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Everything about Restricted randomization

In statistics, restricted randomization, also known as constrained randomization, occurs in the design of experiments and in particular in the context of randomized experiments and randomized controlled trials. Restricted randomization allows intuitively poor allocations of treatments to experimental units to be avoided, while retaining the theoretical benefits of randomization. For example, in a clinical trial of a new proposed treatment of obesity compared to a control, an experimenter would want to avoid outcomes of the randomization in which the new treatment was allocated only to the heaviest patients. The goal of improving the balance between the treatment and control groups could be obtained using rerandomization, which is a form of restricted randomization in which allocations that fail a pre-specified covariate-balance criterion are discarded and the units are randomized again; this improves the precision of estimated treatment effects, but the analysis then has to account for the restriction. It has also been argued that the additional choices this involves, such as which covariates to balance and how to adjust p-values, give researchers more room for p-hacking. Wikipedia →

🧭 Start here: a 6-step reading path

  1. Read the overview: Restricted randomizationA 5-minute orientation
  2. Skim a recent paper: Randomized controlled trial for time-restricted eating in healthy volunteers without obesityInfluential work since 2022
  3. Pick up a classic book: Gaudy nightThe most-reprinted book on the subject
  4. Read the landmark paper: A Simple Proof of the Restricted Isometry Property for Random MatricesCited 1,729 times
  5. Go sideways: Glossary of experimental designA closely related topic
  6. Go sideways: Blocking (statistics)A closely related topic

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