Thinking outside the cylinder: on the use of clinical trial registries in evidence synthesis communities

Clinical trials take a long time to be published, if they are at all. And when they are published, most of them are either missing critical information or have changed the way they describe the outcomes to suit the results they found (or wanted). Despite these problems, the vast majority of the new methods and technologies that we build in biomedical informatics to improve evidence synthesis remain focused on published articles and bibliographic databases.

Simply: no matter how accurately we can convince a machine to screen the abstracts of published articles for us, we are still bound by what doesn’t appear in those articles.

We might assume that published articles are the best source of clinical evidence we have. But there is an obvious alternative. Clinical trial registries are mature systems that have been around for more than a decade, their use is mandated by law and policy for many countries and organisations, and they tell us more completely and more quickly what research should be available. With new requirements for results reporting coming into effect, more and more trials have structured summary results available (last time I checked ClinicalTrials.gov, it was 7.8 million participants from more than 20,000 trials, and that makes up more than 20% of all completed trials).

The reality is that not all trials are registered, not all registered trials are linked to their results, and not all results are posted in clinical trial registries. And having some results available in a database doesn’t immediately solve the problems of publication bias, outcome reporting bias, spin, and failure to use results to help design replication studies.

Aside: people working on Registered Reports have been looking at this for much less time (since 2013). Some of the more zealous young open science types decided this was a wild innovation that will variously “eliminate” or “fix” publication bias and all manner of other issues in scientific research. It won’t. Not immediately. But those of us who understand the history of prospective registration from clinical trial research can help to explain why it is much more complicated than it seems, and can help the rest of science catch up on the biases and unintended consequences that will appear as they further implement prospective registration in practice.

In a systematic review of the processes used to link clinical trial registries to published articles, we found that the proportions of trials that had registrations was about the same as the proportion of registrations that had publications (when they were checked properly, not the incorrect number of 8.7 million patients you might have heard about). Depending on whether you are an optimist or a pessimist, you can say that what is available in clinical trial registries is just as good/bad as what is available in bibliographic databases.

Beyond that, the semi-structured results that are available in ClinicalTrials.gov are growing rapidly (by volume and proportion). The results data (a) help us to avoid some of the biases that appear in published research; (b) can appear earlier; (c) can be used to reconcile published results; and (d) as it turns out, make it much easier to convince a machine to screen for trial registrations that meet a certain set of inclusion criteria.

I suspect that the assumption that clinical trial registries are less useful than the published literature is a big part of the reason why nearly all of the machine learning and other natural language processing research in the area is still stuck on published articles. But that is a bad assumption.

Back in 2012, we wrote in Science Translational Medicine about how to build an open community of researchers to build and grow a repository of structured clinical trial results data. The following is based on that list but with nearly 6 years of hindsight:

  • Make it accessible: And not just in the sense of being open, but by providing tools to make it easier to access results data; tools that support data extraction, searching, screening, synthesis. We already have lots of tools in this space that were developed to work with bibliographic databases, and many could easily be modified to work with results data from clinical trial registries.
  • Make all tools available to everyone: The reason why open source software communities work so well is that by sharing, people build tools on top of other tools on top of methods. It was an idea of sharing that was borne of necessity back when computers were scarce, slow, and people had to make the most of their time with them. Tools for searching, screening, cleaning, extracting, and synthesising should be made available to everyone via simple user interfaces.
  • Let people clean and manage data together: Have a self-correcting mechanism that allows people to update and fix problems with the metadata representing trials and links to articles and systematic reviews, even if the trial is not their own. And share those links, because there’s nothing worse than duplicating data cleaning efforts. If the Wikipedia/Reddit models don’t work, there are plenty of others.
  • Help people allocate limited resources: If we really want to reduce the amount of time it takes to identify unsafe or ineffective interventions, we need to support the methods and tools that help the whole community identify the questions that are most in need of answers, and decide together how best to answer them, rather than competing to chase bad incentives like money and career progression. Methods for spotting questions with answers that may be out of date should become software tools that anyone can use.
  • Make it ethical and transparent: There are situations where data should not be shared, especially when we start thinking about including individual participant data in addition to summary data. There are also risks that people may use the available data to tell stories that are simply not true. Risks related to ethics, privacy, and biases need to be addressed and tools need to be validated carefully to help people avoid mistakes whenever possible.

We are already starting to do some of this work in my team. But there are so many opportunities for people in biomedical informatics to think beyond the bibliographic databases, and to develop new methods that will transform the way we do evidence synthesis. My suggestion: start with the dot points above. Take a risk. Build the things that will diminish the bad incentives and support the good incentives.

