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The UK body that oversees health research is writing a new strategy on clinical trial transparency and it wants to hear opinions on it. The Health Research Authority (HRA) says its strategy aims to “make transparency easy, make compliance clear and make information public.” It has opened a public consultation on the strategy and some […]

An AllTrials report for the House of Commons Science and Technology Select Committee this week has found that 33 NHS trust sponsors and six UK universities are reporting none of their clinical trial results, while others have gone from 0% to 100% following an announcement from the Select Committee in January that universities and NHS […]

When university and hospital trusts were called to the UK parliament last year to answer questions on why they were not following the rules on reporting results, we saw how effective the questioning from politicians was. Those of you who watched the parliamentary session saw the pressure the university representatives were put under. Because the politicians asked […]

A judge in New York has ruled that hundreds of clinical trials registered on ClinicalTrials.gov are breaking the law by not reporting results. The ruling came in a court case launched against the US Department of Health and Human Services by two plaintiffs, a family doctor and a professor of journalism. The case focused on […]

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.

---

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.

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.]

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]

It is a surprisingly common peril among analysts: we don’t have the data to answer the question we’re interested in, so we answer a related question where we do have data. Unfortunately, the new answer turns out to shed no light on the original interesting question.

This is sometimes referred to as the Streetlight Effect – a phenomenon aptly illustrated by Mutt and Jeff over half a century ago:

This is the situation that the Tufts Center for the Study of Drug Development seems to have gotten itself into in its latest "Impact Report".  It’s worth walking through the process of how an interesting question ends up in an uninteresting answer.

So, here’s an interesting question:

My company owns a drug that may be approvable through FDA’s 505(b)(2) pathway. What is the estimated time and cost difference between pursuing 505(b)(2) approval and conventional approval?

That’s "interesting", I suppose I should add, for a certain subset of folks working in drug development and commercialization. It’s only interesting to that peculiar niche, but for those people I suspect it’s extremely interesting - because it is a real situation that a drug company may find itself in, and there are concrete consequences to the decision.

Unfortunately, this is also a really difficult question to answer. As phrased, you'd almost need a randomized trial to answer it. Let’s create a version which is less interesting but easier to answer:

What are the overall development time and cost differences between drugs seeking approval via 505(b)(2) and conventional pathways?

This is much easier to answer, as pharmaceutical companies could look back on development times and costs of all their compounds, and directly compare the different types. It is, however, a much less useful question. Many new drugs are simply not eligible for 505(b)(2) approval. If those drugs

Extreme qualitative differences of 505(b)(2) drugs. Source: Thomson Reuters analysis via RAPS

are substantially different in any way (riskier, more novel, etc.), then they will change the comparison in highly non-useful ways. In fact, in 2014, only 1 drug classified as a New Molecular Entity (NME) went through 505(b)(2) approval, versus 32 that went through conventional approval. And in fact, there are many qualities that set 505(b)(2) drugs apart.

So we’re likely to get a lot of confounding factors in our comparison, and it’s unclear how the answer would (or should) guide us if we were truly trying to decide which route to take for a particular new drug. It might help us if we were trying to evaluate a large-scale shift to prioritizing 505(b)(2) eligible drugs, however.

Unfortunately, even this question is apparently too difficult to answer. Instead, the Tufts CSDD chose to ask and answer yet another variant:

What is the difference in time that it takes the FDA for its internal review process between 505(b)(2) and conventionally-approved drugs?

This question has the supreme virtue of being answerable. In fact, I believe that all of the data you’d need is contained within the approval letter that FDA posts publishes for each new approved drug.

But at the same time, it isn’t a particularly interesting question anymore. The promise of the 505(b)(2) pathway is that it should reduce total development time and cost, but on both those dimensions, the report appears to fall flat.

• Cost: This analysis says nothing about reduced costs – those savings would mostly come in the form of fewer clinical trials, and this focuses entirely on the FDA review process.
• Time: FDA review and approval is only a fraction of a drug’s journey from patent to market. In fact, it often takes up less than 10% of the time from initial IND to approval. So any differences in approval times will likely easily be overshadowed by differences in time spent in development. 

But even more fundamentally, the problem here is that this study gives the appearance of providing an answer to our original question, but in fact is entirely uninformative in this regard. The accompanying press release states:

The 505(b)(2) approval pathway for new drug applications in the United States, aimed at avoiding unnecessary duplication of studies performed on a previously approved drug, has not led to shorter approval times.

This is more than a bit misleading. The 505(b)(2) statute does not in any way address approval timelines – that’s not it’s intent. So showing that it hasn’t led to shorter approval times is less of an insight than it is a natural consequence of the law as written.

Most importantly, showing that 505(b)(2) drugs had a longer average approval time than conventionally-approved drugs in no way should be interpreted as adding any evidence to the idea that those drugs were slowed down by the 505(b)(2) process itself. Because 505(b)(2) drugs are qualitatively different from other new molecules, this study can’t claim that they would have been developed faster had their owners initially chosen to go the route of conventional approval. In fact, such a decision might have resulted in both increased time in trials and increased approval time.

This study simply is not designed to provide an answer to the truly interesting underlying question.

[Disclosure: the above review is based entirely on a CSDD press release and summary page. The actual report costs $125, which is well in excess of this blog’s expense limit. It is entirely possible that the report itself contains more-informative insights, and I’ll happily update that post if that should come to my attention.]

Maybe those pesky sites are good for something after all. 

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

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

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

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

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

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

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

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

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

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

Maybe it’s time to stop blaming the sites? To be fair, they didn’t exactly set the world on fire – and I’m guessing the total cost of activating the 8 sites significantly exceeded the costs of setting up the virtual recruitment and patient logistics. But still, the site-less, “patient centric” approach once again came up astonishingly short.
Krischer J, Cronholm PF, Burroughs C, McAlear CA, Borchin R, Easley E, Davis T, Kullman J, Carette S, Khalidi N, Koening C, Langford CA, Monach P, Moreland L, Pagnoux C, Specks U, Sreih AG, Ytterberg S, Merkel PA, & Vasculitis Clinical Research Consortium. (2017). Experience With Direct-to-Patient Recruitment for Enrollment Into a Clinical Trial in a Rare Disease: A Web-Based Study. Journal of medical Internet research, 19 (2) PMID: 28246067

Your daily light exposure impacts your health. A new study finds that too much light at night and not enough natural light during the day can be harmful. This story first aired on Morning Edition on Nov. 4, 2024.

Hospitals have been forced to innovate with new ways of hydrating patients and giving them medications, after a key factory that produces IV fluid bags flooded during Hurricane Helene. (This story first aired on Morning Edition on Nov. 7, 2024.)

A person has tested positive in British Columbia, Canadian health officials said, though the results must be sent to another lab for confirmation.

President-elect Donald Trump tried unsuccessfully to get rid of the Affordable Care Act during his first term. What action will he take this time around?

More people are getting cancer in their 20s, 30s, and 40s, and surviving, thanks to rapid advancement in care. Many will have decades of life ahead of them, which means they face greater and more complex challenges in survivorship. Lourdes Monje is navigating these waters at age 29.

NPR's Leila Fadel talks with actor Patrick Dempsey about his efforts to raise money for cancer treatment and prevention.

Sudan's civil war has displaced 10 million citizens. Here are profiles of two young people from the most vulnerable groups: an unaccompanied minor caring for twin brothers, a woman who was raped.

Participant reported relief from chronic low back pain and reduced need for pain-relief medications.

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