Brazen Scofflaws? Are Pharma Companies Really Completely Ignoring FDAAA?

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.

[Image credit: Shamless lawlessness via Flikr user willytronics.]

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

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.]

All Your Site Are Belong To Us

'Competitive enrollment' is exactly that.

This is a graph I tend to show frequently to my clients – it shows the relative enrollment rates for two groups of sites in a clinical trial we'd been working on. The blue line is the aggregate rate of the 60-odd sites that attended our enrollment workshop, while the green line tracks enrollment for the 30 sites that did not attend the workshop. As a whole, the attendees were better enrollers that the non-attendees, but the performance of both groups was declining.

Happily, the workshop produced an immediate and dramatic increase in the enrollment rate of the sites who participated in it – they not only rebounded, but they began enrolling at a better rate than ever before. Those sites that chose not to attend the workshop became our control group, and showed no change in their performance.

The other day, I wrote about ENACCT's pilot program to improve enrollment. Five oncology research sites participated in an intensive, highly customized program to identify and address the issues that stood in the way of enrolling more patients.  The sites in general were highly enthused about the program, and felt it had a positive impact on the operations.

There was only one problem: enrollment didn't actually increase.

Here’s the data:

This raises an obvious question: how can we reconcile these disparate outcomes?

On the one hand, an intensive, multi-day, customized program showed no improvement in overall enrollment rates at the sites.

On the other, a one-day workshop with sixty sites (which addressed many of the same issues as the ENACCT pilot: communications, study awareness, site workflow, and patient relationships) resulted in and immediate and clear improvement in enrollment.

There are many possible answers to this question, but after a deeper dive into our own site data, I've become convinced that there is one primary driver at work: for all intents and purposes, site enrollment is a zero-sum game. Our workshop increased the accrual of patients into our study, but most of that increase came as a result of decreased enrollments in other studies at our sites.

Our workshop graph shows increased enrollment ... for one study. The ENACCT data is across all studies at each site. It stands to reason that if sites are already operating at or near their maximum capacity, then the only way to improve enrollment for your trial is to get the sites to care more about your trial than about other trials that they’re also participating in.

And that makes sense: many of the strategies and techniques that my team uses to increase enrollment are measurably effective, but there is no reason to believe that they result in permanent, structural changes to the sites we work with. We don’t redesign their internal processes; we simply work hard to make our sites like us and want to work with us, which results in higher enrollment. But only for our trials.

So the next time you see declining enrollment in one of your trials, your best bet is not that the patients have disappeared, but rather that your sites' attention has wandered elsewhere.

What (If Anything) Improves Site Enrollment Performance?

ENACCT has released its final report on the outcomes from the National Cancer Clinical Trials Pilot Breakthrough Collaborative (NCCTBC), a pilot program to systematically identify and implement better enrollment practices at five US clinical trial sites. Buried after the glowing testimonials and optimistic assessments is a grim bottom line: the pilot program didn't work.

Here are the monthly clinical trial accruals at each of the 5 sites. The dashed lines mark when the pilots were implemented:

4 of the 5 sites showed no discernible improvement. The one site that did show increasing enrollment appears to have been improving before any of the interventions kicked in.

This is a painful but important result for anyone involved in clinical research today, because the improvements put in place through the NCCTBC process were the product of an intensive, customized approach. Each site had 3 multi-day learning sessions to map out and test specific improvements to their internal communications and processes (a total of 52 hours of workshops). In addition, each site was provided tracking tools and assigned a coach to assist them with specific accrual issues.

That’s an extremely large investment of time and expertise for each site. If the results had been positive, it would have been difficult to project how NCCTBC could be scaled up to work at the thousands of research sites across the country. Unfortunately, we don’t even have that problem: the needle simple did not move.

While ENACCT plans a second round of pilot sites, I think we need to face a more sobering reality: we cannot squeeze more patients out of sites through training and process improvements. It is widely believed in the clinical research industry that sites are low-efficiency bottlenecks in the enrollment process. If we could just "fix" them, the thinking goes – streamline their workflow, improve their motivation – we could quickly improve the speed at which our trials complete. The data from the NCCTBC paints an entirely different picture, though. It shows us that even when we pour large amounts of time and effort into a tailored program of "evidence and practice-based changes", our enrollment ROI may be nonexistent.

