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Testing the ratio of two poisson rates
Kangxia Gu1, Hon Keung Tony Ng, Man Lai Tang
1Department of Statistical Science, Southern Methodist University, 3225 Daniel Avenue PO Box 750332, Dallas, Texas 75275-0332, USA.
Biometrical Journal. Biometrische Zeitschrift
|March 4, 2008
Summary
This study compares four methods for testing the ratio of two Poisson rates, finding the likelihood ratio test and certain asymptotic tests most effective for medical research. Sample size calculations are also provided for these statistical tests.
Area of Science:
- Biostatistics
- Statistical Inference
- Medical Statistics
Background:
- Comparing two Poisson rates is crucial in medical research for analyzing event frequencies.
- Various statistical tests exist, but their performance characteristics require thorough evaluation.
Purpose of the Study:
- To compare the properties and power of four distinct statistical tests for the ratio of two Poisson rates.
- To provide sample size calculation formulas and assess their validity.
- To offer recommendations for test selection in practical applications.
Main Methods:
- Monte Carlo simulation experiments were employed to evaluate test performance.
- Four general approaches were compared: asymptotically normal tests, approximate p-value tests, exact conditional tests, and a likelihood ratio test.
- Sample size calculation formulae were derived and validated for each procedure.
Main Results:
- The likelihood ratio test and specific asymptotic tests demonstrated superior power performance in simulations.
- The study provides validated sample size calculation formulas for the compared test procedures.
- Simulation results informed recommendations for practical test selection.
Conclusions:
- The likelihood ratio test and certain asymptotic tests are recommended for comparing two Poisson rates due to their performance.
- The provided sample size calculations enhance the practical utility of these statistical methods in medical studies.
- The study offers valuable guidance for researchers analyzing count data in clinical and epidemiological contexts.
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