Applied longitudinal data analysis for medical science: a practical guide
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Cambridge
Cambridge University Press
2023
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| Edition: | Third edition |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis Klappentext |
| Physical Description: | xii, 258 Seiten Illustrationen |
| ISBN: | 9781009288033 9781009288040 |
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Content Preface page xi Acknowledgements 1. 2. xii Introduction 1 1.1 Introduction 1 1.2 Study Design 1 1.2.1 Observational Longitudinal Studies 2 1.3 General Approach 4 1.4 Prior Knowledge 4 1.5 Example 4 1.6 Software 5 1.7 Data Structure 6 1.8 What is New in the Third Edition 6 Continuous Outcome Variables 7 2.1 Two Measurements 7 2.1.1 Example 7 2.2 Non-parametric Equivalent of the Paired t-test 8 2.2.1 Example 9 2.3 More than Two Measurements 9 2.3.1 The Univariate Approach: A Numerical Example 12 2.3.2 The Shape of the Relationship between an Outcome Variable and Time 13 2.3.2.1 A Numerical Example 14 2.3.3 Example 15 2.4 The Univariate or the Multivariate Approach? 19 2.5 Comparing Groups 19 2.5.1 The Univariate Approach: A Numerical Example 20 2.5.2 Example 21 2.6 Comments 25 2.7 Post-hoc Procedures 25 2.8 Different Contrasts 26 2.8.1 Example 26 2.9 Non-parametric Equivalent of GLM for Repeated Measures 29 2.9.1 Example 30 3. Continuous Outcome Variables: Regression-based Methods 31 3.1 Introduction 31 3.2 Longitudinal Regression Methods 3.3 Mixed Model Analysis 32 3.3.1 Introduction 32 3.3.2 Mixed Models for Longitudinal Data Analysis 32 3.3.3 Example 34 3.3.4 Interpretation of the Regression Coefficient 38 335 Comments 4! 3.4 Generalised Estimating Equations 3.4.1 Introduction 42 3.4.2 Correlation Structures 42 3.4.3 Example 44 3.4.3.1 Different Correlation Structures 31 42 46 3.5 Comparison between Mixed Model Analysis and GEE Analysis 48 3.6 The Adjustment for Covariance Method 49 3.6.1 Example 50 3.6.2 Extension of Mixed Model Analysis 54 3.6.3 Comments 54 4.
The Modelling of Time 56 4.1 Growth Curve Analysis 56 4.2 Comparing Groups 60 4.3 Adjustment for Time 64 4.3.1 Time versus Age 68 4.4 Interaction with Time 69 4.5 Classification of Subjects with Different Growth Trajectories 70 5. Models to Disentangle the Between- and Within-subjects Relationship 76 5.1 Introduction 76 5.2 Hybrid Models 76 5.2.1 Example 76 5.2.2 Direct Estimation of the Hybrid Model 78 5.2.3 Hybrid Models with Categorical Time dependent Covariates 80 vii
Content 5.2.4 Comments 82 5.3 Models to Estimate the Withinsubjects Part of the Longitudinal Relationship 82 5.3.1 . Introduction 82 5.3.2 Model of Changes 83 532.1 Example 6. 8. viii 90 111 Dichotomous Outcome Variables 116 7.1 Two Measurements 116 7.2 More than Two Measurements 117 7.3 Comparing Groups 117 7.4 Example 117 7.4.1 Introduction 117 7.4.2 Development over Time 117 7.4.3 Comparing Groups 119 7.5 Longitudinal Regression Methods 119 7.5.1 Introduction 119 7.5.2 Generalised Estimating Equations 121 7.5.3 Mixed Model Analysis 124 7.5.4 Comparison between GEE Analysis and Mixed Model Analysis 127 7.5.5 The Adjustment for Covariance Method 130 7.5.6 Models to Disentangle the Betweenand Within-subjects Relationship 130 7.5.7 Comments 133 Categorical and Count Outcome Variables 134 8.1 Categorical Outcome Variables 82.1.1 Introduction 143 8.2.12 GEE Analysis 143 8.2.1. Causality in Observational Longitudinal Studies 92 6.1 Time-lag Models 92 6.1.1 Example 92 6.1.2 Comments 92 6.2 Longitudinal Mediation Models 94 6.2.1 Example 96 6.2.2 Comments 103 6.3 Other Methods that Claim to Estimate Causal Relationships 106 6.3.1 G-methods 107 6.3.2 Joint models 110 63.2.1 Example 7. 