Statistical analysis of medical data using SAS:
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton [u.a.]
Chapman & Hall/CRC
2006
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Einschl. bibliographischer Referenzen (S. 411-415) und Index |
| Physical Description: | 428 S. Ill., graph. Darst. |
| ISBN: | 9781584884699 158488469X |
Staff View
MARC
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| adam_text | ^ Contents
1 An Introduction to SAS 1
1.1 Introduction 1
1.2 The User Interface 2
1.2.1 The Editor Window 4
1.2.2 The Log Window 4
1.2.3 The Output Window 5
1.2.4 The Results Window 5
1.2.5 The Explorer Window 6
1.2.6 Some Other Menus 6
1.3 The SAS Language 6
1.3.1 Program Steps 7
1.3.2 Variable Names and Data Set Names 8
1.3.3 Variable Lists 8
1.4 Reading Data — The Data Step 8
1.4.1 Creating SAS Data Sets from Raw Data 9
1.4.2 The Data Statement 10
1.4.3 The Infile Statement 10
1.4.4 The Input Statement 12
1.4.5 Proc Import 18
1.4.6 Reading Data from Other Programs and Databases 18
1.4.7 Temporary and Permanent SAS Data Sets — SAS Libraries... 19
1.4.8 Reading Data from an Existing SAS Data Set 19
1.5 Modifying SAS Data 20
1.5.1 Creating and Modifying Variables 20
1.5.2 Deleting Variables 23
1.5.3 Deleting Observations 23
1.5.4 Subsetting Data Sets 24
1.5.5 Concatenating and Merging Data Sets 24
1.5.6 Merging Data Sets — Adding Variables 25
1.5.7 The Operation of the Data Step 26
1.6 The Proc Step 26
1.6.1 The Proc Statement 27
1.6.2 The Var Statement 27
1.6.3 The Where Statement 27
1.6.4 The By Statement 27
1.6.5 The Class Statement 28
1.7 Global Statements 28
1.7.1 Options 29
1.8 SAS Graphics 29
1.8.1 ProcGplot 30
1.8.2 Overlaid Graphs 32
1.8.3 Viewing and Printing Graphics 32
1.9 ODS — The Output Delivery System 33
1.10 SAS Macros 35
1.11 Some Tips for Preventing and Correcting Errors 37
2 Describing and Summarizing Data 39
2.1 Introduction 39
2.2 Graphing and Summarizing Continuous Data 39
2.3 Categorical Data 61
2.4 Summary 67
3 Basic Inference 69
3.1 Introduction 69
3.2 Simple Inference for Continuous Variables 69
3.2.1 Tests for Independent Samples 69
3.2.2 Tests for Dependent Samples 74
3.3 Simple Inference for Categorical Data 78
3.3.1 Testing for Independence in Contingency Tables 78
3.3.2 Fisher s Exact Test 83
3.3.3 The Mantel Haenszel Test 87
3.3.4 McNemar s Test for Correlated Proportions 89
3.4 Summary 90
4 Scatterplots, Correlation, Simple Regression, and Smoothing 93
4.1 Introduction 93
4.2 The Scatterplot 93
4.2.1 The Aspect Ratio of a Scatterplot 98
4.2.2 Estimating Bivariate Densities 103
4.3 Scatterplot Matrices 108
4.4 Simple Linear Regression and Locally Weighted Regression Ill
4.5 Summary 119
5 Analysis of Variance and Covariance 121
5.1 Introduction 121
5.2 A Simple One Way Example 121
5.3 Multiple Comparison Procedures 126
5.4 A Factorial Experiment 128
5.5 Unbalanced Designs 139
5.5.1 Type I Sums of Squares 139
5.5.2 Type II Sums of Squares 140
5.5.3 Type III Sums of Squares 140
5.5.4 Analysis of Antipyrine Data 141
5.6 Nonparametric Analysis of Variance 143
5.7 Analysis of Covariance 145
5.8 Summary 150
6 Multiple Regression 151
6.1 Introduction 151
6.2 The Multiple Linear Regression Model 151
6.2.1 Anesthesia Example 154
6.2.2 Mortality and Water Hardness 156
6.2.3 Weight and Physical Measurement in Men 162
6.3 Identifying a Parsimonious Model 166
6.3.1 All Possible Subsets Regression 167
6.3.2 Stepwise Methods 168
6.4 Checking Model Assumptions: Residuals and Other
Regression Diagnostics 177
6.5 The General Linear Model 179
6.6 Summary 180
7 Logistic Regression 183
