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Biostatistics and Computer-based Analysis of Health Data using R [Kietas viršelis]

(Paris-Diderot University, France), (University Pierre et Marie Curie, France)
  • Formatas: Hardback, 206 pages, aukštis x plotis: 229x152 mm, weight: 340 g
  • Išleidimo metai: 12-Jul-2016
  • Leidėjas: ISTE Press Ltd - Elsevier Inc
  • ISBN-10: 178548088X
  • ISBN-13: 9781785480881
Kitos knygos pagal šią temą:
Biostatistics and Computer-based Analysis of Health Data using RDidesnis paveikslėlis
  • Formatas: Hardback, 206 pages, aukštis x plotis: 229x152 mm, weight: 340 g
  • Išleidimo metai: 12-Jul-2016
  • Leidėjas: ISTE Press Ltd - Elsevier Inc
  • ISBN-10: 178548088X
  • ISBN-13: 9781785480881
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781785480881.html
Raktažodžiai:

Many of the actions performed from by a statistical software comes back to handle,down to manipulating or even transforming digital data, which actually representing statistical data literally.It is therefore of primary importance to understand how the statisticsal data are displayed and how they can be exploited by software such as R. The authors explore basic and variable commands, sample comparisons, analysis of variance, epidemiological studies and censored data.With proposed applications and examples of commands following each chapter, this book allows you to apply advanced statistical concepts to your own data and software.

This book features:

  • Useful commands for describing a data table composed made up of quantitative and qualitative variables
  • Measures of association encountered in epidemiological studies: odds ratio, relative risk, prevalence
  • Analysis of censored data, the key main tests associated with the construction of a survival curve (log-rank test or Wilcoxon) and the Cox regression model

