*Comments from the previous edition.*

Svend Juul and Morten Frydenberg’s *An Introduction to Stata for Health Researchers, Fourth Edition *is distinguished in its careful attention to detail. The reader will learn not only the skills for statistical analysis but also the skills to make the analysis reproducible. The authors use a friendly, down-to-earth tone and include tips gained from a lifetime of collaboration and consulting.

The book is based on the assumption that the reader has some basic knowledge of statistics but no knowledge of Stata. The authors build the reader’s abilities as a builder would build a house: laying a firm foundation in Stata, framing a general structure in which good work can be accomplished, adding the details that are particular to various types of statistical analyses, and, finally, trimming with a thorough treatment of graphics and special topics such as power and sample-size computations.

Juul and Frydenberg start not only by teaching the reader how to communicate with Stata through its unified syntax but also by demonstrating how Stata thinks about its basic building blocks. The authors show how Stata views data, thus allowing the reader to see the variety of possible data structures. They also show how to manipulate data to create a dataset that is well documented. When demonstrating analysis techniques, the authors show how to think of analysis in terms of estimation and postestimation. They make the book easy to use as a learning tool and easy to refer back to for useful techniques.

Once they introduce Stata to new users, Juul and Frydenberg fill in the details for performing analysis in Stata. As would be expected from a book addressing health researchers, the authors mostly demonstrate the statistical techniques that are common in biostatistics and epidemiology: case–control, matched case–control, and incidence-rate data analysis; linear and generalized linear models, including logistic, Poisson, and binomial regression; survival analysis with proportional hazards; and classification using receiver operating characteristic curves. While presenting general estimation techniques, the authors also spend time with interactions and techniques for checking model assumptions.

While teaching Stata implementation, Juul and Frydenberg reinforce habits that allow reproducible research and graceful backtracking in case of errors. Early in the book, they introduce how to use do-files for creating sequences and log files for tracking work. At the end of the book, they introduce some useful programming techniques, such as loops and branching, that simplify repetitive tasks.

The fourth edition has been substantially revised based on new features in Stata 12 and Stata 13. The updated material has been streamlined while including new features in Stata.

© Copyright 1996–2023 StataCorp LLC

**List of tables**

** List of figures**

**Preface to the fifth edition** (PDF)

**Preface to the first edition** (PDF)

**Online supplements**

** Notations in this book**

**I The basics**

1.2 Starting and exiting Stata

1.3 Windows in Stata

1.4 Issuing commands

1.5 Managing output

1.6 Stata file types and names

1.7 Keyboard shortcuts

2.2 The complete Stata manuals

2.3 Other resources

3.2 Syntax diagrams

3.3 Lists of variables and numbers

3.4 Qualifiers

3.5 Weights

3.6 Options

3.7 Prefixes

3.8 Other syntax elements

3.9 Version control

3.10 Commands that influence program flow

3.11 Errors and error messages

**II Data management**

4.2 Missing values

4.3 Storage types and precision

4.4 Date and time variables

4.5 String variables

4.6 Memory considerations

5.2 Entering data

5.3 Exchanging data with other software

6.2 Dataset label and notes

7.2 Operators and functions in calculations

7.3 The egen command

7.4 Recoding variables

7.5 Checking correctness of calculations

7.6 Giving numbers to observations

8.2 Renaming and reordering variables

8.3 Sorting data

8.4 Combining files

8.5 Reshaping data

9.2 Data management

9.3 Analysis

9.4 Protect your data

**III Analysis**

10.2 Listing observations

10.3 Simple tables for categorical variables

10.4 Analyzing binary variables

10.5 Analyzing continuous variables

10.6 Estimating confidence intervals

10.7 Immediate commands

11.2 Regression postestimation

11.3 Categorical predictors—factor variables

11.4 Interactions in regression models

11.5 Logistic regression

11.6 Other regression models

11.7 Nonindependent observations

11.8 Bootstrapping

12.2 The Kaplan–Meier survival function

12.3 Tabulating rates

12.4 Cox proportional hazards regression

12.5 Preparing data for advanced survival analyses

12.6 Advanced survival modeling

12.7 Poisson regression

13.2 Precision analysis

13.3 Power by simulation—A superiority study

14.2 Reproducibility of measurements

14.3 Using tests for diagnosis

15.2 Working with diagnoses

15.3 Preparing tables for publication

15.4 Including graphs in Word and PDF files

15.5 Profile.do changing the ado-path

15.6 Unicode and ASCII encoding of characters

15.7 Other analyses

**IV Graphs**

16.2 Anatomy of graph commands

16.3 Graph size

16.4 Schemes

16.5 Graph options: Axes

16.6 Graph options: Text elements

16.7 Plot options: Markers, lines, etc.

16.8 Histograms and other distribution graphs

16.9 Twoway graphs: scatterplots and line plots

16.10 Bar graphs

16.11 By-graphs and combined graphs

16.12 Saving and exporting graphs

**V Advanced topics**

17.2 Macros and scalars

17.3 Some useful commands

17.4 Programs

17.5 Debugging programs and complex commands

**References**

**Author index**(PDF)

**Subject index**(PDF)

© Copyright 1996–2023 StataCorp LLC