes them successful).

However, they also differ in one key aspect: what they work on. One of them – Tristen Moors from Hyperion Therapeutics - works on an ultra-rare condition, Urea Cycle Disorder, a disease affecting only a few hundred children every year. On the other hand, Dr. Ann Edmunds is an ENT working in a thriving private practice. I met her because she was consistently the top enroller in a number of trials relating to tympanostomy tube insertion. Surgery to place “t-tubes” is one of the most common and routine outpatients surgeries there is, with an estimated half million kids getting tubes each year.

Each presents a special challenge: for rare conditions, how do you even find enough patients? For routine procedures, how do you convince parents to complicate their (and their children’s) lives by signing up for a multi-visit, multi-procedure trial?

Ann and Tristen have spent a lot of time tackling these issues, and should have some great advice to give.

For more information on the session, here’s Don’s posting on our news blog.

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

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

Results reporting requirements are pretty clear. Maybe critics should re-check their methods?

Ben Goldacre has rather famously described the clinical trial reporting requirements in the Food and Drug Administration Amendments Act of 2007 as a “fake fix” that was being thoroughly “ignored” by the pharmaceutical industry.

Pharma: breaking the law in broad daylight?

He makes this sweeping, unconditional proclamation about the industry and its regulators on the basis of  a single study in the BMJ, blithely ignoring the fact that a) the authors of the study admitted that they could not adequately determine the number of studies that were meeting FDAAA requirements and b) a subsequent FDA review that identified only 15 trials potentially out of compliance, out of a pool of thousands.


Despite the fact that the FDA, which has access to more data, says that only a tiny fraction of studies are potentially noncompliant, Goldacre's frequently repeated claims that the law is being ignored seems to have caught on in the general run of journalistic and academic discussions about FDAAA.

And now there appears to be additional support for the idea that a large percentage of studies are noncompliant with FDAAA results reporting requirements, in the form of a new study in the Journal of Clinical Oncology: "Public Availability of Results of Trials Assessing Cancer Drugs in the United States" by Thi-Anh-Hoa Nguyen, et al.. In it, the authors report even lower levels of FDAAA compliance – a mere 20% of randomized clinical trials met requirements of posting results on clinicaltrials.gov within one year.

Unsurprisingly, the JCO results were immediately picked up and circulated uncritically by the usual suspects.

I have to admit not knowing much about pure academic and cooperative group trial operations, but I do know a lot about industry-run trials – simply put, I find the data as presented in the JCO study impossible to believe. Everyone I work with in pharma trials is painfully aware of the regulatory environment they work in. FDAAA compliance is a given, a no-brainer: large internal legal and compliance teams are everywhere, ensuring that the letter of the law is followed in clinical trial conduct. If anything, pharma sponsors are twitchily over-compliant with these kinds of regulations (for example, most still adhere to 100% verification of source documentation – sending monitors to physically examine every single record of every single enrolled patient - even after the FDA explicitly told them they didn't have to).

I realize that’s anecdotal evidence, but when such behavior is so pervasive, it’s difficult to buy into data that says it’s not happening at all. The idea that all pharmaceutical companies are ignoring a highly visible law that’s been on the books for 6 years is extraordinary. Are they really so brazenly breaking the rules? And is FDA abetting them by disseminating incorrect information?

Those are extraordinary claims, and would seem to require extraordinary evidence. The BMJ study had clear limitations that make its implications entirely unclear. Is the JCO article any better?

Some Issues

In fact, there appear to be at least two major issues that may have seriously compromised the JCO findings:

1. Studies that were certified as being eligible for delayed reporting requirements, but do not have their certification date listed.

The study authors make what I believe to be a completely unwarranted assumption:

In trials for approval of new drugs or approval for a new indication, a certification [permitting delayed results reporting] should be posted within 1 year and should be publicly available.

It’s unclear to me why the authors think the certifications “should be” publicly available. In re-reading FDAAA section 801, I don’t see any reference to that being a requirement. I suppose I could have missed it, but the authors provide a citation to a page that clearly does not list any such requirement.

