4th Edition

An Introduction to Stata for Health Researchers, Fourth Edition



ISBN 9781597181358
Published March 21, 2014 by Stata Press
346 Pages

USD $90.95

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Book Description

An Introduction to Stata for Health Researchers, Fourth Edition methodically covers data management, simple description and analysis, and more advanced analyses often used in health research, including regression models, survival analysis, and evaluation of diagnostic methods. A chapter on graphics explores most graph types and describes how to modify the appearance of a graph before submitting it for publication.

The authors emphasize the importance of good documentation habits to prevent errors and wasted time. Demonstrating the use of strategies and tools for documentation, they provide robust examples and offer the datasets for download online.

Updated to correspond to Stata 13, this fourth edition is written for both Windows and Mac users. It provides improved online documentation, including further reading in online manuals.

Table of Contents

The Basics
Getting Started
Installing and updating Stata
Starting and exiting Stata
Customizing Stata (Windows)
Windows in Stata
Issuing commands
Managing output
Reusing commands

Getting Help—and More
The manuals
Online help
Other resources
Errors and error messages

Stata File Types and Names

Command Syntax
General syntax rules
Syntax diagrams
Lists of variables and numbers
Qualifiers
Weights
Options
Prefixes
Other syntax elements
Version control

Data Management
Variables
Types of variables
Numeric formats
Missing values
Storage types and precision
Date and time variables
String variables
Memory considerations

Getting Data in and out of Stata
Opening and saving Stata data
Entering data
Reading ASCII data
Exchanging data with other programs

Documentation Commands
Labels
Working with labels: an example

Calculations
Generate and replace
Operators and functions in calculations
Extended functions: egen
Recoding variables
Checking correctness of calculations
Numbering observations

Commands Affecting Data Structure
Safeguarding your data
Selecting observations and variables
Renaming and reordering variables
Sorting data
Combining files
Reshaping data

Taking Good Care of Your Data
The audit trail
Collecting and entering data
Data management
Analysis
Protect your data
Archiving the project

Analysis
Description and Simple Analysis
Overview of a dataset
Listing observations
Simple tables for categorical variables
Analyzing continuous variables
Estimating confidence intervals
Immediate commands

Regression Analysis
Linear regression
Regression postestimation
Categorical predictors—factor variables
Interactions in regression models
Logistic regression
Other regression models
Nonindependent observations

Time-to-Event Data
Setting the time scale and event: The stset command
The Kaplan–Meier survival function
Tabulating rates
Cox proportional hazards regression
Preparing data for advanced survival analyses
Advanced survival modeling
Poisson regression
Standardization

Measurement and Diagnosis
Comparing two measurements
Reproducibility of measurements
Using tests for diagnosis

Miscellaneous
Random samples, simulations
Power and sample-size analysis
Commands that influence program flow
Decimal periods and commas
Logging output permanently
Other analyses

Graphs
Graphs
Anatomy of a graph
Anatomy of graph commands
Graph size
Schemes
Graph options: axes
Graph options: text elements
Plot options: markers, lines, etc.
Graph examples
By-graphs and combined graphs
Using dialogs to generate commands
Saving, displaying, and printing graphs

Advanced Topics
Advanced Topics

Using saved results
Macros and scalars
Some useful commands
Programs
Debugging programs

Appendices
A: Stata Manuals
B: Exercises

C: Shortcuts and Keystrokes

References

Indices

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Author(s)

Biography

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.

Reviews

"While impossible to cover every aspect of statistical analysis in a single book, this book does a nice job of covering the most common methods, from simple to complex, with an emphasis on the common methods seen in the field of health research … ideal to keep on hand as a reference manual."
—Melissa Plegue, University of Michigan in International Statistical Review