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On estimating the difference between two probabilities, with special reference to clinical trials
Biometrics
|March 1, 1983
Summary
This study compares two methods for estimating the difference in success probabilities between binomial experiments. Large-sample confidence intervals are shown to be superior to exact distribution methods, both theoretically and practically.
Area of Science:
- Statistics
- Probability Theory
- Experimental Design
Background:
- Estimating differences in success probabilities is crucial for comparing binomial experiments.
- Existing methods include those based on exact distributions and large-sample approximations.
Purpose of the Study:
- To compare the theoretical and practical performance of confidence intervals for the difference between two binomial proportions.
- To determine the superiority of large-sample theory-based confidence intervals over exact distribution-based methods.
Main Methods:
- Construction of confidence intervals using large-sample theory.
- Comparison of these intervals with those derived from exact binomial distributions.
- Theoretical and practical evaluation of interval properties.
Main Results:
- Confidence intervals derived from large-sample theory demonstrate superior performance.
- The large-sample method is practically and theoretically advantageous compared to the exact distribution method.
Conclusions:
- Large-sample confidence intervals are recommended for estimating the difference between binomial success probabilities.
- The findings support the practical application of large-sample methods in statistical inference for binomial data.
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