But their methodology assumes that all trials that have a certification will have it posted:

If no results were posted at ClinicalTrials.gov, we determined whether the responsible party submitted a certification. In this case, we recorded the date of submission of the certification to ClinicalTrials.gov.

If a sponsor gets approval from FDA to delay reporting (as is routine for all drugs that are either not approved for any indication, or being studied for a new indication – i.e., the overwhelming majority of pharma drug trials), but doesn't post that approval on the registry, the JCO authors deem that trial “noncompliant”. This is not warranted: the company may have simply chosen not to post the certification despite being entirely FDAAA compliant.

2. Studies that were previously certified for delayed reporting and subsequently reported results

It is hard to tell how the authors treated this rather-substantial category of trials. If a trial was certified for delayed results reporting, but then subsequently published results, the certification date becomes difficult to find. Indeed, it appears in the case where there were results, the authors simply looked at the time from study completion to results posting. In effect, this would re-classify almost every single one of these trials from compliant to non-compliant. Consider this example trial:

• Phase 3 trial completes January 2010
• Certification of delayed results obtained December 2010 (compliant)
• FDA approval June 2013
• Results posted July 2013 (compliant)

In looking at the JCO paper's methods section, it really appears that this trial would be classified as reporting results 3.5 years after completion, and therefore be considered noncompliant with FDAAA. In fact, this trial is entirely kosher, and would be extremely typical for many phase 2 and 3 trials in industry.

Time for Some Data Transparency

The above two concerns may, in fact, be non-issues. They certainly appear to be implied in the JCO paper, but the wording isn't terribly detailed and could easily be giving me the wrong impression.

However, if either or both of these issues are real, they may affect the vast majority of "noncompliant" trials in this study. Given the fact that most clinical trials are either looking at new drugs, or looking at new indications for new drugs, these two issues may entirely explain the gap between the JCO study and the unequivocal FDA statements that contradict it.

I hope that, given the importance of transparency in research, the authors will be willing to post their data set publicly so that others can review their assumptions and independently verify their conclusions. It would be more than a bit ironic otherwise.
Thi-Anh-Hoa Nguyen, Agnes Dechartres, Soraya Belgherbi, and Philippe Ravaud (2013). Public Availability of Results of Trials Assessing Cancer Drugs in the United States JOURNAL OF CLINICAL ONCOLOGY DOI: 10.1200/JCO.2012.46.9577

This morning I ran across a bit of a coffee-spitter: in the middle of an otherwise opaquely underinformative press release fromTranscelerate Biopharma about the launch of their

Counterfeits flooding the market? Really?

"Comparator Network" - which will perhaps streamline member companies' ability to obtain drugs from each other for clinical trials using active comparator arms -  the CEO of the consortium, Dalvir Gill, drops a rather remarkable quote:

"Locating and accessing these comparators at the right time, in the right quantities and with the accompanying drug stability and regulatory information we need, doesn't always happen efficiently. This is further complicated by infiltration of the commercial drug supply chain by counterfeit drugs.  With the activation of our Comparator Network the participating TransCelerate companies will be able to source these comparator drugs directly from each other, be able to secure supply when they need it in the quantities they need, have access to drug data and totally mitigate the risk of counterfeit drugs in that clinical trial."

Counterfeit drugs have even been known to have been involved in clinical drug trials.^{[citation needed]}

“Children are not small adults.” We invoke this saying, in a vague and hand-wavy manner, whenever we talk about the need to study drugs in pediatric populations. It’s an interesting idea, but it really cries out for further elaboration. If they’re not small adults, what are they? Are pediatric efficacy and safety totally uncorrelated with adult efficacy and safety? Or are children actually kind of like small adults in certain important ways?

Pediatric post-marketing studies have been completed for over 200 compounds in the years since BPCA (2002, offering a reward of 6 months extra market exclusivity/patent life to any drug conducting requested pediatric studies) and PREA (2007, giving FDA power to require pediatric studies) were enacted. I think it is fair to say that at this point, it would be nice to have some sort of comprehensive idea of how FDA views the risks associated with treating children with medications tested only on adults. Are they in general less efficacious? More? Is PK in children predictable from adult studies a reasonable percentage of the time, or does it need to be recharacterized with every drug?

