Tu W, Zhou X H
Division of Biostatistics, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202-2859, USA.
Stat Med. 1999 Oct 30;18(20):2749-61. doi: 10.1002/(sici)1097-0258(19991030)18:20<2749::aid-sim195>3.0.co;2-c.
Medical cost data often exhibit strong skewness and sometimes contain large proportions of zero values. Such characteristics prevent the analysis of variance (ANOVA) F-test and other frequently used standard tests from providing the correct inferences when the comparison of means is of interest. One solution to the problem is to introduce a parametric structure based on log-normal distributions with zero values and then construct a likelihood ratio test. While such a likelihood ratio test possesses excellent type I error control and power, its implementation requires a rather complicated iterative optimization program. In this paper, we propose a Wald test with simple computation. We then conduct a Monte Carlo simulation to compare the type I error rates and powers of the proposed Wald test with those of the likelihood ratio test. Our simulation study indicates that although the likelihood ratio test slightly outperforms the Wald test, the performance of the Wald test is also satisfactory, especially when the sample sizes are reasonably large. Finally, we illustrate the use of the proposed Wald test by analysing a clinical study assessing the effects of a computerized prospective drug utilization intervention on in-patient charges.
医疗成本数据通常呈现出强烈的偏态，有时还包含很大比例的零值。当关注均值比较时，这些特征使得方差分析（ANOVA）F检验和其他常用的标准检验无法提供正确的推断。解决该问题的一种方法是引入基于具有零值的对数正态分布的参数结构，然后构建似然比检验。虽然这种似然比检验具有出色的I型错误控制和检验功效，但其实施需要相当复杂的迭代优化程序。在本文中，我们提出了一种计算简单的Wald检验。然后，我们进行蒙特卡罗模拟，以比较所提出的Wald检验与似然比检验的I型错误率和检验功效。我们的模拟研究表明，尽管似然比检验略优于Wald检验，但Wald检验的性能也令人满意，尤其是当样本量合理大时。最后，我们通过分析一项评估计算机化前瞻性药物使用干预对住院费用影响的临床研究，来说明所提出的Wald检验的应用。