I applaud the ENACCT team for this pilot, and especially for sharing the full monthly enrollment totals at each site. This data should cause clinical development teams everywhere to pause and reassess their beliefs about site enrollment performance and how to improve it.

Clinical trials: recent reading recommendations

My recommended reading list -- highlights from the past week:


Absolute required reading for anyone who designs protocols or is engaged in recruiting patients into clinical trials: Susan Guber writes eloquently about her experiences as a participant in cancer clinical trials.
New York Times Well Blog: The Trials of Cancer Trials

Today's #FDAFridayPhoto features Harvey
Wiley, leader of the famed FDA "Poison Squad".

The popular press in India continues to be disingenuous and exploitative in its coverage of clinical trial deaths in that country. (My previous thoughts on that are here.) Kiran Mazumdar-Shaw, an industry leader, has put together an intelligent and articulate antidote.
The Economic Times: Need a rational view on clinical trials


Rahlen Gossen exhibits mastery of the understatement: “Though the Facebook Insights dashboard is a great place to start, it has a few significant disadvantages.” She also provides a good overview of the most common pitfalls you’ll encounter when you try to get good metrics out of your Facebook campaign. 

Rebar Interactive Blog: Metrics Gone Wrong: Facebook “Likes” in Clinical Research

I have not had a chance to watch it yet, but I’m excited to see that theHeart.org has just posted a 7-part video editorial series by Yale’s Harlan Krumholz and Duke Stanford’s Bob Harrington on “a frank discussion on the controversies in the world of clinical trials”. 

theHeart.org: Rethinking clinical trials and research in cardiology

The Case against Randomized Trials is, Fittingly, Anecdotal

I have a lot of respect for Eric Topol, and am a huge fan of his ongoing work to bring new mobile technology to benefit patients.

The Trial of the Future

However, I am simply baffled by this short video he recently posted on his Medscape blog. In it, he argues against the continued use of randomized controlled trials (RCTs) to provide evidence for or against new drugs.

His argument for this is two anecdotes: one negative, one positive. The negative anecdote is about the recently approved drug for melanoma, Zelboraf:

Well, that's great if one can do [RCTs], but often we're talking about needing thousands, if not tens of thousands, of patients for these types of clinical trials. And things are changing so fast with respect to medicine and, for example, genomically guided interventions that it's going to become increasingly difficult to justify these very large clinical trials. 

For example, there was a drug trial for melanoma and the mutation of BRAF, which is the gene that is found in about 60% of people with malignant melanoma. When that trial was done, there was a placebo control, and there was a big ethical charge asking whether it is justifiable to have a body count. This was a matched drug for the biology underpinning metastatic melanoma, which is essentially a fatal condition within 1 year, and researchers were giving some individuals a placebo.

First and foremost, this is simply factually incorrect on a couple extremely important points.

1. Zelboraf was not approved based on any placebo-controlled trials. The phase 1 and phase 2 trials were both single-arm, open label studies. The only phase 3 trial run before FDA approval used dacarbazine in the comparator arm. In fact, of the 34 trials currently listed for Zelboraf on ClinicalTrials.gov, only one has a placebo control: it’s an adjuvant trial for patients whose melanoma has been completely resected, where no treatment may very well be the best option.
2. The Zelboraf trials are not an example of “needing thousands, if not tens of thousands, of patients” for approval. The phase 3 trial enrolled 675 patients. Even adding the phase 1 and 2 trials doesn’t get us to 1000 patients.

Correcting these details take a lot away from the power of this single drug to be a good example of why we should stop using “the sanctimonious [sic] randomized, placebo-controlled clinical trial”.

The second anecdote is about a novel Alzheimer’s Disease candidate:

A remarkable example of a trial of the future was announced in May. For this trial, the National Institutes of Health is working with [Banner Alzheimer's Institute] in Arizona, the University of Antioquia in Colombia, and Genentech to have a specific mutation studied in a large extended family living in the country of Colombia in South America. There is a family of 8000 individuals who have the so-called Paisa mutation, a presenilin gene mutation, which results in every member of this family developing dementia in their 40s. 

