No trial stands alone


“The result of this trial speaks for itself!”

This often heard phrase contains a troubling assumption: That an experiment can stand entirely on in its own. That it can be interpreted without reference to other trials and other results. In a couple of articles published over the last two weeks, my co-authors and I deliver a one-two punch to this idea.

The first punch is thrown at the US FDA’s use of “assay sensitivity,” a concept defined as a clinical trial’s “ability to distinguish between an effective and an ineffective treatment.” This concept is intuitively appealing, since all it seems to say is that a trial should be well-designed. A well-designed clinical trial should be able to answer its question and distinguish an effective from an ineffective treatment. However, assay sensitivity has been interpreted to mean that placebo controls are “more scientific” than active controls. This is because superiority to placebo seems to guarantee that the experimental agent is effective, whereas superiority or equivalence to an active control does not rule out the possibility that both agents are actually ineffective.  This makes placebo-controlled trials more “self-contained,” easier to interpret, and therefore, methodologically superior.

In a piece in Perspectives in Biology and Medicine, Charles Weijer and I dismantle the above argument by showing, first, that all experiments rely on some kinds of “external information”–be it information about an active control’s effects, pre-clinical data, the methodological validity of various procedures, etc. Second, that a placebo can suffer from all of the same woes that might afflict an active control (e.g., the “placebo effect” is not one, consistent effect, but can vary depending upon the type or color of placebo used), so there is no guarantee of assay sensitivity in a placebo-controlled trial. And finally, the more a trial’s results can be placed into context, and interpreted in light of other trials, the more potentially informative it is.

This leads to punch #2: How should we think about trials in context? In a piece in Trials, Charles Heilig, Charles Weijer, and I present the “Accumulated Evidence and Research Organization (AERO) Model,” a graph-theoretic approach to representing the sequence of experiments and clinical trials that constitute a translational research program. The basic idea is to illustrate each trial in the context of its research trajectory using a network graph (or a directed acyclic graph, if you want to get technical), with color-coded nodes representing studies and their outcomes; and arrows representing the intellectual lineage between studies. This work is open-access, so I won’t say too much more about it here, but instead encourage you to go and give it a look. We provide a lot of illustrations to introduce the graphing algorithm, and then apply the approach to a case-study involving inconsistent results across a series of tuberculosis trials.

In sum: Trials should not be thought of as self-contained. This is not even desirable! Rather, all trials (or at least trials in translational medicine) should be thought of as nodes in a complex, knowledge producing network. Each one adding something to our understanding. But none ever truly “speaks for itself,” because none should ever stand alone.


    title = {No trial stands alone},
    journal = {STREAM research},
    author = {Spencer Phillips Hey},
    address = {Montreal, Canada},
    date = 2013,
    month = jun,
    day = 16,
    url = {}


Spencer Phillips Hey. "No trial stands alone" Web blog post. STREAM research. 16 Jun 2013. Web. 19 Jul 2024. <>


Spencer Phillips Hey. (2013, Jun 16). No trial stands alone [Web log post]. Retrieved from


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