Essentially, my point is that BPCA/PREA is a pretty crude tool: it is both too broad in setting what is basically a single standard for all new adult medications, and too vague as to what exactly that standard is.

In fact, a 2008 published review from FDA staffers and a 2012 Institute of Medicine report both show one clear trend: in a significant majority of cases, pediatric studies resulted in validating the adult medication in children, mostly with predictable dose and formulation adjustments (77 of 108 compounds (71%) in the FDA review, and 27 of 45 (60%) in the IOM review, had label changes that simply reflected that use of the drug was acceptable in younger patients).

So, it seems, most of the time, children are in fact not terribly unlike small adults.

But it’s also true that the percentages of studies that show lack of efficacy, or bring to light a new safety issue with the drug’s use in children, is well above zero. There is some extremely important information here.

To paraphrase John Wanamaker: we know that half our PREA studies are a waste of time; we just don’t know which half.

This would seem to me to be the highest regulatory priority – to be able to predict which new drugs will work as expected in children, and which may truly require further study. After a couple hundred compounds have gone through this process, we really ought to be better positioned to understand how certain pharmacological properties might increase or decrease the risks of drugs behaving differently than expected in children. Unfortunately, neither the FDA nor the IOM papers venture any hypotheses about this – both end up providing long lists of examples of certain points, but not providing any explanatory mechanisms that might enable us to engage in some predictive risk assessment.

While FDASIA did not advance PREA in terms of more rigorously defining the scope of pediatric requirements (or, better yet, requiring FDA to do so), it did address one lingering concern by requiring that FDA publish non-compliance letters for sponsors that do not meet their commitments. (PREA, like FDAAA, is a bit plagued by lingering suspicions that it’s widely ignored by industry.)

The first batch of letters and responses has been published, and it offers some early insights into the problems engendered by the nebulous nature of PREA and its implementation.

These examples, unfortunately, are still a bit opaque – we will need to wait on the FDA responses to the sponsors to see if some of the counter-claims are deemed credible. In addition, there are a few references to prior deferral requests, but the details of the request (and rationales for the subsequent FDA denials) do not appear to be publicly available. You can read FDA’s take on the new postings on their blog, or in the predictably excellent coverage from Alec Gaffney at RAPS.

Looking through the first 4 drugs publicly identified for noncompliance, the clear trend is that there is no trend. All these PREA requirements have been missed for dramatically different reasons.

Here’s a quick rundown of the drugs at issue – and, more interestingly, the sponsor responses:

1. Renvela - Genzyme (full response)

Genzyme appears to be laying responsibility for the delay firmly at FDA’s feet here, basically claiming that FDA continued to pile on new requirements over time:

Genzyme’s correspondence with the FDA regarding pediatric plans and design of this study began in 2006 and included a face to face meeting with FDA in May 2009. Genzyme submitted 8 revisions of the pediatric study design based on feedback from FDA including that received in 4 General Advice Letters. The Advice Letter dated February 17, 2011  contained further recommendations on the study design, yet still required the final clinical study report  by December 31, 2011.

This highlights one of PREA’s real problems: the requirements as specified in most drug approval letters are not specific enough to fully dictate the study protocol. Instead, there is a lot of back and forth between the sponsor and FDA, and it seems that FDA does not always fully account for their own contribution to delays in getting studies started.

2. Hectorol - Genzyme (full response)

In this one, Genzyme blames the FDA not for too much feedback, but for none at all:

On December 22, 2010, Genzyme submitted a revised pediatric development plan (Serial No. 212) which was intended to address FDA feedback and concerns that had been received to date. This submission included proposed protocol HECT05310. [...] At this time, Genzyme has not received feedback from the FDA on the protocol included in the December 22, 2010 submission.