Researchers will be testing a drug that binds amyloid, a monoclonal antibody, in just 300 family members. They're not following these patients out to the point of where they get dementia. Instead, they are using surrogate markers to see whether or not the process of developing Alzheimer's can be blocked using this drug. This is an exciting way in which we can study treatments that can potentially prevent Alzheimer's in a very well-demarcated, very restricted population with a genetic defect, and then branch out to a much broader population of people who are at risk for Alzheimer's. These are the types of trials of the future. 

There are some additional disturbing factual errors here – the extended family numbers about 5,000, not 8,000. And estimates of the prevalence of the mutation within that family appear to vary from about one-third to one-half, so it’s simply wrong to state that “every member of this family” will develop dementia.

However, those errors are relatively minor, and are completely overshadowed by the massive irony that this is a randomized, placebo-controlled trial. Only 100 of the 300 trial participants will receive the active study drug, crenezumab. The other 200 will be on placebo.

And so, the “trial of the future” held up as a way to get us out of using randomized, placebo-controlled trials is actually a randomized, placebo-controlled trial itself. I hope you can understand why I’m completely baffled that Topol thinks this is evidence of anything.

Finally, I have to ask: how is this the trial of the future, anyway? It is a short-term study on a highly-selected patient population with a specific genetic profile, measuring surrogate markers to provide proof of concept for later, larger studies. Is it just me, or does that sound exactly like the early lovastatin trials of the mid-1980’s, which tested cholesterol reduction in a small population of patients with severe heterozygous familial hypercholesterolemia? Back to the Future, indeed.


[Image: time-travelling supercar courtesy of Flickr user JoshBerglund19.]

Accelerated Approvals are Too Fast, Except When They're Too Slow

A great article in Medscape reports on two unrelated articles on the FDA’s process for granting (and following up on) Accelerated Approvals of oncology drugs.

First, a very solid review of all oncology drugs approved through the accelerated process since 1992 is in the latest journal of the National Cancer Institute. The review, written by FDA personnel, is in general concerned with the slow pace of confirmatory Phase 3 trials – over a third (18 of 47) have not yet been completed, and even the ones that have completed have taken considerable time. The authors consider process changes and fines as viable means for the FDA to encourage timely completion.

Second, over at the New England Journal of Medicine, Dr Bruce Chabner has a perspective piece that looks at the flip side: he argues that some compounds should be considered even earlier for accelerated approval, using the example of Plexxikon’s much-heralded PLX4032, which showed an amazing 80% response rate in Metastatic Melanoma (albeit in a very small sample of 38 patients).

I would argue that we are just now starting to get enough experience to have a very good conversation about accelerated approval and how to improve it -- still, less than 50 data points (47 approved indications) means that we need to remind ourselves that we're still mostly in the land of anecdote. However, it may be time to ask: how much does delay truly cost us in terms of our overall health? What is the cost of delayed approval (how many patients may potentially suffer from lack of access), and correspondingly what is the cost of premature approval and/or delayed confirmation (how many patients are exposed to ineffective and toxic treatments)?

The good news, to me, is that we're finally starting to collect enough information to make a rational estimate of these questions.

1st-Person Accounts of Trial Participation

Two intriguing articles on participation in clinical trials were published this week. Both happen to be about breast cancer, but both touch squarely on some universal points:

ABC News features patient Haralee Weintraub, who has enrolled in 5 trials in the past 10 years. While she is unusual for having participated in so many studies, Weintraub’s offers great insights into the barriers and benefits of being in the trial, including the fact that many benefits – such as close follow-up and attention from the treatment team -- are not obvious at first.

Meanwhile, the New York Times’ recurring column from Dr Peter Bach on his wife’s breast cancer offers a moving description of her consent into a trial. His essay focuses mainly on the incremental, slow pace of cancer research (“this arduous slog”) and how it is both incredibly frustrating and absolutely necessary for long-term improvements in treatment.