Background: Although many researchers in the field of health economics and quality of care compare the length of stay (LOS) in two inpatient samples, they often fail to check whether the sample meets the assumptions made by their chosen statistical test. In fact, LOS data show a highly right-skewed, discrete distribution in which most of the observations are tied; this violates the assumptions of most statistical tests. Objectives: To estimate the type I and type II errors associated with the application of 12 different statistical tests to a series of LOS samples. Methods: The LOS distribution was extracted from an exhaustive French national database of inpatient stays. The type I error was estimated using 19 sample sizes and 1,000,000 simulations per sample. The type II error was estimated in three alternative scenarios. For each test, the type I and type II errors were plotted as a function of the sample size. Results: Gamma regression with log link, the log rank test, median regression, Poisson regression, and Weibull survival analysis presented an unacceptably high type I error. In contrast, the Student standard t test, linear regression with log link, and the Cox models had an acceptable type I error but low power. Conclusions: When comparing the LOS for two balanced inpatient samples, the Student t test with logarithmic or rank transformation, the Wilcoxon test, and the Kruskal-Wallis test are the only methods with an acceptable type I error and high power.
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机译：背景：尽管卫生经济学和护理质量领域的许多研究人员比较了两个住院样本的住院时间（LOS），但他们往往无法检查样本是否符合他们选择的统计测试的假设。事实上，LOS数据显示了一个高度右偏的离散分布，其中大多数观测值是并列的;这违反了大多数统计检验的假设。研究目的：估计与对一系列 LOS 样本应用 12 种不同统计检验相关的 I 型和 II 型误差。方法：从详尽的法国国家住院患者数据库中提取 LOS 分布。使用 19 个样本量和每个样本 1,000,000 次模拟来估计 I 类误差。II类误差是在三种备选方案中估计的。对于每个测试，将 I 型和 II 型误差绘制为样本量的函数。结果：Gamma回归与对数链接、对数秩检验、中位数回归、泊松回归和Weibull生存分析呈现出不可接受的高I型误差。相比之下，学生标准 t 检验、带对数链接的线性回归和 Cox 模型具有可接受的 I 类误差，但功效较低。结论：在比较两个平衡住院患者样本的 LOS 时，具有对数或秩变换的 Student t 检验、Wilcoxon 检验和 Kruskal-Wallis 检验是唯一具有可接受的 I 型误差和高功效的方法。
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