When considering the medical care costs data with a high proportion of zero items of two inpatient groups, comparing them can be estimated using confidence intervals for the ratio of the means of two delta-lognormal distributions. The Bayesian credible interval-based uniform-beta prior (BCIh-UB) is proposed and compared with the generalized confidence interval (GCI), fiducial GCI (FGCI), the method of variance estimates recovery (MOVER), BCIh based on Jeffreys’ rule prior (BCIh-JR), and BCIh based on the normal-gamma prior (BCIh-NG). The coverage probability (CP), average length (AL), and lower and upper error rates were the performance measures applied for assessing the methods through a Monte Carlo simulation. A numerical evaluation showed that BCIh-UB had much better CP and AL than the others even with a large difference between the variances and with a high proportion of zero. Finally, to illustrate the efficacy of BCIh-UB, the methods were applied to two sets of medical care costs data.

中文翻译：

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使用贝叶斯可信区间比较 Delta-Lognormal 分布均值比率的医疗费用

当考虑两个住院患者组的零项目比例较高的医疗费用数据时，可以使用两个 delta-log 正态分布均值的置信区间来估计它们的比较。基于贝叶斯可信区间的统一贝塔先验（BCIH-UB) 提出并与广义置信区间 (GCI)、基准 GCI (FGCI)、方差估计恢复法 (MOVER)、BCI 进行比较H基于 Jeffreys 的先验规则（BCIH-JR) 和 BCIH基于正常伽马先验（BCIH-NG）。覆盖概率 (CP)、平均长度 (AL) 以及上下错误率是用于通过蒙特卡罗模拟评估方法的性能指标。数值评估表明，BCIH-UB 的 CP 和 AL 比其他人好得多，即使方差之间的差异很大并且零比例很高。最后，说明BCI的功效H-UB，该方法应用于两组医疗费用数据。