If this is true, it appears extremely embarrassing for FDA. Have they really not provided feedback in over 2.5 years, and yet still sending noncompliance letters to the sponsor? It will be very interesting to see an FDA response to this.

3. Cleviprex – The Medicines Company (full response)

This is the only case where the pharma company appears to be clearly trying to game the system a bit. According to their response:

Recognizing that, due to circumstances beyond the company’s control, the pediatric assessment could not be completed by the due date, The Medicines Company notified FDA in September 2010, and sought an extension. At that time, it was FDA’s view that no extensions were available. Following the passage of FDASIA, which specifically authorizes deferral extensions, the company again sought a deferral extension in December 2012. 

So, after hearing that they had to move forward in 2010, the company promptly waited 2 years to ask for another extension. During that time, the letter seems to imply that they did not try to move the study forward at all, preferring to roll the dice and wait for changing laws to help them get out from under the obligation.

4. Twinject/Adrenaclick – Amedra (full response)

The details of this one are heavily redacted, but it may also be a bit of gamesmanship from the sponsor. After purchasing the injectors, Amedra asked for a deferral. When the deferral was denied, they simply asked for the requirements to be waived altogether. That seems backwards, but perhaps there's a good reason for that.

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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.
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

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.

The articles identified through the search had to match the corresponding trial in terms of the information registered at ClinicalTrials.gov (i.e., same objective, same sample size, same primary outcome, same location, same responsible party, same trial phase, and same sponsor) and had to present results for the primary outcome. 

So it appears that a reviewed had to score the journal article as an exact match on 8 criteria in order for the trial to be considered the same. That could easily lead to exclusion of journal articles on the basis of very insubstantial differences. The authors provide no detail on this; and again, that would be easy to verify if the study dataset was published. 

The reason I harp on this, and worry about the matching methodology, is that two of the authors of this study were also involved in a methodologically opaque and flawed study about clinical trial results posted in the JCO. In that study, as well, the authors appeared to use an incorrect methodology to identify published clinical trials. When I pointed the issues out, the corresponding author merely reiterated what was already (insufficiently) in the paper's Methodology section.

I find it strange beyond belief, and more than a little hypocritical, that researchers would use a public, taxpayer-funded database as the basis of their studies, and yet refuse to provide their data for public review. There are no technological or logistical issues preventing this kind of sharing, and there is an obvious ethical point in favor of transparency.

But if the authors are reasonably close to correct in their results, I'm not sure what to make of this study. 

The Nature article covering this study contend that

[T]he [ClinicalTrials.gov] database was never meant to replace journal publications, which often contain longer descriptions of methods and results and are the basis for big reviews of research on a given drug.

I suppose that some journal articles have better methodology sections, although this is far from universally true (and, like this study here, these methods are often quite opaquely described and don't support replication). As for results, I don't believe that's the case. In this study, the opposite was true: ClinicalTrial.gov results were generally more complete than journal results. And I have no idea why the registry wouldn't surpass journals as a more reliable and complete source of information for "big reviews".

Perhaps it is a function of my love of getting my hands dirty digging into the data, but if we are witnessing a turning point where journal articles take a distant back seat to the ClinicalTrials.gov registry, I'm enthused. ClinicalTrials.gov is public, free, and contains structured data; journal articles are expensive, unparsable, and generally written in painfully unclear language. To me, there's really no contest. 

Carolina Riveros, Agnes Dechartres, Elodie Perrodeau, Romana Haneef, Isabelle Boutron, & Philippe Ravaud (2013). Timing and Completeness of Trial Results Posted at ClinicalTrials.gov and Published in Journals PLoS Medicine DOI: 10.1371/journal.pmed.1001566

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.

• Both are university/private collaborations, and both (perhaps unsurprisingly) are rooted in California: Heath eHeart is jointly run by UCSF and the American Heart Association, while Quantified Diet is run by app developer Lift with scientific support from a (unidentified?) team at Berkeley.
• Both are pushing for a million or more participants, dwarfing even very large traditional studies by orders of magnitude.
• Health eHeart is entirely observational, and researchers will have the ability to request its data to test their own hypotheses, whereas Quantified Diet is a controlled, randomized trial.

