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E-raamat: Analyzing Health Data in R for SAS Users

(Laboure College, Milton, MA USA),
  • Formaat: 334 pages
  • Ilmumisaeg: 22-Nov-2017
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498795890
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Analyzing Health Data in R for SAS UsersSuurem pilt
  • Formaat: 334 pages
  • Ilmumisaeg: 22-Nov-2017
  • Kirjastus: Chapman & Hall/CRC
  • Keel: eng
  • ISBN-13: 9781498795890
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Analyzing Health Data in R for SAS Users is aimed at helping health data analysts who use SAS accomplish some of the same tasks in R. It is targeted to public health students and professionals who have a background in biostatistics and SAS software, but are new to R.

For professors, it is useful as a textbook for a descriptive or regression modeling class, as it uses a publicly-available dataset for examples, and provides exercises at the end of each chapter. For students and public health professionals, not only is it a gentle introduction to R, but it can serve as a guide to developing the results for a research report using R software.

Features:











Gives examples in both SAS and R





Demonstrates descriptive statistics as well as linear and logistic regression





Provides exercise questions and answers at the end of each chapter





Uses examples from the publicly available dataset, Behavioral Risk Factor Surveillance System (BRFSS) 2014 data





Guides the reader on producing a health analysis that could be published as a research report





Gives an example of hypothesis-driven data analysis





Provides examples of plots with a color insert

Arvustused

"R is an increasingly popular programming in statistics and data science. This well-presented and timely book builds a critical bridge between SAS and R, which is particularly appropriate for students and researchers with knowledge and experiencing in using SAS language to gain programming proficiency in R language. I highly recommend this very insightful book to statisticians, data scientists, social scientists, psychologists, biologists, public health researchers and practitioners, and clinicians who are familiar with SAS to harness the magnificent power of R. I would use this book as a major reference book for a biostatistics course on R."~Tianhua Niu, Tulane University School of Medicine

