Stratified randomization for clinical trials

J Clin Epidemiol. 1999 Jan;52(1):19-26. doi: 10.1016/s0895-4356(98)00138-3.


Trialists argue about the usefulness of stratified randomization. For investigators designing trials and readers who use them, the argument has created uncertainty regarding the importance of stratification. In this paper, we review stratified randomization to summarize its purpose, indications, accomplishments, and alternatives. In order to identify research papers, we performed a Medline search for 1966-1997. The search yielded 33 articles that included original research on stratification or included stratification as the major focus. Additional resources included textbooks. Stratified randomization prevents imbalance between treatment groups for known factors that influence prognosis or treatment responsiveness. As a result, stratification may prevent type I error and improve power for small trials (<400 patients), but only when the stratification factors have a large effect on prognosis. Stratification has an important effect on sample size for active control equivalence trials, but not for superiority trials. Theoretical benefits include facilitation of subgroup analysis and interim analysis. The maximum desirable number of strata is unknown, but experts argue for keeping it small. Stratified randomization is important only for small trials in which treatment outcome may be affected by known clinical factors that have a large effect on prognosis, large trials when interim analyses are planned with small numbers of patients, and trials designed to show the equivalence of two therapies. Once the decision to stratify is made, investigators need to chose factors carefully and account for them in the analysis.

Publication types

  • Research Support, U.S. Gov't, P.H.S.
  • Review

MeSH terms

  • Bias
  • Data Interpretation, Statistical
  • Effect Modifier, Epidemiologic
  • Guidelines as Topic
  • Humans
  • Prognosis
  • Random Allocation*
  • Randomized Controlled Trials as Topic*
  • Reproducibility of Results
  • Research Design
  • Treatment Outcome