This new 10-minute TEDMED talk is getting quite a bit of attention:


 (if embedded video does not work, try the TED site itself.)

In it, Roger Stein claims to have created an approach to advancing drugs through clinical trials that will "fundamentally change the way research for cancer and lots of other things gets done".

Because the costs of bringing a drug to market are so high, time from discovery to marketing is so long, and the chances of success of any individual drug are so grim, betting on any individual drug is foolish, according to Stein. Instead, risks for a large number of potential assets should be pooled, with the eventual winners paying for the losers.

To do this, Stein proposes what he calls a "megafund" - a large collection of assets (candidate therapies). Through some modeling and simulations, Stein suggests some of the qualities of an ideal megafund: it would need in the neighborhood of $3-15 billion to acquire and manage 80-150 drugs. A fund of this size and with these assets would be able to provide an equity yield of about 12%, which would be "right in the investment sweet spot of pension funds and 401(k) plans".

Here's what I find striking about those numbers: let's compare Stein's Megafund to everyone's favorite Megalosaurus, the old-fashioned Big Pharma dinosaur sometimes known as Pfizer:

	Megafund (Stein)	Megalosaurus (Pfizer)
Funding	$3-15 billion	$9 billion estimated 2013 R&D spend
Assets	80-150	81 (in pipeline, plus many more in preclinical)
Return on Equity	12% (estimated)	9.2% (last 10 years) to 13.2% (last 5)

Since Pfizer's a dinosaur, it can't possibly compete with the sleek, modern Megafund, right? Right?

These numbers look remarkably similar. Pfizer - and a number of its peers - are spending Megafund-sized budget each year to shepherd through a Megafund-sized number of compounds. (Note many of Pfizer's peers have substantially fewer drugs in their published pipelines, but they own many times more compounds - the pipeline is just the drugs what they've elected to file an IND on.)

What am I missing here? I understand that a fund is not a company, and there may be some benefits to decoupling asset management decisions from actual operations, but this won't be a tremendous gain, and would presumably be at least partially offset by increased transaction costs (Megafund has to source, contract, manage, and audit vendors to design and run all its trials, after all, and I don't know why I'd think it could do that any more cheaply than Big Pharma can). And having a giant drug pipeline's go/no go decisions made by "financial engineers" rather than pharma industry folks would seem like a scenario that's only really seen as an upgrade by the financial engineers themselves.
A tweet from V.S. Schulz pointed me to a post on Derek Lowe's In the Pipeline blog. which lead to a link to this paper by Stein and 2 others in Nature Biotechnology from a year and a half ago. The authors spend most of their time differentiating themselves from other structures in the technical, financial details rather than explaining why megafund would work better at finding new drugs. However, they definitely think this is qualitatively different from existing pharma companies, and offer a couple reasons. First,

[D]ebt financing can be structured to be more “patient” than private or public equity by specifying longer maturities; 10- to 20-year maturities are not atypical for corporate bonds. ... Such long horizons contrast sharply with the considerably shorter horizons of venture capitalists, and the even shorter quarterly earnings cycle and intra-daily price fluctuations faced by public companies.

I'm not sure where this line of though is coming from. Certainly all big pharma companies' plans extend decades into the future - there may be quarterly earnings reports to file, but that's a force exerted far more on sales and marketing teams than on drug development. The financing of pharmaceutical development is already extremely long term.

Even in the venture-backed world, Stein and team are wrong if they believe there is pervasive pressure to magically deliver drugs in record time. Investors and biotech management are both keenly aware of the tradeoffs between speed and regulatory success. Even this week's came-from-nowhere Cinderella story, Intercept Pharmaceuticals, was founded with venture money over a decade ago - these "longer maturities" are standard issue in biotech. We aren't making iPhone apps here, guys.

