Quantile Regression in Clinical Research: Complete analysis for data at a loss of homogeneity

Quantile regression is an approach to data at a loss of homogeneity, for  example (1) data with outliers, (2) skewed data like corona - deaths  data, (3) data with inconstant variability, (4) big data. In clinical  research many examples can be given like circadian phenomena, and  diseases where spreading may be dependent on subsets with frailty, low  weight, low hygiene, and many forms of lack of healthiness. Stratified  analyses is the laborious and rather explorative way of analysis, but  quantile analysis is a more fruitful, faster and completer alternative  for the purpose. Considering all of this, we are on the verge of a  revolution in data analysis. The current edition is the first textbook  and tutorial of quantile regressions for medical and healthcare students  as well as recollection/update bench, and help desk for professionals.  Each chapter can be studied as a standalone and covers one of the many  fields in the fast growing world of quantile regressions. Step by step  analyses of over 20 data files stored at extras.springer.com are  included for self-assessment. We should add that the authors are well  qualified in their field. Professor Zwinderman is past-president of the  International Society of Biostatistics (2012-2015) and Professor  Cleophas is past-president of the American College of  Angiology(2000-2002). From their expertise they should be able to make  adequate selections of modern quantile regression methods for the  benefit of physicians, students, and investigators.