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A Wald test comparing medical costs based on log-normal distributions with zero valued costs
1Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202-2859, USA.
Statistics in Medicine
|October 16, 1999
Summary
A new Wald test offers a simpler alternative for analyzing skewed medical cost data with zeros, providing satisfactory results comparable to complex likelihood ratio tests, especially with larger sample sizes.
Area of Science:
- Biostatistics
- Health Economics
- Medical Informatics
Background:
- Medical cost data frequently display high skewness and a substantial number of zero values.
- Standard statistical tests like the analysis of variance (ANOVA) F-test are inadequate for comparing means with such data characteristics.
- Accurate statistical inference is crucial for understanding healthcare costs and intervention impacts.
Purpose of the Study:
- To introduce a computationally simple Wald test for analyzing skewed medical cost data with zero values.
- To compare the performance of the proposed Wald test against a complex likelihood ratio test using Monte Carlo simulations.
- To demonstrate the practical application of the Wald test in a clinical study evaluating a drug utilization intervention.
Main Methods:
- Development of a Wald test based on a log-normal distribution model incorporating zero values.
- Conducting Monte Carlo simulations to assess type I error rates and statistical power.
- Application of the proposed Wald test to analyze real-world clinical data on in-patient charges.
Main Results:
- The proposed Wald test demonstrates satisfactory performance in terms of type I error control and power.
- The likelihood ratio test slightly outperforms the Wald test, but the Wald test is computationally simpler.
- The Wald test is effective for analyzing medical cost data, particularly with larger sample sizes.
Conclusions:
- The Wald test provides a practical and reliable method for analyzing skewed medical cost data with zeros.
- This approach enhances the ability to draw correct inferences in health economic and biostatistical analyses.
- The test is valuable for evaluating healthcare interventions and understanding their financial impact.
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