Second,

Although big pharma companies are central to the later stages of drug development and the marketing and distributing of approved drugs, they do not currently play as active a role at the riskier preclinical and early stages of development

Again, I'm unsure why this is supposed to be so. Of Pfizer's 81 pipeline compounds, 55 are in Phase 1 or 2 - a ratio that's pretty heavy on early, risky project, and that's not too different from industry as a whole. Pfizer does not publish data on the number of compounds it currently has undergoing preclinical testing, but there's no clear reason I can think of to assume it's a small number.

So, is Megafund truly a revolutionary idea, or is it basically a mathematical deck-chair-rearrangement for the "efficiencies of scale" behemoths we've already got?

[Image: the world's first known dino, Megalosaurus, via Wikipedia.]

• 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

• 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]

The good folks down at eyeforpharma have asked me to write a few blog posts in the run-up to their Patient Centered Clinical Trials conference in Boston this September. In my second article -Buzzword Innovation: The Patient Centricity “Fad” and the Token Patient - I went over some concerns I have regarding the sudden burst of enthusiasm for patient centricity in the clinical trial world.

Apparently, that hit a nerve – in an email, Ulrich Neumann tells me that “your last post elicited quite a few responses in my inbox (varied, some denouncing it as a fad, others strongly protesting the notion, hailing it as the future).”

In preparing my follow up post, I’ve spoken to a couple people on the leading edge of patient engagement:

• Abbe Steel, CEO of HealthiVibe, which is focused on bringing greater patient input into the earliest stages of trial design through focus groups and patient surveys
• Casey Quinlan, co-founder of Patients for Clinical Research, which aims to be a force in patient education and engagement for clinical trials

In addition to their thoughts, eyeforpharma is keenly interested in hearing from more people. They've even posted a survey – from Ulrich:

To get a better idea of what other folks think of the idea, I am sending out a little ad hoc survey. Only 4 questions (so people hopefully do it). Added benefit: There is a massive 50% one-time discount for completed surveys until Friday connected to it as an incentive).

So, here are two things for you to do:

1. Complete the survey and share your thoughts
2. Come to the conference and tell us all exactly what you think

Look forward to seeing you there.

[Conflict of Interest Disclosure: I am attending the Patient Centered Clinical Trials conference. Having everyone saying the same thing at such conferences conflicts with my ability to find them interesting.]

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".

On this date 349 years ago, Samuel Pepys relates in his famous diary a remarkable story about an upcoming medical experiment. As far as I can tell, this is the first written description of a paid research subject.

According to his account, the man (who he describes as “a little frantic”) was to be paid to undergo a blood transfusion from a sheep. It was hypothesized that the blood of this calm and docile animal would help to calm the man.

Some interesting things to note about this experiment:

• Equipoise. There is explicit disagreement about what effect the experimental treatment will have: according to Pepys, "some think it may have a good effect upon him as a frantic man by cooling his blood, others that it will not have any effect at all".
• Results published. An account of the experiment was published just two weeks later in the journal Philosophical Transactions. 
• Medical Privacy. In this subsequent write-up, the research subject is identified as Arthur Coga, a former Cambridge divinity student. According to at least one account, being publicly identified had a bad effect on Coga, as people who had heard of him allegedly succeeded in getting him to spend his stipend on drink (though no sources are provided to confirm this story).
• Patient Reported Outcome. Coga was apparently chosen because, although mentally ill, he was still considered educated enough to give an accurate description of the treatment effect. 

Depending on your perspective, this may also be a very early account of the placebo effect, or a classic case of ignoring the patient’s experience. Because even though his report was positive, the clinicians remained skeptical. From the journal article:

The Man after this operation, as well as in it, found himself very well, and hath given in his own Narrative under his own hand, enlarging more upon the benefit, he thinks, he hath received by it, than we think fit to own as yet.

…and in fact, a subsequent diary entry from Pepys mentions meeting Coga, with similarly mixed impressions: “he finds himself much better since, and as a new man, but he is cracked a little in his head”.

The amount Coga was paid for his participation? Twenty shillings – at the time, that was exactly one Guinea.

[Image credit: Wellcome Images]