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E-raamat: Biostatistics and Computer-based Analysis of Health Data using Stata

(Paris-Diderot University, France), (University Pierre et Marie Curie, France)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Sep-2016
  • Kirjastus: ISTE Press Ltd - Elsevier Inc
  • Keel: eng
  • ISBN-13: 9780081010846
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Biostatistics and Computer-based Analysis of Health Data using StataSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 06-Sep-2016
  • Kirjastus: ISTE Press Ltd - Elsevier Inc
  • Keel: eng
  • ISBN-13: 9780081010846
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Biostatistics and Computer-Based Analysis of Health Data Using Stata is primarily intended for health researchers who have basic knowledge of statistical methodology and relies on the use of Stata to demonstrate a statistical approach to the management of data modeling. Many of the actions performed by statistical software come back to handling, manipulating, or even transforming digital data, which literally represent statistical data. It is therefore of primary importance to understand how statistical data are displayed and how they can be exploited by software such as Stata. Assuming basic statistical concepts, the book focuses on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data, featuring a comparison of two groups, analysis of categorical data, ANOVA, linear and logistic regression, and survival analysis. Divided into three sections, this book surveys all aspects of data processing and statistical analysis of cross-sectional and experimental medical data, including full coverage of regression models commonly found in medical statistics (linear and logistic regression, Cox regression).Provides detailed examples of the use of Stata for common biostatistical tasks in medical researchFeatures a work program structured around the four previous chapters, and a series of practical exercises with commented correctionsIncludes an appendix to help the reader familiarize with additional packages and commandsFocuses on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data

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Focusing on the practice of biostatistical methods that are essential to clinical research, epidemiology, and analysis of biomedical data, this practical guide uses the statistics software Stata to survey all aspects of data processing and statistical analysis of health data, including full coverage of regression models commonly found in medical statistics.
Introduction ix
Chapter 1 Language Elements
1(24)
1.1 Data representation in Stata
1(11)
1.1.1 The Stata language
1(2)
1.1.2 Creating and manipulating variables
3(2)
1.1.3 Indexed or criteria-based selection of observations
5(1)
1.1.4 Processing the missing values
6(1)
1.1.5 Data management
7(1)
1.1.6 Importing external data
7(2)
1.1.7 Variable management
9(2)
1.1.8 Converting a numerical variable into a categorical variable
11(1)
1.2 Descriptive univariate statistics and estimation
12(2)
1.2.1 Summarizing a numerical variable
12(1)
1.2.2 Summarizing a categorical variable
13(1)
1.3 Bivariate descriptive statistics
14(4)
1.3.1 Describing a numeric variable by the levels of a categorical variable
14(4)
1.3.2 Describing two qualitative variables
18(1)
1.4 Key points
18(1)
1.5 Further reading
19(1)
1.6 Applications
19(6)
Chapter 2 Measures of Association, Comparisons of Means and Proportions for Two Samples or More
25(34)
2.1 Comparisons of two group means
25(5)
2.1.1 Independent samples
25(4)
2.1.2 Non-independent samples
29(1)
2.1.3 Non-parametric approach
29(1)
2.2 Comparaisons of two proportions
30(4)
2.2.1 Independent samples
30(4)
2.2.2 Non-independent samples
34(1)
2.3 Risk measures and OR
34(3)
2.4 Analysis of variance
37(10)
2.4.1 One-way AN OVA
37(2)
2.4.2 Pairwise comparisons of means
39(1)
2.4.3 Linear trend test
40(2)
2.4.4 Computing specific contrasts
42(1)
2.4.5 Non-parametric approach
43(1)
2.4.6 Two-factor ANOVA
44(3)
2.5 Key points
47(1)
2.6 Further reading
47(1)
2.7 Applications
47(12)
Chapter 3 Linear Regression
59(20)
3.1 Measures of association between two numeric variables
59(3)
3.1.1 Bivariate descriptive statistics
59(2)
3.1.2 Pearson's correlation
61(1)
3.1.3 Non-parametric correlation
62(1)
3.2 Linear regression
62(6)
3.2.1 Estimation of the model parameters
62(2)
3.2.2 Pointwise and interval-based prediction
64(1)
3.2.3 Model diagnostic
65(3)
3.3 Multiple linear regression
68(1)
3.4 Key points
69(1)
3.5 Further reading
70(1)
3.6 Applications
70(9)
Chapter 4 Logistic Regression and Epidemiological Analyses
79(22)
4.1 Measures of association in epidemiology
79(7)
4.1.1 Prognostic studies and risk measures
79(5)
4.1.2 Diagnostic studies
84(2)
4.2 Logistic regression
86(6)
4.2.1 Estimation of the model parameters
86(1)
4.2.2 Logistic regression and diagnostic studies
87(1)
4.2.3 Point and interval prediction
88(2)
4.2.4 Case of grouped data
90(2)
4.3 Key points
92(1)
4.4 Further reading
92(1)
4.5 Applications
92(9)
Chapter 5 Survival Data Analysis
101(10)
5.1 Data representation and descriptive statistics
101(1)
5.1.1 Survival data representation format
101(1)
5.2 Descriptive statistics
102(1)
5.3 Survival function and Kaplan-Meier curve
103(5)
5.3.1 Mortality table
103(2)
5.3.2 Kaplan-Meier curve
105(1)
5.3.3 Cumulative hazard function
106(1)
5.3.4 Survival functions equality test
106(2)
5.4 Cox regression
108(2)
5.5 Key points
110(1)
5.6 Further reading
111(1)
5.7 Applications
111(1)
Bibliography 111(8)
Index 119
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.
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