Potential Advantages and Disadvantages of Stratification in Methods of Randomization

Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 114)

Abstract

Randomization of patients to different therapy groups has been established to a gold standard in clinical trials. But pre-stratification of randomization has been discussed as a controversial issue in this context. To support investigator’s decision concerning stratification in the phase of planning a trial we investigated the impact of stratification with respect to the risk of prognostic imbalance between treatment groups by a simulation approach. We give a comprehensive overview of the risk for pre-defined imbalances, several trial sizes and prevalence of a prognostic factor, comparing stratified vs. unstratified randomization.We quantified the maximum risk of a prognostic imbalance due to randomization of 59 % (complete randomization CR, N = 30, prevalence of a prognostic factor 50 %). For type I error we calculated a maximum of 32 % (permuted-block randomization PBR(B), N = 100, average success rate 50 %) for a clinically relevant difference, and about 5 % for a statistically significant difference in trials with N = 100 or 400 patients. Stratification can be helpful to reduce this risk by up to 16 percentage points (pps) for clinical differences in the case of a large average success rate of 50 %, and large differences between the strata (10 % vs. 90 %) in small trials of N = 100. For statistical differences, however, the impact of stratification is rather negligible.

Keywords

Stratification 

References

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    Kernan, W.N., et al.: Stratified randomization for clinical trials. J. Clin. Epidemiol. 52(1), 19–26 (1999)CrossRefGoogle Scholar
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    Feinstein, A.R., Landis, J.R.: The role of prognostic stratification in preventing the bias permitted by random allocation of treatment. J. Chronic Dis. 29(4), 277–284 (1976)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute for Biostatistics and Informatics in Medicine and Ageing ResearchUniversity Medicine RostockRostockGermany

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