## An Introduction to Stata for Health ResearchersSvend JuulCopyright 2006 ISBN-13: 978-1-59718-010-8 Pages: 326; paperback Second edition now available | |

See a larger photo of the front cover See the back cover Table of contents Preface (pdf) Author index (pdf) Subject index (pdf) Errata Other supplementary materials provided by the author Download the datasets used in this book Review of this book from the Stata Journal (pdf) |

*An Introduction to Stata for Health Researchers*, by Svend Juul,
provides in-depth and insightful coverage of all three topics for which
Stata is known: data management, statistics, and graphics. By focusing on
those who are involved in health research, Juul is able to strongly cover
each topic.

The first nine (yes, nine!) chapters of the book are devoted to getting started with using Stata and learning the essentials of data management. Throughout this section, the author leaves no key information unstated, effectively placing himself “in the shoes” of the reader new to Stata. The reader learns the ins and outs of Stata’s windows, the importance of documentation, how to use do-files, how to get help (and how to help yourself), the command syntax, how to work with datasets, and the basic data management tasks such as merging and reshaping datasets.

Chapter 10 covers summary statistics, tables, and simple tests. Chapter 11 keeps the new user in mind while providing an introduction to the modern Stata graphics systems.

Although *An Introduction to Stata for Health Researchers* sounds like
a book created solely for beginners, even the experienced Stata user will
learn from the biostatistical discussions in chapters 12–15. Juul
covers the usual topics for health researchers, including the following: the
analysis of stratified data via **epitab** and regression models; linear,
logistic, and Poisson regression; and survival analysis including Cox
regression, standardized rates, and correlation/ROC analysis of
measurements. When discussing these methods, Juul expertly shows how they
relate to each other, for example, the analysis of a stratified
case–control study using both **mhodds** and **logistic**.
Sometimes the methods agree exactly, but sometimes they don't; the book
explains the change in model assumptions leading to the differences.

The book concludes with supplementary material on advanced topics such as sample size calculations, simulation, some Stata programming concepts, and tips on caring for your data and maintaining reproducibility.

List of Figures

Preface (pdf)

