Effects of data transformation on multivariate analyses in intracerebral hemorrhage
Vladimir Rendevski, Boris Aleksovski, Milena Kolevska, Dragan A. Stojanov, Koco Dimitrovski, Ana Mihajlovska Rendevska, Vasko Aleksovski, Aleksandar Petlickovski, Дејан Трајков, Киро Стојаноски · Macedonian Pharmaceutical Bulletin · 2016
Multivariate statistical approaches have been increasingly applied in hemorrhagic stroke data analysis. Nevertheless, several aspects regarding their relevance and validity in respect of the application of data transformations have not been studied in details. This paper examines the effects of different data transformations in the standard statistical methods of the multivariate analysis of the intracerebral hemorrhage (ICH) parameters in small group samples. Two different methods for data transformations (log transformation (log(Xi )), square root transformation (√Xi ))have been carried out. The initial volume of the ICH have been studied using several test for skewness, kurtosis, histogram distribution method and different quartile-quartile (Q-Q) and probability-probability (P-P) plots as criteria for normal distribution. Multivariate analyses for the prediction of the perifocal edema was performed using raw and transformed data. Our results indicate that the data transformation operations should be performed very carefully because different analytical outputs lead to different scientific conclusions.