Preface ix
Acknowledgments xi
Authors xiii
1 Differences between SAS and R
1(26)
Structure of Program
1(11)
Installation of PC Version: SAS versus R
2(1)
Licensing Differences: SAS versus R
3(1)
SAS Components versus R Packages
3(1)
What is RStudio?
4(1)
Maintaining Current Versions in SAS versus R
4(2)
SAS versus R User Communities
6(1)
SAS versus R User Interfaces
7(2)
Code Documentation and Metadata: SAS versus R
9(3)
Handling of Data
12(10)
A Focus on SAS Data Handling
12(1)
Comparison to R Data Handling
13(2)
Basic Differences in Code Syntax: R versus SAS
15(2)
SAS Formats and Labels versus R Approaches
17(5)
SAS versus R---What to Choose?
22(3)
Why R Can Be a Difficult Choice for Public Health Efforts
22(2)
Considerations When Choosing R versus SAS
24(1)
Optional Exercises
25(2)
All Sections
25(1)
Questions
25(1)
Answers
25(2)
2 Preparing Data for Analysis
27(106)
Reading Data into R
27(6)
Importing Data
27(1)
Checking the Dataset after Reading It in
28(5)
Checking Data in R
33(15)
Statistics on Continuous Data in R
33(4)
Visualizing Continuous Data in R
37(3)
Statistics on Categorical Data in R: One Variable
40(4)
Statistics on Categorical Data in R: Crosstabs
44(4)
Editing Data in R
48(44)
Trimming off Unneeded Variables
48(2)
Applying Qualification Criteria through Subsetting Datasets
50(3)
Creating Grouping Variables
53(8)
Creating Indicator Variables for Two-Level Categories
61(1)
Creating Indicator Variables for Three-Level Categories
62(1)
Creating Indicator Variables for Multilevel Ordinal Categories
63(2)
Creating Indicator Variables for Multilevel Nominal Categories
65(2)
Creating Missing Flags
67(1)
Preparing Binary Outcome Variable
68(1)
Planning a Survival Analysis Dataset with Time-to-Event Variables
68(2)
Developing the Survival Dataset
70(12)
Dealing with Dates
82(6)
Recoding and Classifying Continuous Variables
88(1)
Recoding a Continuous Outcome Variable
89(3)
Data Validation in R
92(24)
Bivariate Relationships between Continuous Variables
93(6)
Bivariate Relationships between Categorical and Continuous Variables
99(7)
Bivariate Relationships between Categorical Variables
106(2)
Power Calculations
108(7)
Write Out Analytic File
115(1)
Optional Exercises
116(17)
Section "Reading Data into R"
116(1)
Questions
116(1)
Answers
116(1)
Section "Checking Data in R"
117(1)
Questions
117(1)
Answers
118(4)
Section "Editing Data in R"
122(1)
Questions
122(2)
Answers
124(3)
Section "Data Validation in R"
127(1)
Questions
127(1)
Answers
128(5)
3 Basic Descriptive Analysis
133(64)
Making "Table 1"---Categorical Outcome
133(23)
Structure of Categorical Table 1
133(3)
SAS Approaches to Categorical Table 1 Structure
136(5)
SAS Approaches to Table Presentation Using Excel
141(1)
SAS Bivariate Categorical Tests
142(1)
The Table Command in R
143(2)
R Approaches to Categorical Table 1
145(8)
Approaches to Automating Table Generation in R
153(2)
R Bivariate Statistical Tests
155(1)
Making "Table 1"---Continuous Outcome
156(20)
Structure of Continuous Table 1
156(1)
SAS Approaches to Continuous Table 1
156(7)
Continuous Bivariate Statistical Tests in SAS
163(2)
R Approaches to Continuous Table 1
165(6)
Continuous Bivariate Statistical Tests in R
171(5)
Descriptive Analysis of Survival Data
176(13)
Summary Statistics and Plots on Time Variable
176(1)
Generating and Plotting Survival Curves
177(8)
Bivariate Tests of Survival Curves
185(4)
Optional Exercises
189(8)
Section "Making Table 1'---Categorical Outcome"
189(1)
Questions
189(1)
Answers
189(1)
Section "Making Table 1'---Continuous Outcome"
190(1)
Questions
190(1)
Answers
190(3)
Section "Descriptive Analysis of Survival Data"
193(1)
Questions
193(1)
Answers
194(3)
4 Basic Regression Analysis
197(90)
This Book's Approach
197(6)
Selection of Modeling Approach
197(2)
Selection of Manual Approach
199(1)
Operationalizing the Stepwise Selection Process
200(1)
Prespecifying Hypotheses and Avoiding Fishing
201(2)
Linear Regression and ANOVA
203(25)
Preparing to Run Linear Regression
203(2)
Linear Regression Modeling and Model Fit Statistics
205(6)
Selecting the Final Linear Regression Model
211(3)
Considerations in Improving the Final Model
214(1)
Considering Collinearity
214(1)
Adding Interactions
215(2)
Goodness-of-Fit Statistics
217(1)
Linear Regression Model Presentation
218(7)
Plot to Assist Interpretation
225(3)
Logistic Regression
228(28)
Estimates Produced by Logistic Regression
229(1)
Logistic Regression Considerations
229(2)
Introduction to Logistic Regression Modeling
231(1)
Logistic Regression Modeling and Model Fitting
232(7)
Selecting the Final Logistic Regression Model
239(10)
Logistic Regression Model Presentation
249(6)
Plot to Assist Interpretation
255(1)
Survival Analysis Regression
256(20)
Selecting a Parametric Distribution in Survival Analysis
257(5)
Selecting a Semiparametric Distribution for Survival Analysis
262(2)
Introduction to Survival Analysis Regression Modeling
264(1)
Survival Analysis Regression Modeling and Model Fitting
265(1)
Parametric Survival Analysis
265(5)
Semiparametric Survival Analysis
270(4)
Issues to Consider in Survival Analysis
274(1)
Selecting the Final Survival Analysis Model
275(1)
Survival Analysis Model Presentation
275(1)
A Note about Macros
276(2)
Optional Exercises
278(9)
Section "This Book's Approach"
278(1)
Questions
278(1)
Answers
278(1)
Section "Linear Regression and ANOVA"
278(1)
Questions
278(1)
Answers
279(1)
Section "Logistic Regression"
280(1)
Questions
280(1)
Answers
280(1)
Section "Survival Analysis Regression"
281(1)
Questions
281(1)
Answers
282(3)
Section "A Note about Macros"
285(1)
Questions
285(1)
Answers
285(2)
References 287(10)
Index 297
Monika M. Wahi, MPH, CPH is an experienced epidemiologist with multiple peer-reviewed articles and book chapters on many public health subjects. Her focus is on applying informatics methods to the practice of epidemiology, as well as teaching public health and biostatistics. She serves as a lecturer at Laboure College in Milton, Massachusetts and is Chief Science Officer of Vasanta Health Science.

Peter Seebach has over 25 years of experience with programming languages, ranging from developing open source projects to working on language standards committees. He currently works as a Senior devOps Engineer at Markley Cloud Services. His previous publications include a number of technical articles and the book Beginning Portable Shell Scripting.
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