88 88 5.3.4 Comments 138 141 8.2 Count Outcome Variables 8.2.1 Example 143 85 5.3.3 Autoregressive Model Two Measurements 134 More than Two Measurements 135 Comparing Groups 135 Example 135 Regression-based Methods 136 8.1.5.1 Example 83 5.322 Another Example 533.1 Example 8.1.1 8.1.2 8.1.3 8.1.4 8.1.5 3 Mixed Model Analysis 9. Outcome Variables with Floor or Ceiling Effects 152 9.1 Introduction 152
9.2 Tobit Mixed Model Analysis 153 9.2.1 Example 153 9.3 Longitudinal Two-part Models 159 9.3.1 Example 160 9.3.2 Comments 162 10. Analysis of Longitudinal Intervention Studies 164 10.1 Introduction 164 10.2 Continuous Outcome Variables 164 10.2.1 Randomised Controlled Trials with One Follow-up Measurement 165 102.1.1 Example 168 10.2.2 Randomised Controlled Trials with More than One Follow up Measurement 172 10.2.2.1 Simple Analysis 175 10.2.2.2 Summary Statistics 177 10.22.3 Generalised Linear Model for Repeated Measures 178 10.22.4 Generalised Linear Model for Repeated Measures Adjusted for Baseline 10.22.5 Regression-based Methods 178 178 10.3 Dichotomous Outcome Variables 187 10.3.1 Introduction 187 10.3.2 Simple Analysis 188 10.3.3 Regression-based Methods 189 10.3.4 Other Methods 191 10.4 Stepped Wedge Designs 195 10.5 Comments 134 146 8.2.2 Comparison between GEE Analysis and Mixed Model Analysis 147 8.2.3 Negative Binomial Regression Analysis 148 8.2.4 Comments 150 196
Content 10.6 Beyond the Randomised Controlled Trial 197 10.6.1 Difference in Difference Method 198 10.6.1.1 Example 198 10.6.1.2 Comments 199 11. Missing Data in Longitudinal Studies 201 11.1 Introduction 201 11.2 Informative or Non-informative Missing Data 201 11.3 Example 202 11.3.1 Generating Datasets with Missing Data 202 11.3.2 Analysis of Determinants for Missing Data 203 11.4 Analysis Performed on Datasets with Missing Data 204 11.5 Imputation Methods 205 11.5.1 Continuous Variables 205 115.1.1 Cross-sectional Imputation Methods 115.12 Longitudinal Imputation Methods 115.13 Comments 205 206 206 115.1.4 Multiple Imputation 206 11.5.2 Dichotomous and Categorical Variables 11.5.3 Example 207 207 207 1153.1 Continuous Variables 11532 Multiple Imputation in Combination with Mixed Model Analysis? 11533 Additional Analyses 209 210 115.3.4 Dichotomous Variables 210 11.5.4 Comments 212 11.6 Alternative Methods 213 11.7 GEE Analysis or Mixed Model Analysis for the Analysis of Datasets with Missing Data? 214 11.8 Conclusions 214 12. Sample Size Calculations 12.1 Introduction 216 12.2 Example 218 12.3 Comment 219 216 13. Software for Longitudinal Data Analysis 220 13.1 Introduction 220 13.2 GEE Analysis with a Continuous Outcome Variable 220 13.2.1 STATA 220 13.2.2 SAS 220 13.2.3 R 221 13.2.4 SPSS 222 13.2.5 Overview 223 13.3 GEE Analysis with a Dichotomous Outcome Variable 224 13.3.1 STATA 224 13.3.2 SAS 224 13.3.3 R 224 13.3.4 SPSS 224 13.3.5 Overview 224 13.4 Mixed Model Analysis with a Continuous Outcome Variable 226 13.4.1 Introduction 226 13.4.2 STATA 226 13.4.3 SAS 227
13.4.4 R 229 13.4.5 SPSS 231 13.4.6 Overview 235 13.5 Mixed Model Analysis with a Dichotomous Outcome Variable 235 13.5.1 Introduction 235 13.5.2 STATA 235 13.5.3 SAS 236 13.5.4 R 239 13.5.5 SPSS 240 13.5.6 Overview 242 References 243 Index 251 ІХ
