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E-grāmata: Applied Medical Statistics Using SAS

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(University of Glasgow, Scotland, UK), (Professor Emeritus, Kings College, London, UK)
  • Formāts: 559 pages
  • Izdošanas datums: 01-Oct-2012
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781439867983
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Applied Medical Statistics Using SASPalielināt
  • Formāts: 559 pages
  • Izdošanas datums: 01-Oct-2012
  • Izdevniecība: Chapman & Hall/CRC
  • Valoda: eng
  • ISBN-13: 9781439867983
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Adding topics useful to medical statisticians, this new edition of a popular intermediate-level reference explores the use of SAS for analyzing medical data. A new chapter on visualizing data includes a detailed account of graphics for investigating data and smoothing techniques. The book also includes new chapters on measurement in medicine, epidemiology/observational studies, meta-analysis, Bayesian methods, and handling missing data. The book maintains its example-based approach, with SAS code and output included throughout and available online--Provided by publisher. Written with medical statisticians and medical researchers in mind, this intermediate-level reference explores the use of SAS for analyzing medical data. Applied Medical Statistics Using SAS covers the whole range of modern statistical methods used in the analysis of medical data, including regression, analysis of variance and covariance, longitudinal and survival data analysis, missing data, generalized additive models (GAMs), and Bayesian methods. The book focuses on performing these analyses using SAS, the software package of choice for those analysing medical data. FeaturesCovers the planning stage of medical studies in detail; several chapters contain details of sample size estimationIllustrates methods of randomisation that might be employed for clinical trialsCovers topics that have become of great importance in the 21st century, including Bayesian methods and multiple imputationIts breadth and depth, coupled with the inclusion of all the SAS code, make this book ideal for practitioners as well as for a graduate class in biostatistics or public health. Complete data sets, all the SAS code, and complete outputs can be found on an associated website: http://support.sas.com/amsus

Recenzijas

"Each chapter in the book is well laid out, contains examples with SAS code, and ends with a concise summary. The chapters in the book contain the right level of information to use SAS to apply different statistical methods. a good overview of how to apply in SAS 9.3 the many possible statistical analysis methods." Caroline Kennedy, Takeda Development Centre Europe Ltd., Statistical Methods for Medical Research, 2015

" a well-organized and thorough exploration of broad coverage in medical statistics. The book is an excellent reference of statistical methods with examples of medical data and SAS codes for statisticians or statistical analysts who are working in the medical/clinical area. It also can be a reference book for an introductory or intermediate graduate biostatistics course." Jun Zhao, Journal of Biopharmaceutical Statistics, 24, 2014

"A recent request to a statistical professional body by a doctor seeking help with analysing data they had collected was greeted with derision by some of the members of that body. The doctor in question may have been better served by simply purchasing this wide-ranging and accessible book. Medical students would also appreciate the range of topics addressed. I think consultant statisticians would also appreciate the refreshers/introductions to statistical techniques and the SAS code for each. Indeed SAS code is liberally scattered throughout the text, and a couple of SAS macros are referred to in the meta-analysis chapter. The text is supported by ten pages of references and a sizeable index. The code and example data sets can be downloaded from the SAS website." Alice Richardson, International Statistical Review (2013), 81

"Applied Medical Statistics Using SAS is a thorough documentation of statistical methods, inclusive of medical data sets and SAS code. The book would make an excellent reference guide for medical data analysts with access to base SAS 9.3 or a textbook for an introductory and intermediate graduate biostatistics course. [ It] comes to the market at an appropriate time in the extension of statistical applications to the medical industry The thoroughness of procedures and the consideration the authors included in the selection of graphs, SAS code, and theory allow this book to be a resourceful companion for medical analysts. If looking for a broad selection of medical analyses using base SAS 9.3, this is the book for you; in addition, if a particular topic is required for further analyses, the book references additional sources." Journal of Statistical Software, Volume 52, January 2013 "Each chapter in the book is well laid out, contains examples with SAS code, and ends with a concise summary. The chapters in the book contain the right level of information to use SAS to apply different statistical methods. a good overview of how to apply in SAS 9.3 the many possible statistical analysis methods." Caroline Kennedy, Takeda Development Centre Europe Ltd., Statistical Methods for Medical Research, 2015

