Biostatistics and Computer-based Analysis of Health Data Using Stata |
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Comment from the Stata technical groupBiostatistics and Computer-based Analysis of Health Data Using Stata, by Christophe Lalanne and Mounir Mesbah, aims to help researchers in health fields learn Stata quickly. To this end, the authors demonstrate many types of analysis commonly used in health fields, and they discuss the corresponding Stata commands and their results. The authors first introduce Stata and data management commands that anyone using Stata will want to be familiar with. Then the authors discuss basic and advanced statistical methods, including summary statistics, tests of means and proportions, correlation, ANOVA, linear regression, epidemiological tables for case–control and cross-sectional studies, logistic regression, and survival analysis. The given examples of these methods are based on clinical trials and epidemiological studies that will be of interest to researchers in health fields. |
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Table of contentsView table of contents >> Introduction
Chapter 1. Language Elements
1.1. Data representation in Stata
1.1.1. The Stata language
1.2. Descriptive univariate statistics and estimation1.1.2. Creating and manipulating variables 1.1.3. Indexed or criteria-based selection of observations 1.1.4. Processing the missing values 1.1.5. Data management 1.1.6. Importing external data 1.1.7. Variable management 1.1.8. Converting a numerical variable into a categorical variable
1.2.1. Summarizing a numerical variable
1.3. Bivariate descriptive statistics1.2.2. Summarizing a categorical variable
1.3.1. Describing a numeric variable by the levels of a categorical
variable
1.4. Key points1.3.2. Describing two qualitative variables 1.5. Further reading 1.6. Applications Chapter 2. Measures of Association, Comparisons of Means
and Proportions for Two Samples or More
2.1. Comparisons of two group means
2.1.1. Independent samples
2.2. Comparisons of two proportions2.1.2. Non-independent samples 2.1.3. Non-parametric approach
2.2.1. Independent samples
2.3. Risk measures and OR2.2.2. Non-independent samples 2.4. Analysis of variance
2.4.1. One-way ANOVA
2.5. Key points2.4.2. Pairwise comparisons of means 2.4.3. Linear trend test 2.4.4. Computing specific contrasts 2.4.5. Non-parametric approach 2.4.6. Two-factor ANOVA 2.6. Further reading 2.7. Applications Chapter 3. Linear Regression
3.1. Measures of association between two numeric variables
3.1.1. Bivariate descriptive statistics
3.2. Linear regression3.1.2. Pearson's correlation 3.1.3. Non-parametric correlation
3.2.1. Estimation of the model parameters
3.3. Multiple linear regression3.2.2. Pointwise and interval-based prediction 3.2.3. Model diagnostic 3.4. Key points 3.5. Further reading 3.6. Applications Chapter 4. Logistic Regression and Epidemiological
Analyses
4.1. Measures of association in epidemiology
4.1.1. Prognostic studies and risk measures
4.2. Logistic regression4.1.2. Diagnostic studies
4.2.1. Estimation of the model parameters
4.3. Key points4.2.2. Logistic regression and diagnostic studies 4.2.3. Point and interval prediction 4.2.4. Case of grouped data 4.4. Further reading 4.5. Applications Chapter 5. Survival Data Analysis
5.1. Data representation and descriptive statistics
5.1.1. Survival data representation format
5.2. Descriptive statistics5.3. Survival function and Kaplan–Meier curve
5.3.1. Mortality table
5.4. Cox regression5.3.2. Kaplan–Meier curve 5.3.3. Cumulative hazard function 5.3.4. Survival functions equality test 5.5. Key points 5.6. Further reading 5.7. Applications Bibliography
Index
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