Daugiau informacijos

Through an approach that addresses the concept that many of the actions performed by statistical software comes back to the handling, manipulation, or even transformation of digital data, this book provides basic commands and data tables that help readers apply advanced statistical concepts to their own data and software.
Introduction ix
Chapter 1 Elements of the Language
1(22)
1.1 Before proceeding
1(2)
1.1.1 Installing R
1(1)
1.1.2 Rstudio
1(1)
1.1.3 List of useful packages
2(1)
1.1.4 Find help
2(1)
1.1.5 R scripts
2(1)
1.2 Data representation in R
3(6)
1.2.1 Management of numerical variables
3(1)
1.2.2 Operations with a numerical variable
4(2)
1.2.3 Management of categorical variables
6(1)
1.2.4 Manipulation of categorical variables
7(2)
1.3 Selection of observations
9(1)
1.3.1 Index-based selection
9(1)
1.3.2 Criterion-based selection
9(1)
1.4 Representation and processing of missing values
10(1)
1.5 Importing and storing data
11(3)
1.5.1 Univariate data
11(1)
1.5.2 Multivariate data
12(1)
1.5.3 Storing the data in an external file
13(1)
1.6 Multidimensional data management
14(4)
1.6.1 Construction of a structured data table
14(1)
1.6.2 Birthwt data
15(3)
1.7 Key points
18(1)
1.8 Going further
18(1)
1.9 Applications
18(5)
Chapter 2 Descriptive Statistics and Estimation
23(18)
2.1 Summarizing a numerical variable
23(2)
2.1.1 Central tendency and shape of the distribution
23(1)
2.1.2 Distribution indicators
24(1)
2.2 Summarizing a categorical variable
25(2)
2.3 Graphically representing the distribution of a variable
27(6)
2.3.1 The case of numerical variables
28(3)
2.3.2 The case of categorical variables
31(2)
2.4 Interval estimation for a mean or a proportion
33(2)
2.4.1 Confidence interval for a mean
33(1)
2.4.2 Confidence interval for a proportion
34(1)
2.5 Key points
35(1)
2.6 Applications
36(5)
Chapter 3 Measures and Tests of Association Between Two Variables
41(24)
3.1 Bivariate descriptive statistics
41(5)
3.1.1 Describing a numeric variable according to the modalities of a qualitative variable
41(3)
3.1.2 Describing two qualitative variables
44(2)
3.2 Comparisons of two group means
46(6)
3.2.1 Independent samples
46(3)
3.2.2 Non-independent samples
49(3)
3.3 Comparisons of proportions
52(3)
3.3.1 Case of two proportions
52(1)
3.3.2 Chi-squared test
53(1)
3.3.3 The case of non-independent samples
54(1)
3.4 Risk and odds ratio measures
55(1)
3.5 Non-parametric approaches and exact tests
56(2)
3.6 Key points
58(1)
3.7 Going further
58(1)
3.8 Applications
58(7)
Chapter 4 Analysis of Variance and Experimental Design
65(24)
4.1 Data representation and descriptive statistics
65(3)
4.1.1 Data representation format
65(1)
4.1.2 Descriptive statistics and data structuring
66(2)
4.2 One-way ANOVA
68(8)
4.2.1 The one-way ANOVA model
68(3)
4.2.2 Comparisons using pairs of treatments
71(1)
4.2.3 Linear trend test
72(4)
4.3 Non-parametric one-way ANOVA
76(1)
4.4 Two-way ANOVA
76(4)
4.4.1 Construction of an ANOVA table
77(2)
4.4.2 Diagnostic model
79(1)
4.5 Key points
80(1)
4.6 Applications
80(9)
Chapter 5 Correlation and Linear Regression
89(22)
5.1 Descriptive statistics
89(4)
5.1.1 Scatterplot and Loess curve
91(1)
5.1.2 Parametric and non-parametric association measures
91(1)
5.1.3 Interval estimation and inference test
92(1)
5.2 Simple linear regression
93(8)
5.2.1 Regression line
93(3)
5.2.2 Interval estimation and variance analysis table
96(1)
5.2.3 Regression model predictions
97(1)
5.2.4 Diagnostic and residual analysis of the model
98(1)
5.2.5 Connection with ANOVA
99(2)
5.3 Multiple linear regression
101(1)
5.4 Key points
102(1)
5.5 Going further
102(1)
5.6 Applications
102(9)
Chapter 6 Measures of Association in Epidemiology and Logistic Regression
111(26)
6.1 Contingency tables and measures of association
111(4)
6.1.1 Contingency tables
111(1)
6.1.2 Measures and association tests
112(1)
6.1.3 Odds ratio and stratification
112(3)
6.2 Diagnostic studies
115(3)
6.2.1 Sensibility and specificity of a diagnostic test
115(1)
6.2.2 Positive and negative predictive values
116(1)
6.2.3 Synthesis table of the diagnostic properties of a test
117(1)
6.3 Logistic regression
118(6)
6.3.1 Estimation of the model's parameters
118(3)
6.3.2 Predictions with confidence intervals
121(2)
6.3.3 The case of grouped data
123(1)
6.4 ROC curve
124(1)
6.5 Key points
125(1)
6.6 Applications
126(11)
Chapter 7 Survival Data Analysis
137(18)
7.1 Data representation and descriptive statistics
138(1)
7.1.1 Data representation format
138(1)
7.2 Survival function and Kaplan-Meier curve
138(6)
7.2.1 Survival table
138(2)
7.2.2 Kaplan-Meier curve
140(2)
7.2.3 Cumulative hazard function
142(1)
7.2.4 Survival function equality test
143(1)
7.3 Cox regression
144(2)
7.4 Key points
146(1)
7.5 Going further
147(1)
7.6 Applications
147(8)
Appendices
155(32)
Appendix 1 Introduction to RStudio
157(4)
Appendix 2 Graphs with the Lattice Package
161(12)
Appendix 3 The Hmisc and rms Packages
173(14)
Bibliography 187(4)
Index 191
Christophe Lalanne is a Research Engineer at the Paris-Diderot University, France. His research involves the modeling of data from clinical research Mounir Mesbah is Head of Biostatistics in the Statistics Institute of the Pierre and Marie Curie University in Paris, France, where he is also a researcher at the Theoretical and Applied Statistics Laboratory. His research involves theoretical and applied statistics, particularly in the field of health and medicine.