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Published on: February 11, 2019
Further results on a multiple-testing procedure for clinical trials
Abstract:
O'Brien and Fleming (1979, Biometrics 35, 549-556) proposed a procedure for multiple testing in clinical trials for acute disease. We evaluated their procedure for chronic-disease models and found that it can be used without modification. Type I errors are unaltered and no loss of power is seen in comparison with single-sample procedures. However, average savings in sample size are less for the chronic-disease models than for the acute model. Finally, it is shown that for any specific disease model and fixed maximum number of inspections of the data, the percentage reduction in sample size can be readily predicted from the power.
Insights
The O'Brien and Fleming procedure for multiple testing in clinical trials is effective for chronic diseases. While type I errors and power remain unchanged, sample size savings are reduced compared to acute disease models.
Area of Science:
- Clinical Trials
- Biostatistics
- Medical Research
Background:
- The O'Brien and Fleming procedure is a standard for multiple testing in acute disease clinical trials.
- Evaluating its applicability to chronic disease models is crucial for optimizing trial design.
Purpose of the Study:
- To assess the O'Brien and Fleming procedure's efficacy and impact on sample size in chronic disease clinical trials.
- To compare its performance against single-sample procedures.
Main Methods:
- The study evaluated the O'Brien and Fleming procedure using chronic-disease models.
- Statistical analysis focused on type I error rates, statistical power, and sample size requirements.
Main Results:
- The O'Brien and Fleming procedure is applicable to chronic disease models without modification.
- Type I errors and statistical power are comparable to single-sample procedures.
- Sample size savings are less pronounced for chronic diseases than for acute diseases.
Conclusions:
- The O'Brien and Fleming procedure is a valid statistical method for multiple testing in chronic disease clinical trials.
- Sample size reduction can be predicted based on power and the number of data inspections for specific disease models.
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