**An Introduction to Stata for Health Researchers, Fourth Edition**

70,000원

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.

Svend Juul is a former associate professor, now a part-time lecturer, in epidemiology at the School of Public Health, Aarhus University. Juul has extensive experience in teaching epidemiology to medical students and others and in teaching Stata and other computer programs to PhD students in the health sciences.

Morten Frydenberg is an associate professor of biostatistics at the School of Public Health, Aarhus University. He has a PhD in theoretical statistics and more than 20 years of experience as a biostatistical consultant in health sciences. Frydenberg has taught numerous courses in applied biostatistics at both graduate and postgraduate levels.

1.2 Starting and exiting Stata

1.3 Windows in Stata

1.4 Issuing commands

1.5 Managing output

2.2 The PDF documentation

2.3 Other resources

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

4.9 Version control

4.10 Errors and error messages

5.2 Missing values

5.3 Storage types and precision

5.4 Date and time variables

5.5 String variables

5.6 Memory considerations

6.2 Entering data

6.3 Exchanging data with other programs

8.2 Operators and functions in calculations

8.3 The egen command

8.4 Recoding variables

8.5 Checking correctness of calculations

8.6 Giving numbers to observations

9.2 Renaming and reordering variables

9.3 Sorting data

9.4 Combining files

9.5 Reshaping data

10.2 Collecting and entering data

10.3 Data management

10.4 Analysis

10.5 Protect your data

10.6 Archiving the project

11.2 Listing observations

11.3 Simple tables for categorical variables

11.4 Epidemiologic tables

11.5 Analyzing continuous variables

11.6 Finding confidence intervals

11.7 Immediate commands

12.2 Regression postestimation

12.3 Categorical predictors—factor variables

12.4 Interactions in regression models

12.5 Logistic regression

12.6 Other regression models

12.7 Nonindependent observations

13.2 The Kaplan–Meier survival function

13.3 Tabulating rates

13.4 Cox proportional hazards regression

13.5 Preparing data for advanced survival analyses

13.6 Advanced survival modeling

13.7 Poisson regression

13.8 Standardization

14.2 Reproducibility of measurements

14.3 Using tests for diagnosis

15.2 Power and sample-size analysis

15.3 Commands that influence program flow

15.4 Decimal periods and commas

15.5 Logging output permanently

15.6 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 plots: 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

B.2 Managing output

B.3 Calculations

B.4 Working with missing values

B.5 Working with date variables

B.6 Description and simple analysis

B.7 Taking good care of your data