" a well-organized and thorough exploration of broad coverage in medical statistics. The book is an excellent reference of statistical methods with examples of medical data and SAS codes for statisticians or statistical analysts who are working in the medical/clinical area. It also can be a reference book for an introductory or intermediate graduate biostatistics course." Jun Zhao, Journal of Biopharmaceutical Statistics, 24, 2014

"A recent request to a statistical professional body by a doctor seeking help with analysing data they had collected was greeted with derision by some of the members of that body. The doctor in question may have been better served by simply purchasing this wide-ranging and accessible book. Medical students would also appreciate the range of topics addressed. I think consultant statisticians would also appreciate the refreshers/introductions to statistical techniques and the SAS code for each. Indeed SAS code is liberally scattered throughout the text, and a couple of SAS macros are referred to in the meta-analysis chapter. The text is supported by ten pages of references and a sizeable index. The code and example data sets can be downloaded from the SAS website." Alice Richardson, International Statistical Review (2013), 81

"Applied Medical Statistics Using SAS is a thorough documentation of statistical methods, inclusive of medical data sets and SAS code. The book would make an excellent reference guide for medical data analysts with access to base SAS 9.3 or a textbook for an introductory and intermediate graduate biostatistics course. [ It] comes to the market at an appropriate time in the extension of statistical applications to the medical industry The thoroughness of procedures and the consideration the authors included in the selection of graphs, SAS code, and theory allow this book to be a resourceful companion for medical analysts. If looking for a broad selection of medical analyses using base SAS 9.3, this is the book for you; in addition, if a particular topic is required for further analyses, the book references additional sources." Journal of Statistical Software, Volume 52, January 2013

