Randomization to randomization probability: Estimating treatment effects under actual conditions of use.

Abstract

Blinded randomized controlled trials (RCT) require participants to be uncertain if they are receiving a treatment or placebo. Although uncertainty is ideal for isolating the treatment effect from all other potential effects, it is poorly suited for estimating the treatment effect under actual conditions of intended use-when individuals are certain that they are receiving a treatment. We propose an experimental design, randomization to randomization probabilities (R2R), which significantly improves estimates of treatment effects under actual conditions of use by manipulating participant expectations about receiving treatment. In the R2R design, participants are first randomized to a value, π, denoting their probability of receiving treatment (vs. placebo). Subjects are then told their value of π and randomized to either treatment or placebo with probabilities π and 1-π, respectively. Analysis of the treatment effect includes statistical controls for π (necessary for causal inference) and typically a π-by-treatment interaction. Random assignment of subjects to π and disclosure of its value to subjects manipulates subject expectations about receiving the treatment without deception. This method offers a better treatment effect estimate under actual conditions of use than does a conventional RCT. Design properties, guidelines for power analyses, and limitations of the approach are discussed. We illustrate the design by implementing an RCT of caffeine effects on mood and vigilance and show that some of the actual effects of caffeine differ by the expectation that one is receiving the active drug. (PsycINFO Database Record

Time for a reassessment of the treatment of hypothyroidism

“…The expression of dissatisfaction may be partly explained by raised awareness of the problem, based on unspecific subjective criteria, and the possible contribution of a lack in certainty of the diagnostic process discussed above [11, 29–45]. Patient expectations introduce a confounding influence on perceived outcomes [11, 53, 54]. This is difficult to address, particularly since expectation bias extends to RCTs, regarded as the highest class of evidence in Evidenced-Based Medicine [53].…”

“…Patient expectations introduce a confounding influence on perceived outcomes [11, 53, 54]. This is difficult to address, particularly since expectation bias extends to RCTs, regarded as the highest class of evidence in Evidenced-Based Medicine [53]. A conflict arises between Evidenced-Based Medicine and FDA regulations, the latter mandating that drug evaluation is strictly done under conditions of actual use [53, 55].…”

Lessons from Randomised Clinical Trials for Triiodothyronine Treatment of Hypothyroidism: Have They Achieved Their Objectives?

“…Importantly, while evidence-based medicine has traditionally based stronger emphasis on controlled outcomes achieved by RCTs, the treatment effect under actual “conditions of use” is the determinant of approval for a drug by the American Food and Drug Administration (FDA) [ 54 ]. RCTs may not actually meet this FDA criterium, as has been elegantly demonstrated by George et al [ 55 ] from a statistical point of view. The very randomisation required to discriminate the treatment effect from other confounding influences may cause RCTs to poorly predict the actual treatment effect under conditions of intended use [ 55 ].…”

“…RCTs may not actually meet this FDA criterium, as has been elegantly demonstrated by George et al [ 55 ] from a statistical point of view. The very randomisation required to discriminate the treatment effect from other confounding influences may cause RCTs to poorly predict the actual treatment effect under conditions of intended use [ 55 ]. In RCTs, unlike under actual conditions of use where patients know about treatment, patients are uncertain whether they are receiving treatment.…”

“…This introduces a bias, as patient expectations of improvement substantially impact outcomes and effect sizes of the treatment under actual conditions of use. Improvements in trial design (randomisation to randomisation probabilities, R2R) have been suggested to adjust for participant expectancy about receiving treatment and produce treatment estimates closer to those under actual conditions of use, while retaining core strengths of the RCT [ 55 ]. This may be particularly important to consider in QoL trials where patient expectations may markedly influence the subjective treatment outcomes.…”