Biostatistics and computer-based analysis of health data using the R software / Christophe Lalanne, Mounir Mesbah.

Format:

Book

Author/Creator:

Lalanne, Christophe, author.

Mesbah, Mounir, author.

Language:

English

Subjects (All):

Biometry--Computer programs.

Biometry.

Medical statistics--Computer programs.

Medical statistics.

R (Computer program language).

Physical Description:

1 online resource

Place of Publication:

Amsterdam : Elsevier, 2016.

System Details:

text file

Summary:

Biostatistics and Computer-Based Analysis of Health Data Using the R Software addresses the concept that many of the actions performed by statistical software comes back to the handling, manipulation, or even transformation of digital data. It is therefore of primary importance to understand how statistical data is displayed and how it can be exploited by software such as R. In this book, 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 readers to apply advanced statistical concepts to their own data and software.

Contents:

Chapter 1 Elements of the Language 1

1.1 Before proceeding 1

1.1.1 Installing R 1

1.1.2 RStudio 1

1.1.3 List of useful packages 2

1.1.4 Find help 2

1.1.5 R scripts 2

1.2 Data representation in R 3

1.2.1 Management of numerical variables 3

1.2.2 Operations with a numerical variable 4

1.2.3 Management of categorical variables 6

1.2.4 Manipulation of categorical variables 7

1.3 Selection of observations 9

1.3.1 Index-based selection 9

1.3.2 Criterion-based selection 9

1.4 Representation and processing of missing values 10

1.5 Importing and storing data 11

1.5.1 Univariate data 11

1.5.2 Multivariate data 12

1.5.3 Storing the data in an external file 13

1.6 Multidimensional data management 14

1.6.1 Construction of a structured data table 14

1.6.2 Birthwt data 15

1.7 Key points 18

1.8 Going further 18

1.9 Applications 18

Chapter 2 Descriptive Statistics and Estimation 23

2.1 Summarizing a numerical variable 23

2.1.1 Central tendency and shape of the distribution 23

2.1.2 Distribution indicators 24

2.2 Summarizing a categorical variable 25

2.3 Graphically representing the distribution of a variable 27

2.3.1 The case of numerical variables 28

2.3.2 The case of categorical variables 31

2.4 Interval estimation for a mean or a proportion 33

2.4.1 Confidence interval for a mean 33

2.4.2 Confidence interval for a proportion 34

2.5 Key points 35

2.6 Applications 36

Chapter 3 Measures and Tests of Association Between Two Variables 41

3.1 Bivariate descriptive statistics 41

3.1.1 Describing a numeric variable according to the modalities of a qualitative variable 41

3.1.2 Describing two qualitative variables 44

3.2 Comparisons of two group means 46

3.2.1 Independent samples 46

3.2.2 Non-independent samples 49

3.3 Comparisons of proportions 52

3.3.1 Case of two proportions 52

3.3.2 Chi-squared test 53

3.3.3 The case of non-independent samples 54

3.4 Risk and odds ratio measures 55

3.5 Non-parametric approaches and exact tests 56

3.6 Key points 58

3.7 Going further 58

3.8 Applications 58

Chapter 4 Analysis of Variance and Experimental Design 65

4.1 Data representation and descriptive statistics 65

4.1.1 Data representation format 65

4.1.2 Descriptive statistics and data structuring 66

4.2 One-way ANOVA 68

4.2.1 The one-way ANOVA model 68

4.2.2 Comparisons using pairs of treatments 71

4.2.3 Linear trend test 72

4.3 Non-parametric one-way ANOVA 76

4.4 Two-way ANOVA 76

4.4.1 Construction of an ANOVA table 77

4.4.2 Diagnostic model 79

4.5 Key points 80

4.6 Applications 80

Chapter 5 Correlation and Linear Regression 89

5.1 Descriptive statistics 89

5.1.1 Scatterplot and Loess curve 91

5.1.2 Parametric and non-parametric association measures 91

5.1.3 Interval estimation and inference test 92

5.2 Simple linear regression 93

5.2.1 Regression line 93

5.2.2 Interval estimation and variance analysis table 96

5.2.3 Regression model predictions 97

5.2.4 Diagnostic and residual analysis of the model 98

5.2.5 Connection with ANOVA 99

5.3 Multiple linear regression 101

5.4 Key points 102

5.5 Going further 102

5.6 Applications 102

Chapter 6 Measures of Association in Epidemiology and Logistic Regression 111

6.1 Contingency tables and measures of association 111

6.1.1 Contingency tables 111

6.1.2 Measures and association tests 112

6.1.3 Odds ratio and stratification 112

6.2 Diagnostic studies 115

6.2.1 Sensibility and specificity of a diagnostic test 115

6.2.2 Positive and negative predictive values 116

6.2.3 Synthesis table of the diagnostic properties of a test 117

6.3 Logistic regression 118

6.3.1 Estimation of the model's parameters 118

6.3.2 Predictions with confidence intervals 121

6.3.3 The case of grouped data 123

6.4 ROC curve 124

6.5 Key points 125

6.6 Applications 126

Chapter 7 Survival Data Analysis 137

7.1 Data representation and descriptive statistics 138

7.1.1 Data representation format 138

7.2 Survival function and Kaplan-Meier curve 138

7.2.1 Survival table 138

7.2.2 Kaplan-Meier curve 140

7.2.3 Cumulative hazard function 142

7.2.4 Survival function equality test 143

7.3 Cox regression 144

7.4 Key points 146

7.5 Going further 147

7.6 Applications 147.

Notes:

Chapter 1. Language Elements Chapter 2. Descriptive statistics and estimation Chapter 3. Measures and tests of association between two variables Chapter 4. Variance Analysis of devariance and Experiments Design Chapter 5. Correlation and linear regression Chapter 6. Measures of association in epidemiology and logistical regression Chapter 7. Analysis of Survival Data

Title from title screen (viewed Oct. 31, 2016).

Other Format:

Print version :

ISBN:

9780081011751

008101175X

OCLC:

961357710

Access Restriction:

Restricted for use by site license.