Preface xiii
The Authors xv
1 An Introduction to SAS
1(40)
1.1 Introduction
1(1)
1.2 User Interface
1(5)
1.2.1 Editor Window
2(2)
1.2.2 Log Window
4(1)
1.2.3 Output Window
4(1)
1.2.4 Results Window
4(1)
1.2.5 Explorer Window
4(1)
1.2.6 Results Viewer Window
5(1)
1.2.7 Options for Displaying Procedure Results
5(1)
1.2.8 Help and Documentation
5(1)
1.3 SAS Programs
6(5)
1.3.1 Program Steps
7(1)
1.3.2 Variable Names and Data Set Names
8(1)
1.3.3 Variable Lists
8(3)
1.4 Reading Data---The Data Step
11(10)
1.4.1 Creating SAS Data Sets from Raw Data
11(1)
1.4.2 Data Statement
11(1)
1.4.3 Infile Statement
12(1)
1.4.4 Input Statement
13(1)
1.4.4.1 List Input
13(1)
1.4.4.2 Column Input
14(1)
1.4.4.3 Formatted Input
15(2)
1.4.4.4 Multiple Lines per Observation
17(1)
1.4.4.5 Multiple Observations per Line
17(1)
1.4.4.6 Delimited Data
17(1)
1.4.5 Reading Data---Proc Import
18(1)
1.4.6 Reading and Writing Excel Files
19(1)
1.4.7 Temporary and Permanent SAS Data Sets---SAS Libraries
20(1)
1.4.8 Reading Data from an Existing SAS Data Set
20(1)
1.5 Modifying SAS Data
21(6)
1.5.1 Creating and Modifying Variables
21(1)
1.5.1.1 Missing Values in Arithmetic Expressions
21(3)
1.5.2 Deleting Variables
24(1)
1.5.3 Deleting Observations
24(1)
1.5.4 Subsetting Data Sets
24(1)
1.5.5 Concatenating and Merging Data Sets
25(1)
1.5.6 Merging Data Sets---Adding Variables
25(1)
1.5.7 Operation of the Data Step
26(1)
1.6 ProcStep
27(1)
1.6.1 Proc Statement
27(1)
1.6.2 Var Statement
27(1)
1.6.3 Where Statement
28(1)
1.6.4 By Statement
28(1)
1.6.5 Class Statement
28(1)
1.7 Global Statements
28(2)
1.7.1 Options
29(1)
1.8 SAS Graphics
30(2)
1.8.1 xy Plots---proc sgplot
30(1)
1.8.2 Summary Plots
31(1)
1.8.3 Panel Plots
32(1)
1.9 ODS---Output Delivery System
32(2)
1.9.1 ODS Procedure Output
33(1)
1.9.1.1 ODS Styles
33(1)
1.10 Saving Output in SAS Data Sets---ods output
34(2)
1.10.1 ODS Graphics
34(2)
1.11 Enhancing Output
36(1)
1.11.1 Variable Labels
36(1)
1.11.2 Value Labels---SAS Formats
36(1)
1.12 SAS Macros
37(2)
1.13 Some Tips for Preventing and Correcting Errors
39(2)
2 Statistics and Measurement in Medicine
41(32)
2.1 Introduction
41(1)
2.2 A Brief History of Medical Statistics
42(4)
2.3 Measurement in Medicine
46(3)
2.3.1 Scales of Measurement
47(1)
2.3.1.1 Nominal or Categorical Measurements
47(1)
2.3.1.2 Ordinal Scale Measurements
47(1)
2.3.1.3 Interval Scales
48(1)
2.3.1.4 Ratio Scales
48(1)
2.4 Assessing Bias and Reliability of Measurements
49(14)
2.4.1 Assessing Reliability and Bias for Binary and Other Categorical Observations
50(7)
2.4.2 Assessing the Reliability of Quantitative Measurements
57(6)
2.5 Diagnostic Tests
63(9)
2.6 Summary
72(1)
3 Clinical Trials
73(36)
3.1 Introduction
73(1)
3.2 Clinical Trials
74(14)
3.2.1 Types of Randomisation
77(3)
3.2.1.1 Blocked Randomisation
80(2)
3.2.1.2 Stratified Randomisation
82(3)
3.2.1.3 Minimisation Method
85(3)
3.3 How Many Participants Do I Need in My Trial?
88(4)
3.4 Analysis of Data from Clinical Trials
92(15)
3.4.1 p-Values and Confidence Intervals
92(2)
3.4.2 Some Examples of Analysis of Data from Clinical Trials Using Familiar Statistical Methods
94(13)
3.5 Summary
107(2)
4 Epidemiology
109(26)
4.1 Introduction
109(1)
4.2 Types of Epidemiological Study
109(5)
4.2.1 Surveys
110(1)
4.2.2 Case-Control Studies
111(1)
4.2.3 Cohort Studies
112(2)
4.3 Relative Risk and Odds Ratios
114(2)
4.4 Sample Size Estimation for Epidemiologic Studies
