The Rise and Fall of Evidence Based Medicine (1998) [pdf]
(2020) Will COVID-19 be evidence-based medicine’s nemesis?<p><a href="https:&#x2F;&#x2F;journals.plos.org&#x2F;plosmedicine&#x2F;article?id=10.1371&#x2F;journal.pmed.1003266" rel="nofollow">https:&#x2F;&#x2F;journals.plos.org&#x2F;plosmedicine&#x2F;article?id=10.1371&#x2F;jo...</a><p><i>&quot;It is surely time to turn to a more fit-for-purpose scientific paradigm. Complex adaptive systems theory proposes that precise quantification of particular cause-effect relationships is both impossible (because such relationships are not constant and cannot be meaningfully isolated) and unnecessary (because what matters is what emerges in a particular real-world situation). This paradigm proposes that where multiple factors are interacting in dynamic and unpredictable ways, naturalistic methods and rapid-cycle evaluation are the preferred study design. The 20th-century logic of evidence-based medicine, in which scientists pursued the goals of certainty, predictability and linear causality, remains useful in some circumstances (for example, the drug and vaccine trials referred to above). But at a population and system level, we need to embrace 21st-century epistemology and methods to study how best to cope with uncertainty, unpredictability and non-linear causality [16].<p>In a complex system, the question driving scientific inquiry is not “what is the effect size and is it statistically significant once other variables have been controlled for?” but “does this intervention contribute, along with other factors, to a desirable outcome?”. Multiple interventions might each contribute to an overall beneficial effect through heterogeneous effects on disparate causal pathways, even though none would have a statistically significant impact on any predefined variable [11]. To illuminate such influences, we need to apply research designs that foreground dynamic interactions and emergence. These include in-depth, mixed-method case studies (primary research) and narrative reviews (secondary research) that tease out interconnections and highlight generative causality across the system [16, 17].&quot;</i><p>table1: <a href="https:&#x2F;&#x2F;journals.plos.org&#x2F;plosmedicine&#x2F;article?id=10.1371&#x2F;journal.pmed.1003266" rel="nofollow">https:&#x2F;&#x2F;journals.plos.org&#x2F;plosmedicine&#x2F;article?id=10.1371&#x2F;jo...</a>