Accurate and efficient P-values for rank-based independence tests with clustered data using a saddlepoint approximation
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View abstract on PubMed
Summary
This summary is machine-generated.Accurate statistical inference for clustered data is challenging. A new double saddlepoint approximation method provides precise p-values and confidence intervals, overcoming computational limits of permutation tests for rank-based analyses.
Area Of Science
- Biostatistics
- Statistical Inference
- Clinical Trials
Background
- Clustered data in multi-center trials and longitudinal studies present statistical inference challenges due to within-cluster correlation.
- Rank-based tests (logrank, Wilcoxon) are robust but can have inflated Type I errors with standard approximations.
- Exact permutation tests are accurate but computationally infeasible for large datasets.
Purpose Of The Study
- To develop a computationally efficient and accurate statistical method for analyzing clustered data.
- To provide reliable p-values and confidence intervals for rank-based tests in the presence of within-cluster correlation.
- To address the methodological gap between the accuracy of permutation tests and the practicality of asymptotic methods.
Main Methods
- A novel double saddlepoint approximation framework is proposed.
- The method utilizes a permutation distribution reformulation via block urn design to maintain cluster integrity.
- This reformulation allows the test statistic's distribution to be represented as a sum of independent conditional random variables for saddlepoint computation.
Main Results
- The double saddlepoint method accurately controls Type I error rates for rank-based tests on clustered data.
- Performance is statistically equivalent to exact permutation tests but with significantly reduced computational cost.
- The approach handles right-censored survival data and tied ranks effectively.
Conclusions
- The proposed double saddlepoint approximation offers a practical, efficient, and statistically rigorous tool for biostatisticians analyzing clustered data.
- It overcomes the computational limitations of permutation tests while maintaining accuracy.
- The method can prevent false-positive conclusions sometimes arising from standard asymptotic approximations in clinical trial data.
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