Twitter outraged like only Twitter could on January 22 over a strange editorial that appeared in the prestigious New England Journal of Medicine, calling for medical researchers to not make their research data public. The call comes at a time when the scientific publishing zeitgeist is slowly but surely shifting toward journals requiring, sometimes mandating, the authors of studies to make their data freely available so that their work can be validated by other researchers.
Through the editorial, written by Dan Longo and Jeffrey Drazen, both doctors and the latter the chief editor, NEJM also cautions medical researchers to be on the lookout for ‘research parasites’, a coinage that the journal says is befitting “of people who had nothing to do with the design and execution of the study but use another group’s data for their own ends, possibly stealing from the research productivity planned by the data gatherers, or even use the data to try to disprove what the original investigators had posited”. As @omgItsEnRIz tweeted, do the authors even science?
The choice of words is more incriminating than the overall tone of the text, which also tries to express the more legitimate concern of replicators not getting along with the original performers. However, by saying that the ‘parasites’ may “use the data to try to disprove what the original investigators had posited”, NEJM has crawled into an unwise hole of infallibility of its own making.
In October 2015, a paper published in the Journal of Experimental Psychology pointed out why replication studies are probably more necessary than ever. The misguided publish-or-perish impetus of scientific research, together with publishing in high impact-factor journals being lazily used as a proxy for ‘good research’ by many institutions, has led researchers to hack their results – i.e. prime them (say, by cherry-picking) so that the study ends up reporting sensational results when, really, duller ones exist.
The JEP paper had a funnel plot to demonstrate this. Quoting from the Neuroskeptic blog, which highlighted the plot when the paper was published, “This is a funnel plot, a two-dimensional scatter plot in which each point represents one previously published study. The graph plots the effect size reported by each study against the standard error of the effect size – essentially, the precision of the results, which is mostly determined by the sample size.” Note: the y-axis is running top-down.
The paper concerned itself with 43 previously published studies discussing how people’s choices were perceived to change when they were gently reminded about sex.
As Neuroskeptic goes on to explain, there are three giveaways in this plot. One is obvious – that the distribution of replication studies is markedly separated from that of the original studies. Second: the least precise results from the original studies worked with the larger sample sizes. Third: the original studies all seemed to “hug” the outer edge of the grey triangles, which represents a statistical measure responsible for indicating if some results are reliable. The uniform ‘hugging’ is an indication that all those original studies were likely guilty of cherry-picking from their data to conclude with results that are just about reliable, an act called ‘p-hacking’.
A line of research can appear to progress rapidly but without replication studies it’s difficult to establish if the progress is meaningful for science – a notion famously highlighted by John Ioannidis, a professor of medicine and statistics at Stanford University, in his two landmark papers in 2005 and 2014. Björn Brembs, a professor of neurogenetics at the Universität Regensburg, Bavaria, also pointed out how the top journals’ insistence on sensational results could result in a congregation of unreliability. Together with a conspicuous dearth of systematically conducted replication studies, this ironically implies that the least reliable results are often taken the most seriously thanks to the journals they appear in.
The most accessible sign of this is a plot between the retraction index and the impact factor of journals. The term ‘retraction index’ was coined in the same paper in which the plot first appeared; it stands for “the number of retractions in the time interval from 2001 to 2010, multiplied by 1,000, and divided by the number of published articles with abstracts”.
Look where NEJM is. Enough said.
The journal’s first such supplication appeared in 1997, then writing against pre-print copies of medical research papers becoming available and easily accessible – á la the arXiv server for physics. Then, the authors, again two doctors, wrote, “medicine is not physics: the wide circulation of unedited preprints in physics is unlikely to have an immediate effect on the public’s well-being even if the material is biased or false. In medicine, such a practice could have unintended consequences that we all would regret.” Though a reasonable PoV, the overall tone appeared to stand against the principles of open science.
More importantly, both editorials, separated by almost two decades, make one reasonable argument that sadly appears to make sense to the journal only in the context of a wider set of arguments, many of them contemptible. For example, Drazen seems to understand the importance of data being available for studies to be validated but has differing views on different kinds of data. Two days before his editorial was published, another appeared co-authored by 16 medical researchers – Drazen one of them – in the same journal, this time calling for anonymised patient data from clinical trials being made available to other researchers because it would “increase confidence and trust in the conclusions drawn from clinical trials. It will enable the independent confirmation of results, an essential tenet of the scientific process.”
(At the same time, the editorial also says, “Those using data collected by others should seek collaboration with those who collected the data.”)
For another example, NEJM labours under the impression that the data generated by medical experiments will not ever be perfectly communicable to other researchers who were not involved in the generation of it. One reason it provides is that discrepancies in the data between the original group and a new group could arise because of subtle choices made by the former in the selection of parameters to evaluate. However, the solution doesn’t lie in the data being opaque altogether.
A better way to conduct replication studies
An instructive example played out in May 2014, when the journal Social Psychology published a special issue dedicated to replication studies. The issue contained both successful and failed attempts at replicating some previously published results, and the whole process was designed to eliminate biases as much as possible. For example, the journal’s editors Brian Nosek and Daniel Lakens didn’t curate replication studies but instead registered the studies before they were performed so that their outcomes would be published irrespective of whether they turned out positive or negative. For another, all the replications used the same experimental and statistical techniques as in the original study.
One scientist who came out feeling wronged by the special issue was Simone Schnall, the director of the Embodied Cognition and Emotion Laboratory at Cambridge University. The results of a paper co-authored by Schnall in 2008 hadfailed to be replicated, but she believed there had been a mistake in the replication that, when corrected, would corroborate her group’s findings. However, her statements were quickly and widely interpreted to mean she was being a “sore loser”. In one blog, her 2008 findings were called an “epic fail” (though the words were later struck out).
This was soon followed a rebuttal by Schnall, followed by a counter by the replicators, and then Schnall writing two blog posts (here and here). Over time, the core issue became how replication studies were conducted – who performed the peer review, the level of independence the replicators had, the level of access the original group had, and how journals could be divorced from having a choice about which replication studies to publish. But relevant to the NEJM context, the important thing was the level of transparency maintained by Schnall & co. as well as the replicators, which provided a sheen of honesty and legitimacy to the debate.
The Social Psychology issue was able to take the conversation forward, getting authors to talk about the psychology of research reporting. There have been few other such instances – of incidents exploring the proper mechanisms of replication studies – so if the NEJM editorial had stopped itself with calling for better organised collaborations between a study’s original performers and its replicators, it would’ve been great. As Longo and Drazen concluded, “How would data sharing work best? We think it should happen symbiotically … Start with a novel idea, one that is not an obvious extension of the reported work. Second, identify potential collaborators whose collected data may be useful in assessing the hypothesis and propose a collaboration. Third, work together to test the new hypothesis. Fourth, report the new findings with relevant coauthorship to acknowledge both the group that proposed the new idea and the investigative group that accrued the data that allowed it to be tested.”
The mistake lies in thinking anything else would be parasitic. And the attitude affects not just other scientists but some science communicators as well. Any journalist or blogger who has been reporting on a particular beat for a while stands to become a ‘temporary expert‘ on the technical contents of that beat. And with exploratory/analytical tools like R – which is easier than you think to pick up – the communicator could dig deeper into the data, teasing out issues more relevant to their readers than what the accompanying paper thinks is the highlight. Sure, NEJM remains apprehensive about how medical results could be misinterpreted to terrible consequence. But the solution there would be for the communicators to be more professional and disciplined, not for the journal to be more opaque.
January 24, 2016