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

BMC Medical Research Methodology

Nancy Dunton²  Byron J Gajewski¹  Jonathan D Mahnken³  Qingjiang Hou³ 

[1] Schools of Nursing and Allied Health, University of Kansas Medical Center, Kansas City, KS 66160, USA;School of Nursing, University of Kansas Medical Center, Kansas City, KS 66160, USA;Department of Biostatistics, University of Kansas Medical Center, Kansas City, KS 66160, USA

关键词: ANOVA, Mixed model;    Nursing quality indicator;    NDNQI;    Data transformation;   

Others  :  1140020
DOI  :  10.1186/1471-2288-11-118

 received in 2011-04-18, accepted in 2011-08-19,  发布年份 2011

【 摘 要 】

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 3^rd 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.

【 授权许可】

   
2011 Hou et al; licensee BioMed Central Ltd.

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