Box-Cox 幂变换对护理敏感指标的影响：在估计变换参数时忽略结构效应是否重要？

The Box-Cox power transformation on nursing sensitive indicators: does it matter if structural effects are omitted during the estimation of the transformation parameter?

机构信息

Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA.

出版信息

BMC Med Res Methodol. 2011 Aug 19;11:118. doi: 10.1186/1471-2288-11-118.

DOI:10.1186/1471-2288-11-118

PMID:21854614

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3201036/

Abstract

BACKGROUND

Many nursing and health related research studies have continuous outcome measures that are inherently non-normal in distribution. The Box-Cox transformation provides a powerful tool for developing a parsimonious model for data representation and interpretation when the distribution of the dependent variable, or outcome measure, of interest deviates from the normal distribution. The objectives of this study was to contrast the effect of obtaining the Box-Cox power transformation parameter and subsequent analysis of variance with or without a priori knowledge of predictor variables under the classic linear or linear mixed model settings.

METHODS

Simulation data from a 3 × 4 factorial treatments design, along with the Patient Falls and Patient Injury Falls from the National Database of Nursing Quality Indicators (NDNQI® for the 3rd quarter of 2007 from a convenience sample of over one thousand US hospitals were analyzed. The effect of the nonlinear monotonic transformation was contrasted in two ways: a) estimating the transformation parameter along with factors with potential structural effects, and b) estimating the transformation parameter first and then conducting analysis of variance for the structural effect.

RESULTS

Linear model ANOVA with Monte Carlo simulation and mixed models with correlated error terms with NDNQI examples showed no substantial differences on statistical tests for structural effects if the factors with structural effects were omitted during the estimation of the transformation parameter.

CONCLUSIONS

The Box-Cox power transformation can still be an effective tool for validating statistical inferences with large observational, cross-sectional, and hierarchical or repeated measure studies under the linear or the mixed model settings without prior knowledge of all the factors with potential structural effects.

摘要

背景

许多护理和健康相关的研究都有连续的结果测量指标，这些指标的分布本质上是非正态的。Box-Cox 变换为数据表示和解释提供了一个强大的工具，当感兴趣的因变量（或结果测量指标）的分布偏离正态分布时，可以开发一个简洁的模型。本研究的目的是对比在经典线性或线性混合模型设置下，在没有或有预测变量的先验知识的情况下，获得 Box-Cox 幂变换参数和随后的方差分析的效果。

方法

使用来自 3×4 因子处理设计的模拟数据，以及来自国家护理质量指标数据库（NDNQI®）的 2007 年第三季度的患者跌倒和患者伤害跌倒数据（从一千多家美国医院的方便样本中抽取）进行分析。非线性单调变换的效果通过两种方式进行对比：a）估计变换参数以及具有潜在结构效应的因素，b）首先估计变换参数，然后对结构效应进行方差分析。