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To choose a stratified sample, divide the population into groups called strata and then take a...

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Related Experiment Video

Updated: May 15, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Stratification of randomization is not required for a pre-specified subgroup analysis.

¹Genentech, Inc., 1 DNA Way, South San Francisco, CA 94080, USA. lkaiser@gene.com

Pharmaceutical Statistics

|January 3, 2013

Summary

Stratified randomization is not essential for valid subgroup analyses in clinical trials. Unstratified methods ensure treatment balance and unbiased effect estimation, supporting personalized medicine innovation.

Related Experiment Videos

Last Updated: May 15, 2026

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

Area of Science:

Clinical Trials Methodology
Biostatistics
Pharmacogenomics

Background:

Conflicting recommendations exist regarding the necessity of stratified randomization for subgroup analyses in clinical trials.
Subgroup analyses are crucial for demonstrating drug efficacy, particularly for companion diagnostic development.

Purpose of the Study:

To evaluate the validity of subgroup analyses using typical randomization methods without subgroup stratification.
To assess the impact of unstratified randomization on treatment balance and covariate distribution within subgroups.

Main Methods:

Analysis of typical randomization methods to determine patient fractions within subgroups.
Examination of covariate balance between treatment arms within subgroups.
Application of analysis of variance to assess treatment effect estimators.

Main Results:

The fraction of patients receiving experimental treatment in subgroups closely matches the target fraction.
Covariate values are balanced on average between treatment arms within subgroups.
Variance in covariate imbalance is only slightly increased compared to stratified randomization.
Least-squares treatment effect estimators within subgroups are unbiased regardless of stratification.

Conclusions:

Stratified randomization is not a prerequisite for valid subgroup analyses.
Current randomization methods adequately support subgroup analyses, ensuring treatment balance and unbiased estimates.
Requiring subgroup stratification poses an unnecessary barrier to innovation in personalized healthcare and drug development.