| Unpredictability | Balanced sample sizes across conditions | Equivalent baseline characteristics across conditions | Cost & complexity |
---|---|---|---|---|
Simple randomization | Best; random assignment prevents predictability | Poor; likely to result in differences across cells | Poor; likely to result in differences across cells | Best; simple to implement |
Stratification with permuted blocks | OK; Random order of assignments within blocks within strata reduces predictability but known block sizes increase predictability | Very good; blocking improves balance, but this is mitigated by stratification | Good; stratification improves equivalence on specific variables | Very good; more complex, but solutions are widely available |
Maximum tolerated imbalance | Very good; random assignment protects against selection bias until big stick is needed. | Very good; results at or below maximum tolerated imbalance of samples | Poor; No better than simple randomization | OK; can be implemented in a range of available software, but requires coding |
Minimal sufficient balance | Very good; random assignment protects against selection bias until biased coin is needed | Poor; No better than simple randomization | Very good; results at or below maximum tolerated inequivalence of covariates | OK; can be implemented in a range of available software, but requires coding |
Minimization | Poor when purely deterministic; improved with incorporation of random element | Very good; should promote balance, depending on algorithm | Best; promotes equivalence on a large number of variables | OK; can be implemented in a range of available software, but requires coding |