Statistical analysis of medical time series

Abstract

Statistical analysis of data sets is a necessary component of any medical research. Modern methods of mathematical statistics and statistical application suites provide extensive capabilities for analysis of random values. However, when a data set is represented by a series of data ordered by time, or when structure and order of data are essential components of research, special approaches to statistical analysis become necessary.Presented in this article are special statistical methods developed by the authors for analysis of a time series: Time Series Mann-Whitney M-test is an analogue of the known nonparametric Mann-Whitney U-test for two Time Series with an equal number of elements; Nominal Time Series Measure is a statistical estimator of dynamics of a nominal series consisting of «0» (no) and «1» (yes); Time Series Entropy EnRE is a specially developed robust formula for a Time Series, intended for calculation of nonlinear stochastic measure of order or disorder, popular in various researches. Presented methods are accompanied by a detailed demonstration of capacity for statistical analysis of medical Time Series: Analysis of growth dynamics of boys and girls aged 6–7–8 years (data by World Health Organization); analysis of the number of seizures and choice of anti-epileptic drugs (data by The National Society for Epilepsy); Time series entropy EnRE for Detecting Congestive Heart Failure by standard 5-minutesHeart Rate Variability samples (data by Massachusetts Institute of Technology – Boston’s Beth Israel Hospital RR database). It has been noted that, in every case, using the named special methods for statistical analysis of medical Time Series allows one to avoid errors in interpreting results received through statistical methods and substantially increases the accuracy of statistical analysis of medical Time Series