Randomization to randomization probability: Estimating treatment effects under actual conditions of use.
Brandon George,Peng Li,Harris R. Lieberman,Greg Pavela,Andrew W. Brown,Kevin R. Fontaine,Madeline M. Jeansonne,Gareth R. Dutton,Adeniyi J. Idigo,Mariel Parman,Donald B. Rubin,David B. Allison +11 more
TLDR
An experimental design, randomization to randomization probabilities (R2R), is proposed, which significantly improves estimates of treatment effects under actual conditions of use by manipulating participant expectations about receiving treatment.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 Recordread more
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Individualised requirements for optimum treatment of hypothyroidism: complex needs, limited options.
TL;DR: Individualised conditionally defined setpoints may provide appropriate biochemical targets to be clinically tested, together with a stronger focus on clinical presentation and future endpoint markers of tissue thyroid state.
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Time for a reassessment of the treatment of hypothyroidism
John E. M. Midgley,Anthony D. Toft,Rolf Larisch,Johannes W. Dietrich,Johannes W. Dietrich,Rudolf Hoermann +5 more
TL;DR: It appears that the treatment of thyroid disease is witnessing a consequential historic shift, driven by over-reliance on a single laboratory parameter TSH, and the focus on biochemistry rather than patient symptom relief should be re-assessed.
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Toward more rigorous and informative nutritional epidemiology: The rational space between dismissal and defense of the status quo.
Andrew W. Brown,Stella Aslibekyan,Stella Aslibekyan,Dennis M. Bier,Rafael Ferreira da Silva,Adam Hoover,David M. Klurfeld,Eric Loken,Evan Mayo-Wilson,Nir Menachemi,Greg Pavela,D Patrick,Quinn,Dale A. Schoeller,Carmen,Tekwe,Danny,Valdez,Colby J. Vorland,Leah D. Whigham,David B. Allison +20 more
TL;DR: The Rational Space between dismissal and defense of the status quo was discussed at a workshop entitled "Toward more rigorous and informative nutritional epidemiology: the rational space between dismissal, defense and reform" as discussed by the authors.
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Functional and Symptomatic Individuality in the Response to Levothyroxine Treatment.
TL;DR: Considerable intra-individual clustering occurred in the biochemical and symptomatic responses to LT4 treatment, implying statistically multileveled response groups, highlighting clinically distinguishable subgroups within an indiscriminate patient panel.
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Lessons from Randomised Clinical Trials for Triiodothyronine Treatment of Hypothyroidism: Have They Achieved Their Objectives?
TL;DR: The weight of statistical evidence against strong physiological counterarguments is contrasted and areas of improvement for trial design related to validation and sensitivity of QoL instruments, patient selection, statistical power, collider stratification bias, and response heterogeneity to treatment are identified.
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