Modern Methods for Epidemiology [electronic resource] /
Tu, Yu-Kang.editor.
Greenwood, Darren C.editor.
Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensable tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology.
Description based upon print version of record.
Modern Methods for Epidemiology; Preface; Contents; Contributors; Chapter 1: Confounding and Causal Path Diagrams; 1.1 Causal Models; 1.1.1 Directed Acyclic Diagrams (DAGs), Nomenclature and Notation; 1.1.1.1 Nomenclature and the Construction of DAGs; Nodes, Arcs and Directed Arcs; Parents, Children, Ancestors and Descendants; Directed Paths, Backdoor Paths, Colliders and Blocked Paths; Identification of Arcs; 1.1.1.2 Notation; 1.1.2 The Speculative Nature of DAGs and Their Limitations; 1.1.3 Notation; 1.2 Confounding and Confounders; 1.2.1 Confounding and DAGs; 1.2.2 Identifying Confounding
1.2.3 Sufficient Set of Confounders1.2.4 Strengths and Weaknesses of Causal Path Diagrams; 1.3 Conclusions; References; Chapter 2: Statistical Modelling of Partially Observed Data Using Multiple Imputation: Principles and Practice; 2.1 Introduction; 2.2 Issues Raised by Missing Data; 2.2.1 Ambiguity Caused by Missing Data; 2.2.2 Duality of Missingness Mechanism and Distribution of Missing Data Given Observed Data; 2.3 When Will Complete Cases Do?; 2.3.1 Response Y Partially Observed, Other Variables Complete; 2.3.2 Covariate X Partially Observed
2.4 Methods for Analysis of Partially Observed Data2.4.1 Inverse Probability Weighting; 2.4.2 EM-Algorithm and Related Methods; 2.4.3 Repeated Measures Modelling; 2.4.4 Multiple Imputation; 2.5 Practical Application of MI: Issues to Consider; 2.5.1 Formulating the Imputation Model; 2.5.2 Non-linear Relationships and Interactions; 2.5.3 Multilevel Data; 2.5.4 Auxiliary Variables; 2.5.5 Multiple Imputation Is Not Prediction; 2.6 Example: Multilevel Multiple Imputation; 2.7 Conclusions; References; Chapter 3: Measurement Errors in Epidemiology; 3.1 Background to the Problem of Measurement Error
3.1.1 General Context3.1.2 Biased Estimates; 3.1.3 Loss of Power; 3.2 The Structure of Measurement Error; 3.2.1 Characterising Measurement Error; 3.2.2 Underlying Mechanisms for Measurement Error; 3.2.2.1 Classical Measurement Error; 3.2.2.2 Berkson Measurement Error; 3.2.2.3 Random and Systematic Error; 3.2.2.4 Differential and Non-differential Error; 3.2.2.5 Additive and Multiplicative Error Structures; 3.2.2.6 Functional and Structural Modelling; 3.2.3 A Simple Example; 3.2.3.1 Biased Slope; 3.2.3.2 Biased Intercept; 3.2.3.3 Inflated Residual Variance; 3.2.3.4 Reduced Power
3.2.3.5 Summary of Simple Example3.3 Reducing the Effects of Measurement Error; 3.3.1 Design; 3.3.2 Correction Using Aggregate-Level Surrogates; 3.3.3 Correction Using Individual-Level Surrogate Measures; 3.3.4 Using External Sources of Information; 3.3.5 Statistical Methods; 3.3.5.1 Regression Calibration; 3.3.5.2 Simulation Extrapolation (SIMEX); 3.3.5.3 Multiple Imputation; 3.3.5.4 Latent Variable and Multilevel Methods; 3.3.5.5 Bayesian Approaches; 3.3.5.6 Quadrature-Based Methods; 3.4 Practical Example; 3.5 Future Developments; References
Chapter 4: Selection Bias in Epidemiologic Studies
Includes bibliographical references and index.
Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensable tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology.
Medicine.
Epidemiology.
Biostatistics.
Cancer research.
Biomedicine, general.
Epidemiology.
Biostatistics.
Cancer Research.