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Statistical properties of randomization in clinical trials
1George Washington University, Department of Statistics/Computer and Information Systems, Rockville, Maryland 20852.
Controlled Clinical Trials
|December 1, 1988
Summary
This study examines randomization in clinical trials, detailing statistical properties like treatment imbalance and bias. It emphasizes permutation analysis over population models for robust trial design and analysis.
Area of Science:
- Clinical Trials Methodology
- Biostatistics
- Statistical Inference
Background:
- Randomization is crucial for unbiased clinical trials.
- Understanding the statistical properties of randomization procedures is essential for reliable trial design and analysis.
- Previous research has focused on specific randomization methods, but a comprehensive overview of their statistical properties is needed.
Purpose of the Study:
- To define and discuss the statistical properties of various randomization procedures used in clinical trials.
- To explore the impact of randomization on trial design and statistical analysis.
- To evaluate treatment imbalances, statistical power, and potential biases (selection and accidental) associated with different randomization methods.
Main Methods:
- Review of statistical properties of randomization procedures, including simple, permuted-block, and urn randomization.
- Analysis of probabilities of treatment imbalances and their effect on statistical power.
- Examination of the permutational basis for statistical tests versus population models.
- Description of models for selection bias (Blackwell-Hodges) and accidental bias (Efron).
Main Results:
- Treatment imbalance probabilities are computable for most procedures, including stratified randomization.
- Substantial treatment imbalance is required to significantly affect statistical power.
- Permutation analysis offers a robust basis for statistical tests, requiring fewer assumptions than population models.
- Selection bias is linked to the predictability of treatment allocations, while accidental bias relates to covariate imbalance.
Conclusions:
- Randomization procedures have critical statistical properties influencing clinical trial design and analysis.
- Permutation-based statistical tests are recommended for their fewer assumptions.
- Understanding and quantifying potential biases (selection and accidental) is vital for accurate treatment effect assessment.