7.1 Introduction 183
7.2 Logistic Regression 183
7.3 Two Examples of the Application of Logistic Regression 187
7.3.1 Psychiatric Caseness 187
7.3.2 Birthweight of Babies 194
7.4 Diagnosing a Logistic Regression Model 201
7.5 Logistic Regression for 1 1 Matched Studies 206
7.6 Summary 209
8 The Generalized Linear Model 211
8.1 Introduction 211
8.2 Generalized Linear Models 211
8.3 Applying the Generalized Linear Model 212
8.3.1 Poisson Regression 214
8.3.2 Regression with Gamma Errors 219
8.4 Residuals for GLMs 220
8.5 Overdispersion 223
8.6 Summary 224
9 Generalized Additive Models 227
9.1 Introduction 227
9.2 Scatterplot Smoothers 228
9.3 Additive and Generalized Additive Models 238
9.4 Examples of the Application of GAMs 240
9.5 Summary 251
10 Nonlinear Regression Models 255
10.1 Introduction 255
10.2 Nonlinear Regression 256
10.3 Some Examples of Nonlinear Regression Models 257
10.4 Summary 266
11 The Analysis of Longitudinal Data 1 269
11.1 Introduction 269
11.2 Graphical Displays of Longitudinal Data 269
11.3 Summary Measure Analysis of Longitudinal Data 275
11.3.1 Choosing Summary Measures 276
11.3.2 Applying the Summary Measure Approach 277
11.3.3 Incorporating Pretreatment Outcome Values into the
Summary Measure Approach 278
11.3.4 Dealing with Missing Values When Using the Summary
Measure Approach 279
11.4 Summary Measure Approach for Binary Responses 282
11.5 Summary 291
12 The Analysis of Longitudinal Data II: Models for Normal
Response Variables 293
12.1 Introduction 293
12.2 Linear Mixed Effects Models for Repeated Measures Data 294
12.3 Dropouts in Longitudinal Data 312
12.4 Summary 322
13 The Analysis of Longitudinal Data III: Non Normal
Responses 323
13.1 Introduction 323
13.2 Marginal Models and Conditional Models 327
13.2.1 Marginal Models 327
13.2.2 Conditional Models 329
13.3 Analysis of the Respiratory Data 329
13.4 Analysis of Epilepsy Data 335
13.5 Summary 343
14 Survival Analysis 345
14.1 Introduction 345
14.2 The Survivor Function and the Hazard Function 345
14.2.1 Survivor Function 346
14.2.2 The Hazard Function 348
14.3 Comparing Survival Functions 350
14.4 Cox s Regression; the Proportional Hazards Model 352
14.5 Time Varying Covariates 361
14.6 Summary 372
15 Analysing Multivariate Data: Principal Components and
Cluster Analysis 373
15.1 Introduction 373
15.2 Principal Components Analysis 374
15.3 Examples of Principal Components Analysis 379
15.3.1 Head Measurements 379
15.3.2 Expectations of Life 381
15.3.3 Drug Usage by American Students 388
15.4 Cluster Analysis 391
15.5 Agglomerative Hierarchical Clustering 393
15.6 Summary 407
References 411
Index 417
|
| adam_txt |
^ Contents
1 An Introduction to SAS 1
1.1 Introduction 1
1.2 The User Interface 2
1.2.1 The Editor Window 4
1.2.2 The Log Window 4
1.2.3 The Output Window 5
1.2.4 The Results Window 5
1.2.5 The Explorer Window 6
1.2.6 Some Other Menus 6
1.3 The SAS Language 6
1.3.1 Program Steps 7
1.3.2 Variable Names and Data Set Names 8
1.3.3 Variable Lists 8
1.4 Reading Data — The Data Step 8
1.4.1 Creating SAS Data Sets from Raw Data 9
1.4.2 The Data Statement 10
1.4.3 The Infile Statement 10
1.4.4 The Input Statement 12
1.4.5 Proc Import 18
1.4.6 Reading Data from Other Programs and Databases 18
1.4.7 Temporary and Permanent SAS Data Sets — SAS Libraries. 19
1.4.8 Reading Data from an Existing SAS Data Set 19
1.5 Modifying SAS Data 20
1.5.1 Creating and Modifying Variables 20
1.5.2 Deleting Variables 23
1.5.3 Deleting Observations 23
1.5.4 Subsetting Data Sets 24
1.5.5 Concatenating and Merging Data Sets 24
