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
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
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
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
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
17.2 Macros and scalars
17.3 Some useful commands
17.4 Programs
17.5 Debugging programs and complex commands
© Copyright 1996–2023 StataCorp LLC
