INTRODUCTION
A central aspect of scientific inference is distinguishing coincidence
from cause and effect. David Hume – one of the greatest philosophers of
all times – maintained that this is empirically
impossible.1 Methodological development during last
50-70 years have identified randomization as a necessary condition to
establish the relationship between cause and effect2-4, such as ascertaining treatment effects tested in
randomized controlled trials (RCTs).5 Since the early
1960’s 3,6, regulators such as Food and Drug
Administration (FDA) and later on, the European Medicines Agency (EMA)
have typically required RCTs to approve new treatments for use in
medical practice. However, developments in basic science and
signals can be identified without testing in RCTs. 7An unintended consequence of these developments is that a formal
mechanism for rigorous cause-effect inferences is removed and, by and
large, cannot be reliably compensated by using techniques based on
non-randomized comparisons.8 Nevertheless, under some
circumstances, the therapeutic signal observed in non-RCTs testing is
accepted as “truthful”.9 This, for example, occurs
when effects of treatments are so large (“dramatic”) that they are
believed to override the combined effects of biases and random errors
that potentially affect the study’s results.9 The
mechanism for accepting such results as “true” resides in the way our
minds distinguish these signals as “true” from “false”. A
theoretical framework for such inferences can be postulated within the
heuristic theory of decision-making10,11 linked with
signal detection theory (SDT).12,13 Indeed, when
formal methods such as RCTs are not available people resort to
heuristics.10 Here we argue that inference about
dramatic effects is driven by two heuristics: first, the large effects
must be recognized (recognition heuristic) and second, the magnitude of
that effect to cross the decision threshold reflects the heuristic
according to the Weber-Fechner law.
In this paper, we propose how SDT-related heuristics can be used to
interpret the results of non-RCT comparisons of drug approvals by the
FDA and EMA. We believe that wider familiarity with these principles has
important educational values for trainees, practicing physicians,
researchers, and policy-makers.