Essential for non-statisticians and researchers working with longitudinal data from medical studies, this updated new edition discusses the most important techniques available for analysing data of this type. Using non-technical language, the book explores simple, methods such as the paired t-test and summary statistics as well as hnore sophisticated regression-based methods, including mixed model analysis. The emphasis of the discussion lies in the interpretation of the results of these different methods, covering data analysis with continuous, dichotomous, categorical and other outcome variables. Datasets used throughout the book are provided, enabling readers to re֊analyse the examples as they make their way through chapters and improve their understanding of the material. Finally, an extensive and practical overview of, and comparison between, different software packages is provided. Readers will be able to use this book as a practical manual in their everyday work without needing a strong background in statistics. JOS W. R. TWISK is a Professor in the Department of Epidemiology and Data Science at Amsterdam UMC, Amsterdam, The Netherlands. He specialises in the methodological field of longitudinal data analysis and multilevel/mixed model analysis, and is head of the expertise center for Applied Longitudinal Data Analysis at the Amsterdam UMC. Review of previous edition: Overall, the book is well written, and the material is rich and carefully organised . the book is a welcome reference manual or practical guide foi practitioners of statistical methods in (but not
limited to) epidemiological and clinical studies. The book's main value is in its rather comprehensive presentation of a collection of longitudinal data analyses arising from different research questions. In this sense, the book is unique. Some practitioners of statistics may have struggled to learn longitudinal data analysis by reading manuals of software packages. This book is potentially of great benefit to them.' Journal of the American Statistical Association |
| adam_txt |
Content Preface page xi Acknowledgements 1. 2. xii Introduction 1 1.1 Introduction 1 1.2 Study Design 1 1.2.1 Observational Longitudinal Studies 2 1.3 General Approach 4 1.4 Prior Knowledge 4 1.5 Example 4 1.6 Software 5 1.7 Data Structure 6 1.8 What is New in the Third Edition 6 Continuous Outcome Variables 7 2.1 Two Measurements 7 2.1.1 Example 7 2.2 Non-parametric Equivalent of the Paired t-test 8 2.2.1 Example 9 2.3 More than Two Measurements 9 2.3.1 The Univariate Approach: A Numerical Example 12 2.3.2 The Shape of the Relationship between an Outcome Variable and Time 13 2.3.2.1 A Numerical Example 14 2.3.3 Example 15 2.4 The Univariate or the Multivariate Approach? 19 2.5 Comparing Groups 19 2.5.1 The Univariate Approach: A Numerical Example 20 2.5.2 Example 21 2.6 Comments 25 2.7 Post-hoc Procedures 25 2.8 Different Contrasts 26 2.8.1 Example 26 2.9 Non-parametric Equivalent of GLM for Repeated Measures 29 2.9.1 Example 30 3. Continuous Outcome Variables: Regression-based Methods 31 3.1 Introduction 31 3.2 Longitudinal Regression Methods 3.3 Mixed Model Analysis 32 3.3.1 Introduction 32 3.3.2 Mixed Models for Longitudinal Data Analysis 32 3.3.3 Example 34 3.3.4 Interpretation of the Regression Coefficient 38 335 Comments 4! 3.4 Generalised Estimating Equations 3.4.1 Introduction 42 3.4.2 Correlation Structures 42 3.4.3 Example 44 3.4.3.1 Different Correlation Structures 31 42 46 3.5 Comparison between Mixed Model Analysis and GEE Analysis 48 3.6 The Adjustment for Covariance Method 49 3.6.1 Example 50 3.6.2 Extension of Mixed Model Analysis 54 3.6.3 Comments 54 4.