1.5.6 Merging Data Sets — Adding Variables 25
1.5.7 The Operation of the Data Step 26
1.6 The Proc Step 26
1.6.1 The Proc Statement 27
1.6.2 The Var Statement 27
1.6.3 The Where Statement 27
1.6.4 The By Statement 27
1.6.5 The Class Statement 28
1.7 Global Statements 28
1.7.1 Options 29
1.8 SAS Graphics 29
1.8.1 ProcGplot 30
1.8.2 Overlaid Graphs 32
1.8.3 Viewing and Printing Graphics 32
1.9 ODS — The Output Delivery System 33
1.10 SAS Macros 35
1.11 Some Tips for Preventing and Correcting Errors 37
2 Describing and Summarizing Data 39
2.1 Introduction 39
2.2 Graphing and Summarizing Continuous Data 39
2.3 Categorical Data 61
2.4 Summary 67
3 Basic Inference 69
3.1 Introduction 69
3.2 Simple Inference for Continuous Variables 69
3.2.1 Tests for Independent Samples 69
3.2.2 Tests for Dependent Samples 74
3.3 Simple Inference for Categorical Data 78
3.3.1 Testing for Independence in Contingency Tables 78
3.3.2 Fisher's Exact Test 83
3.3.3 The Mantel Haenszel Test 87
3.3.4 McNemar's Test for Correlated Proportions 89
3.4 Summary 90
4 Scatterplots, Correlation, Simple Regression, and Smoothing 93
4.1 Introduction 93
4.2 The Scatterplot 93
4.2.1 The Aspect Ratio of a Scatterplot 98
4.2.2 Estimating Bivariate Densities 103
4.3 Scatterplot Matrices 108
4.4 Simple Linear Regression and Locally Weighted Regression Ill
4.5 Summary 119
5 Analysis of Variance and Covariance 121
5.1 Introduction 121
5.2 A Simple One Way Example 121
5.3 Multiple Comparison Procedures 126
5.4 A Factorial Experiment 128
5.5 Unbalanced Designs 139
5.5.1 Type I Sums of Squares 139
5.5.2 Type II Sums of Squares 140
5.5.3 Type III Sums of Squares 140
5.5.4 Analysis of Antipyrine Data 141
5.6 Nonparametric Analysis of Variance 143
5.7 Analysis of Covariance 145
5.8 Summary 150
6 Multiple Regression 151
6.1 Introduction 151
6.2 The Multiple Linear Regression Model 151
6.2.1 Anesthesia Example 154
6.2.2 Mortality and Water Hardness 156
6.2.3 Weight and Physical Measurement in Men 162
6.3 Identifying a Parsimonious Model 166
6.3.1 All Possible Subsets Regression 167
6.3.2 Stepwise Methods 168
6.4 Checking Model Assumptions: Residuals and Other
Regression Diagnostics 177
6.5 The General Linear Model 179
6.6 Summary 180
7 Logistic Regression 183
7.1 Introduction 183
7.2 Logistic Regression 183
7.3 Two Examples of the Application of Logistic Regression 187
7.3.1 Psychiatric 'Caseness' 187
7.3.2 Birthweight of Babies 194
7.4 Diagnosing a Logistic Regression Model 201
7.5 Logistic Regression for 1 1 Matched Studies 206
7.6 Summary 209
8 The Generalized Linear Model 211
8.1 Introduction 211
8.2 Generalized Linear Models 211
8.3 Applying the Generalized Linear Model 212
8.3.1 Poisson Regression 214
8.3.2 Regression with Gamma Errors 219
8.4 Residuals for GLMs 220
8.5 Overdispersion 223
8.6 Summary 224
9 Generalized Additive Models 227
9.1 Introduction 227
9.2 Scatterplot Smoothers 228
9.3 Additive and Generalized Additive Models 238
9.4 Examples of the Application of GAMs 240
9.5 Summary 251
10 Nonlinear Regression Models 255
10.1 Introduction 255
10.2 Nonlinear Regression 256
10.3 Some Examples of Nonlinear Regression Models 257
10.4 Summary 266
11 The Analysis of Longitudinal Data 1 269
11.1 Introduction 269
11.2 Graphical Displays of Longitudinal Data 269
11.3 Summary Measure Analysis of Longitudinal Data 275
11.3.1 Choosing Summary Measures 276
11.3.2 Applying the Summary Measure Approach 277
11.3.3 Incorporating Pretreatment Outcome Values into the
Summary Measure Approach 278
11.3.4 Dealing with Missing Values When Using the Summary
