Regression Methods in Biostatistics (eBook)

Linear, Logistic, Survival, and Repeated Measures Models

Eric Vittinghoff, David V. Glidden, Stephen C. Shiboski, Charles E. McCulloch (Autoren)

eBook Download: PDF

2006 | 1. Auflage
XVI, 340 Seiten
Springer-Verlag
978-0-387-27255-9 (ISBN)

Infos

Theprimarybiostatisticaltoolsinmodernmedicalresearcharesingle-outcome, multiple-predictor methods: multiple linear regression for continuous o- comes, logistic regression for binary outcomes, and the Cox proportional h- ardsmodelfortime-to-eventoutcomes. Morerecently,generalizedlinearm- els and regression methods for repeated outcomes have come into widespread use in the medical research literature. Applying these methods and interpr- ing the results requires some introduction. However, introductory statistics courses have no time to spend on such topics and hence they are often r- egated to a third or fourth course in a sequence. Books tend to have either very brief coverage or to be treatments of a single topic and more theoretical than the typical researcher wants or needs. Our goal in writing this book was to provide an accessible introduction to multipredictor methods, emphasizing their proper use and interpretation. We feel strongly that this can only be accomplished by illustrating the te- niques using a variety of real datasets. We have incorporated as little theory as feasible. Further, we have tried to keep the book relatively short and to the point. Our hope in doing so is that the important issues and similarities between the methods, rather than their di?erences, will come through. We hope this book will be attractive to medical researchers needing familiarity with these methods and to students studying statistics who would like to see them applied to real data.

Inhalt

Preface 6
Contents 8
1 Introduction 15
1.1 Example: Treatment of Back Pain 15
1.2 The Family of Multipredictor Regression Methods 16
1.3 Motivation for Multipredictor Regression 17
1.4 Guide to the Book 18
2 Exploratory and Descriptive Methods 20
2.1 Data Checking 20
2.2 Types Of Data 21
2.3 One-Variable Descriptions 21
2.4 Two-Variable Descriptions 30
2.5 Multivariable Descriptions 36
2.6 Problems 39
3 Basic Statistical Methods 41
3.1 t-Test and Analysis of Variance 41
3.2 Correlation Coefficient 47
3.3 Simple Linear Regression Model 48
3.4 Contingency Table Methods for Binary Outcomes 56
3.5 Basic Methods for Survival Analysis 66
3.6 Bootstrap Confidence Intervals 74
3.7 Interpretation of Negative Findings 75
3.8 Further Notes and References 77
3.9 Problems 77
3.10 Learning Objectives 79
4 Linear Regression 80
4.1 Example: Exercise and Glucose 81
4.2 Multiple Linear Regression Model 83
4.3 Categorical Predictors 87
4.4 Confounding 94
4.5 Mediation 106
4.6 Interaction 109
4.7 Checking Model Assumptions and Fit 120
4.8 Summary 138
4.9 Further Notes and References 138
4.10 Problems 139
4.11 Learning Objectives 142
5 Predictor Selection 143
5.1 Diagramming the Hypothesized Causal Model 145
5.2 Prediction 147
5.3 Evaluating a Predictor of Primary Interest 150
5.4 Identifying Multiple Important Predictors 154
5.5 Some Details 157
5.6 Summary 163
5.7 Further Notes and References 164
5.8 Problems 165
5.9 Learning Objectives 166
6 Logistic Regression 167
6.1 Single Predictor Models 168
6.2 Multipredictor Models 177
6.3 Case-Control Studies 193
6.4 Checking Model Assumptions and Fit 198
6.5 Alternative Strategies for Binary Outcomes 206
6.6 Likelihood 213
6.7 Summary 216
6.8 Further Notes and References 217
6.9 Problems 217
6.10 Learning Objectives 219
7 Survival Analysis 220
7.1 Survival Data 220
7.2 Cox Proportional Hazards Model 224
7.3 Extensions to the Cox Model 240
7.4 Checking Model Assumptions and Fit 247
7.5 Some Details 254
7.6 Summary 258
7.7 Further Notes and References 258
7.8 Problems 259
7.9 Learning Objectives 260
8 Repeated Measures and Longitudinal Data Analysis 262
8.1 A Simple Repeated Measures Example: Fecal Fat 263
8.2 Hierarchical Data 268
8.3 Longitudinal Data 271
8.4 Generalized Estimating Equations 275
8.5 Random Effects Models 283
8.6 Example: Cardiac Injury Following Brain Hemorrhage 290
8.7 Summary 295
8.8 Further Notes and References 295
8.9 Problems 296
8.10 Learning Objectives 297
9 Generalized Linear Models 299
9.1 Example: Treatment for Depression 299
9.2 Example: Costs of Phototherapy 303
9.3 Generalized Linear Models 305
9.4 Summary 309
9.5 Further Notes and References 309
9.6 Problems 310
9.7 Learning Objectives 311
10 Complex Surveys 312
10.1 Example: NHANES 314
10.2 Probability Weights 314
10.3 Variance Estimation 317
10.4 Summary 321
10.5 Further Notes and References 321
10.6 Problems 322
10.7 Learning Objectives 323
11 Summary 324
11.1 Introduction 324
11.2 Selecting Appropriate Statistical Methods 325
11.3 Planning and Executing a Data Analysis 326
11.4 Further Notes and References 328
References 330
Index 339

Produkt-Details

• Erscheint lt. Verlag: 30.3.2006
• Reihe/Serie: Statistics for Biology and Health
• Reihe/Serie: Statistics for Biology and Health
• Zusatzinfo: XVI, 340 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Mathematik ► Statistik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Medizin / Pharmazie ► Allgemeines / Lexika
• Themenwelt: Studium ► Querschnittsbereiche ► Epidemiologie / Med. Biometrie
• Themenwelt: Naturwissenschaften ► Biologie
• Themenwelt: Technik
• Schlagworte: applied regression methods for biomedical research • confounding, mediation, and interaction • Data Analysis • Generalized Linear Model • linear, logistic, generalized linear, survival (Cox), GEE, a • linear, logistic, generalized linear, survival (Cox), GEE, and mixed models • linear regression • Logistic Regression • model selection and checking • Regression Analysis • Stata • statistical computing with STATA • Statistics • Survival Analysis • Variance
• ISBN-10: 0-387-27255-0 / 0387272550
• ISBN-13: 978-0-387-27255-9 / 9780387272559