The Modelling of Time 56 4.1 Growth Curve Analysis 56 4.2 Comparing Groups 60 4.3 Adjustment for Time 64 4.3.1 Time versus Age 68 4.4 Interaction with Time 69 4.5 Classification of Subjects with Different Growth Trajectories 70 5. Models to Disentangle the Between- and Within-subjects Relationship 76 5.1 Introduction 76 5.2 Hybrid Models 76 5.2.1 Example 76 5.2.2 Direct Estimation of the Hybrid Model 78 5.2.3 Hybrid Models with Categorical Time dependent Covariates 80 vii
Content 5.2.4 Comments 82 5.3 Models to Estimate the Withinsubjects Part of the Longitudinal Relationship 82 5.3.1 . Introduction 82 5.3.2 Model of Changes 83 532.1 Example 6. 8. viii 90 111 Dichotomous Outcome Variables 116 7.1 Two Measurements 116 7.2 More than Two Measurements 117 7.3 Comparing Groups 117 7.4 Example 117 7.4.1 Introduction 117 7.4.2 Development over Time 117 7.4.3 Comparing Groups 119 7.5 Longitudinal Regression Methods 119 7.5.1 Introduction 119 7.5.2 Generalised Estimating Equations 121 7.5.3 Mixed Model Analysis 124 7.5.4 Comparison between GEE Analysis and Mixed Model Analysis 127 7.5.5 The Adjustment for Covariance Method 130 7.5.6 Models to Disentangle the Betweenand Within-subjects Relationship 130 7.5.7 Comments 133 Categorical and Count Outcome Variables 134 8.1 Categorical Outcome Variables 82.1.1 Introduction 143 8.2.12 GEE Analysis 143 8.2.1. Causality in Observational Longitudinal Studies 92 6.1 Time-lag Models 92 6.1.1 Example 92 6.1.2 Comments 92 6.2 Longitudinal Mediation Models 94 6.2.1 Example 96 6.2.2 Comments 103 6.3 Other Methods that Claim to Estimate Causal Relationships 106 6.3.1 G-methods 107 6.3.2 Joint models 110 63.2.1 Example 7. 88 88 5.3.4 Comments 138 141 8.2 Count Outcome Variables 8.2.1 Example 143 85 5.3.3 Autoregressive Model Two Measurements 134 More than Two Measurements 135 Comparing Groups 135 Example 135 Regression-based Methods 136 8.1.5.1 Example 83 5.322 Another Example 533.1 Example 8.1.1 8.1.2 8.1.3 8.1.4 8.1.5 3 Mixed Model Analysis 9. Outcome Variables with Floor or Ceiling Effects 152 9.1 Introduction 152
9.2 Tobit Mixed Model Analysis 153 9.2.1 Example 153 9.3 Longitudinal Two-part Models 159 9.3.1 Example 160 9.3.2 Comments 162 10. Analysis of Longitudinal Intervention Studies 164 10.1 Introduction 164 10.2 Continuous Outcome Variables 164 10.2.1 Randomised Controlled Trials with One Follow-up Measurement 165 102.1.1 Example 168 10.2.2 Randomised Controlled Trials with More than One Follow up Measurement 172 10.2.2.1 Simple Analysis 175 10.2.2.2 Summary Statistics 177 10.22.3 Generalised Linear Model for Repeated Measures 178 10.22.4 Generalised Linear Model for Repeated Measures Adjusted for Baseline 10.22.5 Regression-based Methods 178 178 10.3 Dichotomous Outcome Variables 187 10.3.1 Introduction 187 10.3.2 Simple Analysis 188 10.3.3 Regression-based Methods 189 10.3.4 Other Methods 191 10.4 Stepped Wedge Designs 195 10.5 Comments 134 146 8.2.2 Comparison between GEE Analysis and Mixed Model Analysis 147 8.2.3 Negative Binomial Regression Analysis 148 8.2.4 Comments 150 196
Content 10.6 Beyond the Randomised Controlled Trial 197 10.6.1 Difference in Difference Method 198 10.6.1.1 Example 198 10.6.1.2 Comments 199 11. Missing Data in Longitudinal Studies 201 11.1 Introduction 201 11.2 Informative or Non-informative Missing Data 201 11.3 Example 202 11.3.1 Generating Datasets with Missing Data 202 11.3.2 Analysis of Determinants for Missing Data 203 11.4 Analysis Performed on Datasets with Missing Data 204 11.5 Imputation Methods 205 11.5.1 Continuous Variables 205 115.1.1 Cross-sectional Imputation Methods 115.12 Longitudinal Imputation Methods 115.13 Comments 205 206 206 115.1.4 Multiple Imputation 206 11.5.2 Dichotomous and Categorical Variables 11.5.3 Example 207 207 207 1153.1 Continuous Variables 11532 Multiple Imputation in Combination with Mixed Model Analysis? 11533 Additional Analyses 209 210 115.3.4 Dichotomous Variables 210 11.5.4 Comments 212 11.6 Alternative Methods 213 11.7 GEE Analysis or Mixed Model Analysis for the Analysis of Datasets with Missing Data? 214 11.8 Conclusions 214 12. Sample Size Calculations 12.1 Introduction 216 12.2 Example 218 12.3 Comment 219 216 13. Software for Longitudinal Data Analysis 220 13.1 Introduction 220 13.2 GEE Analysis with a Continuous Outcome Variable 220 13.2.1 STATA 220 13.2.2 SAS 220 13.2.3 R 221 13.2.4 SPSS 222 13.2.5 Overview 223 13.3 GEE Analysis with a Dichotomous Outcome Variable 224 13.3.1 STATA 224 13.3.2 SAS 224 13.3.3 R 224 13.3.4 SPSS 224 13.3.5 Overview 224 13.4 Mixed Model Analysis with a Continuous Outcome Variable 226 13.4.1 Introduction 226 13.4.2 STATA 226 13.4.3 SAS 227