Measure Approach 279
11.4 Summary Measure Approach for Binary Responses 282
11.5 Summary 291
12 The Analysis of Longitudinal Data II: Models for Normal
Response Variables 293
12.1 Introduction 293
12.2 Linear Mixed Effects Models for Repeated Measures Data 294
12.3 Dropouts in Longitudinal Data 312
12.4 Summary 322
13 The Analysis of Longitudinal Data III: Non Normal
Responses 323
13.1 Introduction 323
13.2 Marginal Models and Conditional Models 327
13.2.1 Marginal Models 327
13.2.2 Conditional Models 329
13.3 Analysis of the Respiratory Data 329
13.4 Analysis of Epilepsy Data 335
13.5 Summary 343
14 Survival Analysis 345
14.1 Introduction 345
14.2 The Survivor Function and the Hazard Function 345
14.2.1 Survivor Function 346
14.2.2 The Hazard Function 348
14.3 Comparing Survival Functions 350
14.4 Cox's Regression; the Proportional Hazards Model 352
14.5 Time Varying Covariates 361
14.6 Summary 372
15 Analysing Multivariate Data: Principal Components and
Cluster Analysis 373
15.1 Introduction 373
15.2 Principal Components Analysis 374
15.3 Examples of Principal Components Analysis 379
15.3.1 Head Measurements 379
15.3.2 Expectations of Life 381
15.3.3 Drug Usage by American Students 388
15.4 Cluster Analysis 391
15.5 Agglomerative Hierarchical Clustering 393
15.6 Summary 407
References 411
Index 417 |
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| id | DE-604.BV021325802 |
| illustrated | Illustrated |
| index_date | 2024-07-02T14:00:18Z |
| indexdate | 2024-07-09T20:35:43Z |
| institution | BVB |
| isbn | 9781584884699 158488469X |
| language | English |
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| spellingShingle | Der, Geoff Everitt, Brian 1944- Statistical analysis of medical data using SAS SAS (Computer file) Statistics cabt Software cabt SAS cabt Dossiers médicaux - Informatique Médecine - Informatique Datenverarbeitung Medizin Statistik Data Interpretation, Statistical Mathematical Computing Medical records Data processing Medicine Data processing SAS 9.1 (DE-588)4785838-2 gnd |
| subject_GND | (DE-588)4785838-2 |
| title | Statistical analysis of medical data using SAS |
| title_auth | Statistical analysis of medical data using SAS |
| title_exact_search | Statistical analysis of medical data using SAS |
| title_exact_search_txtP | Statistical analysis of medical data using SAS |
| title_full | Statistical analysis of medical data using SAS Geoff Der ; Brian S. Everitt |
| title_fullStr | Statistical analysis of medical data using SAS Geoff Der ; Brian S. Everitt |
| title_full_unstemmed | Statistical analysis of medical data using SAS Geoff Der ; Brian S. Everitt |
| title_short | Statistical analysis of medical data using SAS |
| title_sort | statistical analysis of medical data using sas |
| topic | SAS (Computer file) Statistics cabt Software cabt SAS cabt Dossiers médicaux - Informatique Médecine - Informatique Datenverarbeitung Medizin Statistik Data Interpretation, Statistical Mathematical Computing Medical records Data processing Medicine Data processing SAS 9.1 (DE-588)4785838-2 gnd |
| topic_facet | SAS (Computer file) Statistics Software SAS Dossiers médicaux - Informatique Médecine - Informatique Datenverarbeitung Medizin Statistik Data Interpretation, Statistical Mathematical Computing Medical records Data processing Medicine Data processing SAS 9.1 |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=014646146&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT dergeoff statisticalanalysisofmedicaldatausingsas AT everittbrian statisticalanalysisofmedicaldatausingsas |