Simple Randomized Experiment

A Simple Randomized Comparative Experiment is a randomized comparative experiment that is an unstratified comparative experiment.

• AKA: Complete Randomization Comparative Experiment, Unstratified Randomized Comparative Experiment.
• Context:
  • It can range from being an Unstratified Randomized Experiment to being an Unstratified Non-Randomized Experiment.
• Counter-Example(s):
  • a Constrained Randomization Experiment, that first separates members into male/female blocks.
• See: Sampling.

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References

2013

• http://en.wikipedia.org/wiki/Randomized_controlled_trial#Simple_randomization
  • This is a commonly used and intuitive procedure, similar to "repeated fair coin-tossing." Also known as "complete" or "unrestricted" randomization, it is robust against both selection and accidental biases. However, its main drawback is the possibility of imbalanced group sizes in small RCTs. It is therefore recommended only for RCTs with over 200 subjects.

1999

• (Kernan et al., 1999) ⇒ Walter N Kernan, Catherine M Viscoli, Robert W Makuch, Lawrence M Brass, and Ralph I Horwitz. (1999). “Stratified Randomization for Clinical Trials." Elsevier. doi:10.1016/S0895-4356(98)00138-3
  • QUOTE: Randomization procedures for clinical trials are intended to create groups of patients that are similar with regard to baseline characteristics that influence prognosis (both known and unknown) other than the treatment being considered. Observed differences between groups in endpoint rates may then be attributed to the treatments rather than to other prognostic features. In simple (or complete) randomization 9, 10 and 11, investigators make up a single randomization list before the trial is begun. Each consecutive patient enrolled receives the next treatment assignment on the list.

    Simple randomization can fail in two important ways. First, it can fail to assign the compared treatments to equal numbers of patients. To avoid this chance imbalance, investigators commonly randomize patients in permuted blocks [8]. A block usually comprises four or six randomly ordered treatment assignments. Within each block, equal numbers of patients receive each treatment. For example, if two treatments, A and B, are being compared, the permuted blocks of four treatment assignments would be AABB, ABAB, BBAA, BABA, ABBA, and BAAB. As patients enter the trial, they receive the next treatment in the current block. Patients are randomized within block after block until the study is complete. With permuted blocks of size four, the number of patients assigned to each treatment within a block can never differ by more than two.

    Second, simple randomization can fail if it creates treated groups of patients that are unbalanced for critical features that are known or suspected to affect prognosis. Significant imbalance is most likely to happen in small trials where chance may result in sicker patients being in one treatment arm than in another. It may also occur for large trials at the time of interim analyses when the full sample size has not been obtained. Finally, it may occur for trials with prolonged recruitment periods, if changes occur over time in prognostic characteristics of enrolled patients.

    To illustrate the chance that simple (unstratified) randomization may lead to treatment groups that are unbalanced with respect to a prognostic factor, consider a trial of two therapies in a disease with an important prognostic factor that is present in 15% of patients. The chance that the two treatment groups will differ by more than 10% for the proportion of patients with the prognostic factor is 33% for a trial of 30 patients, 24% for a trial of 50 patients, 10% for a trial of 100 patients, 3% for a trial of 200 patients, and 0.3% for a trial of 400 patients (Table 1). The chance of an imbalance is greater when prognostic factors are present in 30% of patients than when they are present in 15%.

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