13.4.4 R 229 13.4.5 SPSS 231 13.4.6 Overview 235 13.5 Mixed Model Analysis with a Dichotomous Outcome Variable 235 13.5.1 Introduction 235 13.5.2 STATA 235 13.5.3 SAS 236 13.5.4 R 239 13.5.5 SPSS 240 13.5.6 Overview 242 References 243 Index 251 ІХ
Essential for non-statisticians and researchers working with longitudinal data from medical studies, this updated new edition discusses the most important techniques available for analysing data of this type. Using non-technical language, the book explores simple, methods such as the paired t-test and summary statistics as well as hnore sophisticated regression-based methods, including mixed model analysis. The emphasis of the discussion lies in the interpretation of the results of these different methods, covering data analysis with continuous, dichotomous, categorical and other outcome variables. Datasets used throughout the book are provided, enabling readers to re֊analyse the examples as they make their way through chapters and improve their understanding of the material. Finally, an extensive and practical overview of, and comparison between, different software packages is provided. Readers will be able to use this book as a practical manual in their everyday work without needing a strong background in statistics. JOS W. R. TWISK is a Professor in the Department of Epidemiology and Data Science at Amsterdam UMC, Amsterdam, The Netherlands. He specialises in the methodological field of longitudinal data analysis and multilevel/mixed model analysis, and is head of the expertise center for Applied Longitudinal Data Analysis at the Amsterdam UMC. Review of previous edition: Overall, the book is well written, and the material is rich and carefully organised . the book is a welcome reference manual or practical guide foi practitioners of statistical methods in (but not
limited to) epidemiological and clinical studies. The book's main value is in its rather comprehensive presentation of a collection of longitudinal data analyses arising from different research questions. In this sense, the book is unique. Some practitioners of statistics may have struggled to learn longitudinal data analysis by reading manuals of software packages. This book is potentially of great benefit to them.' Journal of the American Statistical Association |
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| spelling | Twisk, Jos W. R. 1962- Verfasser (DE-588)173605362 aut Applied longitudinal data analysis for medical science a practical guide Jos W.R. Twisk Third edition Cambridge Cambridge University Press 2023 xii, 258 Seiten Illustrationen txt rdacontent n rdamedia nc rdacarrier Längsschnittuntersuchung (DE-588)4034036-3 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 gnd rswk-swf Medizinische Statistik (DE-588)4127563-9 s Längsschnittuntersuchung (DE-588)4034036-3 s DE-604 Erscheint auch als Online-Ausgabe, EPUB 9781009288002 Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034198199&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Augsburg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034198199&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
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| title | Applied longitudinal data analysis for medical science a practical guide |
| title_auth | Applied longitudinal data analysis for medical science a practical guide |
| title_exact_search | Applied longitudinal data analysis for medical science a practical guide |
| title_exact_search_txtP | Applied longitudinal data analysis for medical science a practical guide |
| title_full | Applied longitudinal data analysis for medical science a practical guide Jos W.R. Twisk |
| title_fullStr | Applied longitudinal data analysis for medical science a practical guide Jos W.R. Twisk |
| title_full_unstemmed | Applied longitudinal data analysis for medical science a practical guide Jos W.R. Twisk |
| title_short | Applied longitudinal data analysis for medical science |
| title_sort | applied longitudinal data analysis for medical science a practical guide |
| title_sub | a practical guide |
| topic | Längsschnittuntersuchung (DE-588)4034036-3 gnd Medizinische Statistik (DE-588)4127563-9 gnd |
| topic_facet | Längsschnittuntersuchung Medizinische Statistik |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034198199&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034198199&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT twiskjoswr appliedlongitudinaldataanalysisformedicalscienceapracticalguide |