Learning From Major Clinical Trials
Bending Without Breaking
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- clinical decision making
- clinical trial
- early medical intervention
- randomized controlled trials as topic
- treatment outcome
We are drowning in information while starving for wisdom.
From basic investigations, case reports, observational analyses, and randomized controlled trials (RCTs), the pace of generating and publishing medical information continues to accelerate at an overwhelming rate.1 Separating the wisdom from the rapidly growing cacophony of less dependable to actually unreliable material represents an increasing challenge. Across the spectrum of medical information, the results of RCTs are considered less susceptible to bias and more reliable. However, RCTs range in quality and reliability.
The preponderance of RCTs are small, early-phase studies, a substantial portion of which do not even result in a publication.2 Those designed to test whether an intervention can alter clinical outcomes are considerably larger and more resource-intense. These major clinical-outcome RCTs are the handful that provides estimates of benefits, and harms of a prospective intervention, as well. The most elite outcome RCTs offer results that inform clinical decisions.
Despite the intensity of the effort and extensive information collected in these relatively uncommon clinical-outcome RCTs, all too frequently, their results are conveyed by a single word, either positive or negative. With a statistically nonsignificant primary objective, the seemingly less harsh neutral designation can be equally dismissive. Admittedly, it is essential for a valid statistical construct to first either reject or accept the null hypothesis for the declared (registered) primary objective using the prespecified analysis plan. However, that critical primary analysis conveys only a fraction of the information generated from such a major effort.
When the primary objective of a RCT is met (positive), further hierarchical statistical testing of secondary analyses provides a legitimate statistical framework to …