OBJECTIVES: This study examined selected effects of the proper use of nonparametric inferential statistical methods for analysis of nonnormally distributed data, as exemplified by emergency department length of stay (ED LOS). The hypothesis was that parametric methods have been used inappropriately for evaluation of ED LOS in most recent studies in leading emergency medicine (EM) journals. To illustrate why such a methodologic flaw should be avoided, a demonstration, using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), is presented. The demonstration shows how inappropriate analysis of ED LOS increases the probability of type II errors. METHODS: Five major EM journals were reviewed, January 1, 2004, through December 31, 2007, and all studies with ED LOS as one of the reported outcomes were reviewed. The authors determined whether ED LOS was analyzed correctly by ascertaining whether nonparametric tests were used when indicated. An illustrative analysis of ED LOS was constructed using 2006 NHAMCS data, to demonstrate how inferential testing for statistical significance can deliver differing conclusions, depending on whether nonparametric methods are used when indicated. RESULTS: Forty-nine articles were identified that studied ED LOS; 80% did not perform a test of normality on the ED LOS data. Data were not normally distributed in all 10 of the studies that did perform such tests. Overall, 43% failed to use appropriate nonparametric methods. Analysis of NHAMCS data confirmed that failure to use nonparametric bivariate tests results in type II statistical error and in multivariate models with less explanatory power (a smaller R²) value). CONCLUSIONS: ED LOS, a key ED operational metric, is frequently analyzed incorrectly in the EM literature. Applying parametric statistical tests to such nonnormally distributed data reduces power and increases the probability of a type II error, which is the failure to find true associations. Appropriate use of nonparametric statistics should be a core component of statistical literacy because such use increases the validity of ED research and quality improvement projects.
OBJECTIVES: This study examined selected effects of the proper use of nonparametric inferential statistical methods for analysis of nonnormally distributed data, as exemplified by emergency department length of stay (ED LOS). The hypothesis was that parametric methods have been used inappropriately for evaluation of ED LOS in most recent studies in leading emergency medicine (EM) journals. To illustrate why such a methodologic flaw should be avoided, a demonstration, using data from the National Hospital Ambulatory Medical Care Survey (NHAMCS), is presented. The demonstration shows how inappropriate analysis of ED LOS increases the probability of type II errors. METHODS: Five major EM journals were reviewed, January 1, 2004, through December 31, 2007, and all studies with ED LOS as one of the reported outcomes were reviewed. The authors determined whether ED LOS was analyzed correctly by ascertaining whether nonparametric tests were used when indicated. An illustrative analysis of ED LOS was constructed using 2006 NHAMCS data, to demonstrate how inferential testing for statistical significance can deliver differing conclusions, depending on whether nonparametric methods are used when indicated. RESULTS: Forty-nine articles were identified that studied ED LOS; 80% did not perform a test of normality on the ED LOS data. Data were not normally distributed in all 10 of the studies that did perform such tests. Overall, 43% failed to use appropriate nonparametric methods. Analysis of NHAMCS data confirmed that failure to use nonparametric bivariate tests results in type II statistical error and in multivariate models with less explanatory power (a smaller R²) value). CONCLUSIONS: ED LOS, a key ED operational metric, is frequently analyzed incorrectly in the EM literature. Applying parametric statistical tests to such nonnormally distributed data reduces power and increases the probability of a type II error, which is the failure to find true associations. Appropriate use of nonparametric statistics should be a core component of statistical literacy because such use increases the validity of ED research and quality improvement projects.
Authors: Niels K Rathlev; Dan Obendorfer; Laura F White; Casey Rebholz; Brendan Magauran; Willie Baker; Andrew Ulrich; Linda Fisher; Jonathan Olshaker Journal: West J Emerg Med Date: 2012-05