1 Getting started

1.1 Installing and updating Stata

1.2 Starting and stopping Stata

1.3 Customizing Stata (Windows)

1.4 Windows in Stata

1.5 Issuing commands

1.6 Exercises

1.7 Managing output

1.8 Reusing commands

1.9 More exercises

1.2 Starting and stopping Stata

1.3 Customizing Stata (Windows)

1.4 Windows in Stata

1.5 Issuing commands

1.6 Exercises

1.7 Managing output

1.8 Reusing commands

1.9 More exercises

2 Getting help—and more

2.1 The manuals

2.2 Online help

2.3 Other resources

2.4 Errors and error messages

2.2 Online help

2.3 Other resources

2.4 Errors and error messages

3 Stata file types and names

4 Command syntax

4.1 General syntax rules

4.2 Syntax diagrams

4.3 Lists of variables and numbers

4.4 Qualifiers

4.5 Weights

4.6 Options

4.7 Prefixes

4.8 Other syntax elements

4.2 Syntax diagrams

4.3 Lists of variables and numbers

4.4 Qualifiers

4.5 Weights

4.6 Options

4.7 Prefixes

4.8 Other syntax elements

5 Variables

5.1 Types of variables

5.2 Numeric formats

5.3 Missing values

5.4 Storage types and precision

5.5 Date variables

5.6 String variables

5.7 Memory considerations

5.2 Numeric formats

5.3 Missing values

5.4 Storage types and precision

5.5 Date variables

5.6 String variables

5.7 Memory considerations

6 Getting data in and out of Stata

6.1 Opening and saving Stata data

6.2 Entering data

6.3 Reading ASCII data

6.4 Exchanging data with other programs

6.2 Entering data

6.3 Reading ASCII data

6.4 Exchanging data with other programs

7 Documentation commands

7.1 Labels

7.2 Working with labels: an example

7.2 Working with labels: an example

8 Calculations

8.1 generate and replace

8.2 Operators and functions in calculations

8.3 Extended functions: egen

8.4 Recoding variables

8.5 Numbering observations

8.6 Exercises

8.2 Operators and functions in calculations

8.3 Extended functions: egen

8.4 Recoding variables

8.5 Numbering observations

8.6 Exercises

9 Commands affecting data structure

9.1 Safeguarding your data

9.2 Selecting observations and variables

9.3 Renaming and reordering variables

9.4 Sorting data

9.5 Combining files

9.6 Reshaping data

9.2 Selecting observations and variables

9.3 Renaming and reordering variables

9.4 Sorting data

9.5 Combining files

9.6 Reshaping data

10 Description and simple analysis

10.1 Overview of a dataset

10.2 Listing observations

10.3 Simple tables for categorical variables

10.4 Analyzing continuous variables

10.5 Estimating confidence intervals

10.6 Immediate commands

10.2 Listing observations

10.3 Simple tables for categorical variables

10.4 Analyzing continuous variables

10.5 Estimating confidence intervals

10.6 Immediate commands

11 Graphs

11.1 Anatomy of a graph

11.2 Anatomy of graph commands

11.3 Graph size

11.4 Schemes

11.5 Graph options: Axes

11.6 Graph options: Text elements

11.7 Plot options: Markers, lines, etc.

11.8 Graph examples

11.9 By-graphs and combined graphs

11.10 Saving, displaying, and printing graphs

11.2 Anatomy of graph commands

11.3 Graph size

11.4 Schemes

11.5 Graph options: Axes

11.6 Graph options: Text elements

11.7 Plot options: Markers, lines, etc.

11.8 Graph examples

11.9 By-graphs and combined graphs

11.10 Saving, displaying, and printing graphs

12 Stratified analysis

12.1 Cohort data without censorings

12.2 Case–control data

12.2 Case–control data

13 Regression analysis

13.1 Linear regression

13.2 Logistic regression

13.3 Other regression models

13.4 Analyzing complex design data

13.2 Logistic regression

13.3 Other regression models

13.4 Analyzing complex design data

14 Incidence, mortality, and survival

14.1 Incidence and mortality

14.2 Survival analysis

14.3 Cox regression

14.4 Reorganizing st data

14.5 Poisson regression

14.6 Standardization

14.7 Some advanced issues

14.2 Survival analysis

14.3 Cox regression

14.4 Reorganizing st data

14.5 Poisson regression

14.6 Standardization

14.7 Some advanced issues

15 Measurement and diagnosis

15.1 Reproducibility of measurements

15.2 Comparing methods of measurement

15.3 Using tests for diagnosis

15.4 Combining test results

15.2 Comparing methods of measurement

15.3 Using tests for diagnosis

15.4 Combining test results

16 Miscellaneous

16.1 Random samples, simulations

16.2 Sample size and study power

16.3 Other analyses

16.2 Sample size and study power

16.3 Other analyses

17 Advanced topics

17.1 Using saved results

17.2 Macros

17.3 Programs

17.4 Useful programming commands

17.5 Do-files and ado-files useful for handling output

17.2 Macros

17.3 Programs

17.4 Useful programming commands

17.5 Do-files and ado-files useful for handling output

18 Taking good care of your data

18.1 The audit trail

18.2 Data collection

18.3 The codebook

18.4 Folders and filenames: the log book

18.5 Entering data

18.6 Inspecting and correcting your data

18.7 Modifying data

18.8 Analysis

18.9 Backing up and archiving

18.10 Protecting against abuse

18.2 Data collection

18.3 The codebook

18.4 Folders and filenames: the log book

18.5 Entering data

18.6 Inspecting and correcting your data

18.7 Modifying data

18.8 Analysis

18.9 Backing up and archiving

18.10 Protecting against abuse

A Manuals and other good books

A.1 Stata manuals

A.2 Other books on Stata

A.3 Books using Stata

A.2 Other books on Stata

A.3 Books using Stata

B Advice on working with Windows

References

Author index (pdf)

Subject index (pdf)