Statistical Methodology for Medical Data: Gestational Age, Birth Weight, and Hand Characteristics

Abstract

We briefly review the current trend in handling the neonatal baby weight, gestational age, and other related categorical and continuous variable data, through both robust and outlier-based methods. Graphs are generally presented as smooth curves which give a wrong visual impression and totally hide the actual variability of the data, robust methods are applied without considering that they may certainly be affected by asymmetrical contamination, and the outlier-based methods are incorrectly applied without ascertaining if the individual data arrays are normally distributed, free from discordant observations. Aim: To demonstrate our new procedure for handling of neonatal baby data from two case studies. Methodology: The univariate data were handled through the application of discordancy tests in the light of new precise and accurate critical values, and significance tests were applied to both original and discordant outlier-free data arrays. The bivariate data were also handled from identifying and separating discordant outliers and reporting the quality of the coefficients in terms of standard errors. Finally, the supervised multivariate technique of linear discriminant and canonical analysis is successfully applied. Results: The results of significance tests for some cases varied from the use of all data versus censored data. The identification and separation of bivariate discordant outliers drastically improved the quality of regression lines. Linear discriminant and canonical analysis provided high success values for laboratory-controlled experiments. Conclusion: The need of obtaining both central tendency and dispersion parameters in all medical data is established. The reporting of regression coefficients with individual errors is recommended.