116(3)
4.4.1 Sample Size Estimation for Case-Control Studies
116(2)
4.4.2 Sample Size Estimation for Cohort Studies
118(1)
4.5 Simple Analyses for Data from Observational Studies
119(13)
4.5.1 Chi-Squared Test for Association
119(1)
4.5.2 Finding a Confidence Interval for the Relative Risk and the Odds Ratio
120(1)
4.5.3 Applying SAS to Analyse Examples of Epidemiological Data
121(4)
4.5.4 Fisher's Test
125(3)
4.5.5 Matched Case-Control Data
128(1)
4.5.6 Stratified 2x2 Tables
129(3)
4.6 Summary
132(3)
5 Meta-Analysis
135(22)
5.1 Introduction
135(3)
5.2 Study Selection
138(2)
5.3 Publication Bias
140(1)
5.4 Statistics of Meta-Analysis
141(3)
5.4.1 Fixed-Effects Model
143(1)
5.4.2 Random-Effects Model
143(1)
5.5 An Example of the Application of Meta-Analysis
144(6)
5.6 Meta-Analysis on Sparse Data
150(2)
5.7 Meta-Regression
152(3)
5.8 Summary
155(2)
6 Analysis of Variance and Covariance
157(30)
6.1 Introduction
157(1)
6.2 A Simple Example of One-Way Analysis of Variance
157(5)
6.2.1 One-Way Analysis of Variance Model
158(1)
6.2.2 Applying the One-Way Analysis of Variance Model to Sickle Cell Disease Data
159(3)
6.3 Multiple Comparison Procedures
162(3)
6.3.1 Planned Comparisons
162(2)
6.3.2 Post Hoc Comparisons
164(1)
6.4 A Factorial Experiment
165(7)
6.4.1 Model for Three-Factor Design
170(2)
6.5 Unbalanced Designs
172(6)
6.5.1 Type I Sums of Squares
174(1)
6.5.2 Type II Sums of Squares
174(1)
6.5.3 Type III Sums of Squares
175(1)
6.5.4 Analysis of Antipyrine Data
176(2)
6.6 Nonparametric Analysis of Variance
178(3)
6.6.1 Kruskal--Wallis Distribution-Free Test for One-Way Analysis of Variance
179(1)
6.6.2 Applying the Kruskal--Wallis Test
180(1)
6.7 Analysis of Covariance
181(5)
6.8 Summary
186(1)
7 Scatter Plots, Correlation, Simple Regression, and Smoothing
187(32)
7.1 Introduction
187(1)
7.2 Scatter Plot and Correlation Coefficient
187(6)
7.3 Simple Linear Regression and Locally Weighted Regression
193(10)
7.4 Locally Weighted Regression
203(2)
7.5 Aspect Ratio of a Scatter Plot
205(4)
7.6 Estimating Bivariate Densities
209(4)
7.7 Scatter Plot Matrices
213(3)
7.8 Summary
216(3)
8 Multiple Linear Regression
219(36)
8.1 Introduction
219(1)
8.2 Multiple Linear Regression Model
219(3)
8.3 Some Examples of the Application of the Multiple Linear Regression Model
222(13)
8.3.1 Effect of the Amount of Anaesthetic Agent Administered during an Operation
222(2)
8.3.2 Mortality and Water Hardness
224(6)
8.3.3 Weight and Physical Measurements in Men
230(5)
8.4 Identifying a Parsimonious Model
235(10)
8.4.1 All Possible Subsets Regression
235(1)
8.4.2 Stepwise Methods
236(9)
8.5 Checking Model Assumptions: Residuals and Other Regression Diagnostics
245(4)
8.6 General Linear Model
249(4)
8.7 Summary
253(2)
9 Logistic Regression
255(30)
9.1 Introduction
255(1)
9.2 Logistic Regression
255(3)
9.3 Two Examples of the Application of Logistic Regression
258(16)
9.3.1 Psychiatric `Caseness'
258(10)
9.3.2 Birth Weight of Babies
268(6)
9.4 Diagnosing a Logistic Regression Model
274(1)
9.5 Logistic Regression for 1:1 Matched Studies
275(6)
9.6 Propensity Scores
281(2)
9.7 Summary
283(2)
10 Generalised Linear Model
285(18)
10.1 Introduction
285(1)
10.2 Generalised Linear Models
285(2)
10.3 Applying the Generalised Linear Model
287(11)
10.3.1 Poisson Regression
288(8)
10.3.2 Regression with Gamma Errors
296(2)
10.4 Residuals for GLMs
