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Biostatistics and Computer-based Analysis of Health Data Using Stata

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eBook not available for this title

Authors:	Christophe Lalanne and Mounir Mesbah
Publisher:	ISTE Press - Elsevier
Copyright:	2016
ISBN-13:	978-1-78548-142-0
Pages:	121; hardcover
Price:	$115.00

Authors:	Christophe Lalanne and Mounir Mesbah
Publisher:	ISTE Press - Elsevier
Copyright:	2016
ISBN-13:
Pages:	121; eBook
Price:	$0.00

Authors:	Christophe Lalanne and Mounir Mesbah
Publisher:	ISTE Press - Elsevier
Copyright:	2016
ISBN-13:
Pages:	121; Kindle
Price:	$

Comment from the Stata technical group

Biostatistics 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.

View table of contents >>

Introduction

Chapter 1. Language Elements

1.1. Data representation in Stata

1.1.1. The Stata language
1.1.2. 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. Descriptive univariate statistics and estimation

1.2.1. Summarizing a numerical variable
1.2.2. Summarizing a categorical variable

1.3. Bivariate descriptive statistics

1.3.1. Describing a numeric variable by the levels of a categorical variable
1.3.2. Describing two qualitative variables

1.4. Key points
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
2.1.2. Non-independent samples
2.1.3. Non-parametric approach

2.2. Comparisons of two proportions

2.2.1. Independent samples
2.2.2. Non-independent samples

2.3. Risk measures and OR
2.4. Analysis of variance

2.4.1. One-way ANOVA
2.4.2. 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

2.5. Key points
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
3.1.2. Pearson's correlation
3.1.3. Non-parametric correlation

3.2. Linear regression

3.2.1. Estimation of the model parameters
3.2.2. Pointwise and interval-based prediction
3.2.3. Model diagnostic

3.3. Multiple linear regression
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
4.1.2. Diagnostic studies

4.2. Logistic regression

4.2.1. Estimation of the model parameters
4.2.2. Logistic regression and diagnostic studies
4.2.3. Point and interval prediction
4.2.4. Case of grouped data

4.3. Key points
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 statistics
5.3. Survival function and Kaplan–Meier curve

5.3.1. Mortality table
5.3.2. Kaplan–Meier curve
5.3.3. Cumulative hazard function
5.3.4. Survival functions equality test

5.4. Cox regression
5.5. Key points
5.6. Further reading
5.7. Applications

Bibliography

Index