Quantile regression in clinical research : complete analysis for data at a loss of homogeneity
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Bibliographic Information
Quantile regression in clinical research : complete analysis for data at a loss of homogeneity
Springer, c2021
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Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
3. Separating quantiles, traditional and quantile-wise
4. Special case
Chapter 2 Mathematical models for separating quantiles from one another
1. Introduction
2. Maximizing linear functions with the help of support vectors
3. Maximizing linear function with the help of a quadratic Lagrangian multiplier method
4. Maximizing linear function wit the help of simplex algorithms
5. The intuition of quantile regression
Part I Univariate quantile regression
Chapter 3 Quantile regressions for data at a loss of homogeneity
1. Introduction
2. Traditional linear and robust linear regression analysis
3. Quantile linear regression
4. Conclusion
Chapter 4 Quantile regressions for bimodal outcome data
1. Introduction
2. ARIMA (autoregressive integrating moving average) autoregression methodology
3. Quantile regressions for autoregressive data
4. Conclusion
Chapter 5 Chi-square test for trends versus quantile regression (Chap.15)
1. Introduction
2. Chi-square testing for trend analysis
3. Quantile regressions for trend analysis
4. Conclusion
Chapter 6 One way anova for trends versus quantile regression
1. Introduction
2. One way anova for testing event rates
3. Quantile regression for testing event rates
4. Conclusion
Chapter 7 Poisson regressions for event rates versus quantile regressions
1. Introduction
2. Poisson regression for testing event rates
3. Quantile regressions for testing event rates
4. Conclusion
Chapter 8 Poisson regressions for event outcomes per population versus quantile regression
1. Introduction
2. Poisson regression for event outcomes per population
3. Quantile regression for event outcomes per population
4. Conclusion
Chapter 9 Quasi-likelihood regressions versus quantile regressions
1. Introduction
2. Quasi-likelihood regressions
3. Quantile regressions
4. Conclusion
Chapter 10 Binary Poisson regression and Negative binomial regression versus quantile regression
1. Introduction
2. Binary Poisson regression and negative binomial regression
3. Quantile regression
4. Conclusion
Chapter 11 Paired McNemar versus quantile regression
1. Introduction
2. Mc Nemar's tests for analysis of paired binary data
3. Quantile regression for analysis of paired binary data
4. Conclusion
Part II Multiple variables quantile regression
Chapter 12 Multiple ordinary least squares (OLS) versus quantile regressions
1. Introduction
2. Gene expression levels predict drug efficacy scores
3. Ordinary least squares regression versus quantile regression for the purpose
4. Conclusion
Chapter 13 Partial correlations versus quantile regressions
1. Introduction
2. Exercise and calorieintake and their interaction predict weightloss
3. Partial correlations and qunatile regressions for analysis
4. Conclusion
Chapter 14 Quantile regression to study Corona-mortality
1. Introduction
2. Obesity, age, urbanization, capita income predict corona deaths
3. Ordinary least squares as compared to quantile regressions for analysis
4. Conclusion
Chapter 15 Graphical approach to quantile regressions and continuous outcomes
1. Introduction
2. Traditional multiple variables linear regression for analysis
3. Quantile regression for analysis
4. Conclusion
Chapter 16 Graphical approach to quantile regressions and binary outcomes
1. Introduction
2. Laboratory values predict survival from sepsis
3. Logistic regression versus quantile regression for analysis
4. Conclusion
Chapter 17 Loglinear models for incident risks versus quantile regressions
1. Introduction
2. Loglinear models for incident risks
3. Quantile regression for the same
4. Conclusion
Chapter 18 Adjusted Poisson regressions for event rates versus quantile regressions
1. Introduction
2. Adjusted Poisson regression for event rates
3. Quantile regressions for event rates
4. Conclusion
Chapter 19 Poisson event outcomes per person per period of time versus quantile regression
1. Introduction
2. Poisson event outcomes per person per period of time
3. Quantile regression event outcomes per person per period of time
4. Conclusion
Chapter 20 Restructuring categories into multiple binary variables versus quantile regression
1.Introduction
2. Restructuring categories into multiple binary variables
3. Quantile regressions
4. Conclusions
Chapter 21 Variance components analysis versus quantile regressions
1. Introduction
2. Variance components analysis
3. Quantile regressions
4. Conclusion
Chapter 22 Contrast coefficients analysis versus quantile regressions
1. Introduction
2. Contrast coefficients
3. Quantile regressions
4. Conclusion
Chapter 23 Dichotomous multiple regression versus quantile regression
1. Introduction
2. Dichotomous multiple regression
3. Quantile regression
4. Conclusion
Chapter 24 Probit regression versus quantile regressions
1. Introduction
2. Probit regression
3. Quantile regression
4. Conclusion
Chapter 25 Summaries and abstracts
Index
(200-250 pages)
by "Nielsen BookData"