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Statistical Thinking in Epidemiology

Statistical Thinking in Epidemiology

By: Yu-Kang Tu (author), Mark S. Gilthorpe (author)Hardback

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Description

While biomedical researchers may be able to follow instructions in the manuals accompanying the statistical software packages, they do not always have sufficient knowledge to choose the appropriate statistical methods and correctly interpret their results. Statistical Thinking in Epidemiology examines common methodological and statistical problems in the use of correlation and regression in medical and epidemiological research: mathematical coupling, regression to the mean, collinearity, the reversal paradox, and statistical interaction. Statistical Thinking in Epidemiology is about thinking statistically when looking at problems in epidemiology. The authors focus on several methods and look at them in detail: specific examples in epidemiology illustrate how different model specifications can imply different causal relationships amongst variables, and model interpretation is undertaken with appropriate consideration of the context of implicit or explicit causal relationships. This book is intended for applied statisticians and epidemiologists, but can also be very useful for clinical and applied health researchers who want to have a better understanding of statistical thinking. Throughout the book, statistical software packages R and Stata are used for general statistical modeling, and Amos and Mplus are used for structural equation modeling.

About Author

Dr Yu-Kang Tu is a Senior Clinical Research Fellow in the Division of Biostatistics, School of Medicine, and in the Leeds Dental Institute, University of Leeds, Leeds, UK. He was a visiting Associate Professor to the National Taiwan University, Taipei, Taiwan. First trained as a dentist and then an epidemiologist, he has published extensively in dental, medical, epidemiological and statistical journals. He is interested in developing statistical methodologies to solve statistical and methodological problems such as mathematical coupling, regression to the mean, collinearity and the reversal paradox. His current research focuses on applying latent variables methods, e.g. structural equation modeling, latent growth curve modelling, and lifecourse epidemiology. More recently, he has been working on applying partial least squares regression to epidemiological data. Prof Mark S Gilthorpe is professor of Statistical Epidemiology, Division of Biostatistics, School of Medicine, University of Leeds, Leeds, UK. Having completed a single honours degree in mathematical Physics (University of Nottingham), he undertook a PhD in Mathematical Modelling (University of Aston in Birmingham), before initially embarking upon a career as self-employed Systems and Data Analyst and Computer Programmer, and eventually becoming an academic in biomedicine. Academic posts include systems and data analyst of UK regional routine hospital data in the Department of Public Health and Epidemiology, University of Birmingham; Head of Biostatistics at the Eastman Dental Institute, University College London; and founder and Head of the Division of Biostatistics, School of Medicine, University of Leeds. His research focus has persistently been that of the development and promotion of robust and sophisticated modelling methodologies for non-experimental (and sometimes large and complex) observational data within biomedicine, leading to extensive publications in dental, medical, epidemiological and statistical journals.

Contents

IntroductionUses of Statistics in Medicine and EpidemiologyStructure and Objectives of This BookNomenclature in This BookGlossaryVector Geometry of Linear Models for EpidemiologistsIntroductionBasic Concepts of Vector Geometry in StatisticsCorrelation and Simple Regression in Vector GeometryLinear Multiple Regression in Vector GeometrySignificance Testing of Correlation and Simple Regression in Vector GeometrySignificance Testing of Multiple Regression in Vector GeometrySummaryPath Diagrams and Directed Acyclic GraphsIntroductionPath DiagramsDirected Acyclic GraphsDirect and Indirect EffectsSummaryMathematical Coupling and Regression to the Mean in the Relation between Change and Initial ValueIntroductionHistorical BackgroundWhy Should Change Not Be Regressed on Initial Value? A Review of the ProblemProposed Solutions in the LiteratureComparison between Oldham's Method and Blomqvist's FormulaOldham's Method and Blomqvist's Formula Answer Two Different QuestionsWhat Is Galton's Regression to the Mean?Testing the Correct Null HypothesisEvaluation of the Categorisation ApproachTesting the Relation between Changes and Initial Values When There Are More than Two OccasionsDiscussionAnalysis of Change in Pre-/Post-Test StudiesIntroductionAnalysis of Change in Randomised Controlled TrialsComparison of Six MethodsAnalysis of Change in Non-Experimental Studies: Lord's ParadoxANCOVA and t-Test for Change Scores Have Different AssumptionsConclusionCollinearity and MulticollinearityIntroduction: Problems of Collinearity in Linear RegressionCollinearityMulticollinearityMathematical Coupling and CollinearityVector Geometry of CollinearityGeometrical Illustration of Principal Components Analysis as a Solution to MulticollinearityExample: Mineral Loss in Patients Receiving Parenteral NutritionSolutions to CollinearityConclusionIs `Reversal Paradox' a Paradox?A Plethora of Paradoxes: The Reversal ParadoxBackground: The Foetal Origins of Adult DiseaseHypothesis (Barker's Hypothesis)Vector Geometry of the Foetal Origins HypothesisReversal Paradox and Adjustment for Current Body Size: Empirical Evidence from Meta-AnalysisDiscussionConclusionTesting Statistical InteractionIntroduction: Testing Interactions in Epidemiological ResearchTesting Statistical Interaction between Categorical VariablesTesting Statistical Interaction between Continuous VariablesPartial Regression Coefficient for Product Term in Regression ModelsCategorization of Continuous Explanatory VariablesThe Four-Model Principle in the Foetal Origins HypothesisCategorization of Continuous Covariates and Testing InteractionDiscussionConclusionFinding Growth Trajectories in Lifecourse ResearchIntroductionCurrent Approaches to Identifying Postnatal Growth Trajectories in Lifecourse ResearchDiscussionPartial Least Squares Regression for Lifecourse ResearchIntroductionDataOLS RegressionPLS RegressionDiscussionConclusionConcluding RemarksReferencesIndex

Product Details

  • ISBN13: 9781420099911
  • Format: Hardback
  • Number Of Pages: 231
  • ID: 9781420099911
  • weight: 544
  • ISBN10: 1420099914

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