298(2)
10.5 Overdispersion
300(2)
10.6 Summary
302(1)
11 Generalised Additive Models
303(22)
11.1 Introduction
303(1)
11.2 Scatter Plot Smoothers
304(8)
11.3 Additive and Generalised Additive Models
312(1)
11.4 Examples of the Application of GAMs
313(11)
11.5 Summary
324(1)
12 Analysis of Longitudinal Data I
325(24)
12.1 Introduction
325(1)
12.2 Graphical Displays of Longitudinal Data
325(8)
12.3 Summary Measure Analysis of Longitudinal Data
333(7)
12.3.1 Choosing Summary Measures
333(1)
12.3.2 Applying the Summary Measure Approach
334(1)
12.3.3 Incorporating Pretreatment Outcome Values into the Summary Measure Approach
335(2)
12.3.4 Dealing with Missing Values When Using the Summary Measure Approach
337(3)
12.4 Summary Measure Approach for Binary Responses
340(7)
12.5 Summary
347(2)
13 Analysis of Longitudinal Data II: Linear Mixed-Effects Models for Normal Response Variables
349(28)
13.1 Introduction
349(1)
13.2 Linear Mixed-Effects Models for Repeated Measures Data
350(20)
13.2.1 Random Intercept and Random Intercept and Slope Models
351(2)
13.2.2 Applying the Random Intercept and Random Intercept and Slope Models
353(17)
13.3 Dropouts in Longitudinal Data
370(5)
13.4 Summary
375(2)
14 Analysis of Longitudinal Data III: Non-Normal Responses
377(22)
14.1 Introduction
377(1)
14.2 Marginal Models and Conditional Models
378(5)
14.2.1 Marginal Models
378(3)
14.2.2 Conditional Models
381(2)
14.3 Analysis of the Respiratory Data
383(8)
14.3.1 Marginal Models
383(5)
14.3.2 Generalised Linear Mixed-Effects Models
388(3)
14.4 Analysis of Epilepsy Data
391(7)
14.4.1 Marginal Models
392(2)
14.4.2 Generalised Linear Mixed-Effects Models
394(4)
14.5 Summary
398(1)
15 Survival Analysis
399(22)
15.1 Introduction
399(1)
15.2 Survivor Function and the Hazard Function
400(10)
15.2.1 Survivor Function
400(5)
15.2.2 Hazard Function
405(5)
15.3 Comparing Groups of Survival Times
410(7)
15.3.1 Log-Rank Test
412(3)
15.3.2 Stratified Tests
415(2)
15.4 Sample Size Estimation
417(2)
15.5 Summary
419(2)
16 Cox's Proportional Hazards Models for Survival Data
421(38)
16.1 Introduction
421(1)
16.2 Modelling the Hazard Function: Cox's Regression
421(24)
16.2.1 Examples of Cox's Regression
424(4)
16.2.2 Estimating the Baseline Hazard Function
428(10)
16.2.3 Checking Assumptions in Cox's Regression
438(4)
16.2.4 Stratified Cox's Regression
442(3)
16.3 Time-Varying Covariates
445(7)
16.4 Random-Effects Models for Survival Data
452(5)
16.5 Summary
457(2)
17 Bayesian Methods
459(24)
17.1 Introduction
459(1)
17.2 Bayesian Estimation
460(3)
17.3 Markov Chain Monte Carlo
463(1)
17.4 Prior Distributions
464(1)
17.5 Model Selection When Using a Bayesian Approach
465(1)
17.6 Some Examples of the Application of Bayesian Statistics
465(16)
17.6.1 Psychiatric `Caseness'
465(9)
17.6.2 Cardiac Surgery in Babies
474(7)
17.7 Summary
481(2)
18 Missing Values
483(26)
18.1 Introduction
483(1)
18.2 Patterns of Missing Data
484(1)
18.3 Missing Data Mechanisms
484(2)
18.4 Exploring Missingness
486(7)
18.5 Dealing with Missing Values
493(1)
18.6 Imputing Missing Values
494(2)
18.7 Analysing Multiply Imputed Data
496(1)
18.8 Some Examples of the Application of Multiple Imputation
497(10)
18.8.1 Air Pollution in US Cities
497(5)
18.8.2 Growth of Danish Boys
502(5)
18.9 Summary
507(2)
References 509(10)
Index 519
Geoff Der, Brian S. Everitt
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