Media collection about conflicts of interest in systematic reviews of neuraminidase inhibitors

As usual, I’m keeping a record of major stories in the media related to a recently published paper. I will continue to update this post to reflect the media response to our article in the Annals of Internal Medicine.

When I checked last (1 May 2015), the Altmetric score was 112. Here’s the low-tech way to check that…

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Neuropsych trials involving kids are designed differently when funded by the companies that make the drugs

Over the short break that divided 2013 and 2014, we had a new study published looking at the designs of neuropsychiatric clinical trials that involve children. Because we study trial registrations and not publications, many of the trials that are included in the study are yet to be published, and it is likely that quite a few will never be published.

Neuropsychiatric conditions are a big deal for children and make up a substantial proportion of the burden of disease. In the last decade or so, more and more drugs are being prescribed to children to treat ADHD, depression, autism spectrum disorders, seizure disorders, and a few others. The major problem we face in this area right now is the lack of evidence to help guide the decisions that doctors make with their patients and their patients’ families. Should kids be taking Drug A? Why not Drug B? Maybe a behavioural intervention? A combination of these?

I have already published a few things about how industry and non-industry funded clinical trials are different. To look at how clinical trials differ based on who funds them, we often use the clinicaltrials.gov registry, which currently provides information for about 158K registered trials and is made up of about half US trials, and half trials that are conducted entirely outside the US.

Some differences are generally expected (by cynical people like me) because of the different reasons why industry and non-industry groups decide to do a trial in the first place. We expect that industry trials are more likely to look at their own drugs, the trials are likely to be shorter, more focused on the direct outcomes related to what the drug claims to do (e.g. lower cholesterol rather than reduce cardiovascular risk), and of course they are likely to be designed to nearly always produce a favourable result for the drug in question.

For non-industry groups, there is a kind of hope that clinical trials funded by the public will be for the public good – to fill in the gaps by doing comparative effectiveness studies (where drugs are tested against each other, rather than against a placebo or in a single group) whenever they are appropriate, to focus on the real health outcomes of the populations, and to be capable of identifying risk-to-benefit ratios for drugs that have had questions raised about safety.

The effects of industry sponsorship on clinical trial designs for neuropsychiatric drugs in children

So those differences you might expect to see between industry and non-industry are not quite what we found in our study. For clinical trials that involve children and test drugs used for neuropsychiatric conditions, there really isn’t that much difference between what the industry choose to study and what everyone else does. So even though we did find that industry is less likely to undertake comparative effectiveness trials for these conditions, and the different groups tend to study completely different drugs, the striking result is just how little comparative effectiveness research is being done by both groups.

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A network view of the drug trials undertaken for ADHD by industry (black) and non-industry (blue) groups – each drug is a node in the network; lines between them are the direct comparisons from trials with active comparators.

To make a long story short, it doesn’t look like either side are doing a very good job of systematically addressing the questions that doctors and their patients really need answered in this area.

Some of the reasons for this probably include the way research is funded (small trials might be easier to fund and undertake), the difficulties associated with acquiring ethics and recruiting children to be involved in clinical trials, and the complexities of testing behavioural therapies and other non-drug interventions against and with drugs.

Of course, there are other good reasons for undertaking trials that involve a single group or only test against a placebo (including safety and ethical reasons)… but for conditions like seizure disorders, where there are already approved standard therapies that are known to be safe, it is quite a shock to see that nearly all of the clinical trials undertaken for seizure disorders in children are placebo-controlled or are tested only in a single group.

What should be done?

To really improve the way we produce and then synthesise evidence for children, we really need to consider much more cooperation and smarter trial designs that will actually fill the gaps in knowledge and help doctors make good decisions. It’s true that it is very hard to fund and successfully undertake a big coordinated trial even when it doesn’t involve children, but the mess of clinical trials that are being undertaken today often seem to be for other purposes – to get a drug approved, to expand a market, to fill a clinician-scientist’s CV – or are constrained to the point where the design is too limited to be really useful. And these problems flow directly into synthesis (systematic reviews and guidelines) because you simply can’t review evidence that doesn’t exist.

I expect that long-term clinical trials that take advantage of electronic medical records, retrospective trials, and observational studies involving heterogeneous sets of case studies will come back to prominence for as long as the evidence produced by current clinical trials is hampered by compromised design, resource constraints, and a lack of coordinated cooperation. We really do need better ways to know which questions need to be answered first, and to find better ways to coordinate research among researchers (and patient registries). Wouldn’t it be nice if we knew exactly which clinical trials are most needed right now, and we could organise ourselves into large-enough groups to avoid redundant and useless trials that will never be able to improve clinical decision-making?