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Quantitative Methods for Health Research

E-BookPDF2 - DRM Adobe / Adobe Ebook ReaderE-Book
568 Seiten
Englisch
John Wiley & Sonserschienen am29.11.20172. Auflage
A practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community

This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis-the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods.

A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include:
Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods
Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application
Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice
Integration of practical data analysis exercises to develop skills and confidence
Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text

Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference. 



Nigel Bruce, PhD is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK.
Daniel Pope, PhD is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK.
Debbi Stanistreet, PhD is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.mehr
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E-BookPDF2 - DRM Adobe / Adobe Ebook ReaderE-Book
EUR55,99
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Produkt

KlappentextA practical introduction to epidemiology, biostatistics, and research methodology for the whole health care community

This comprehensive text, which has been extensively revised with new material and additional topics, utilizes a practical slant to introduce health professionals and students to epidemiology, biostatistics, and research methodology. It draws examples from a wide range of topics, covering all of the main contemporary health research methods, including survival analysis, Cox regression, and systematic reviews and meta-analysis-the explanation of which go beyond introductory concepts. This second edition of Quantitative Methods for Health Research: A Practical Interactive Guide to Epidemiology and Statistics also helps develop critical skills that will prepare students to move on to more advanced and specialized methods.

A clear distinction is made between knowledge and concepts that all students should ensure they understand, and those that can be pursued further by those who wish to do so. Self-assessment exercises throughout the text help students explore and reflect on their understanding. A program of practical exercises in SPSS (using a prepared data set) helps to consolidate the theory and develop skills and confidence in data handling, analysis, and interpretation. Highlights of the book include:
Combining epidemiology and bio-statistics to demonstrate the relevance and strength of statistical methods
Emphasis on the interpretation of statistics using examples from a variety of public health and health care situations to stress relevance and application
Use of concepts related to examples of published research to show the application of methods and balance between ideals and the realities of research in practice
Integration of practical data analysis exercises to develop skills and confidence
Supplementation by a student companion website which provides guidance on data handling in SPSS and study data sets as referred to in the text

Quantitative Methods for Health Research, Second Edition is a practical learning resource for students, practitioners and researchers in public health, health care and related disciplines, providing both a course book and a useful introductory reference. 



Nigel Bruce, PhD is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK.
Daniel Pope, PhD is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK.
Debbi Stanistreet, PhD is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.
Details
Weitere ISBN/GTIN9781118665268
ProduktartE-Book
EinbandartE-Book
FormatPDF
FormatFormat mit automatischem Seitenumbruch (reflowable)
Erscheinungsjahr2017
Erscheinungsdatum29.11.2017
Auflage2. Auflage
Seiten568 Seiten
SpracheEnglisch
Dateigrösse11619 Kbytes
Artikel-Nr.3361802
Rubriken
Genre9201

Inhalt/Kritik

Inhaltsverzeichnis
1;Quantitative Methods for Health Research;3
2;Contents;7
3;Preface;17
3.1;Introduction;17
3.2;Learning Objectives;18
3.3;Resource Papers and Information Sources;18
3.4;Key Terms;18
3.5;Sample Size Calculations;18
3.6;SPSS Dataset Used for Illustrating Examples of Statistical Analysis;18
3.7;Self-Assessment Exercises;19
3.8;Mathematical Aspects of Statistics;19
3.9;Organisation of Subject Matter by Chapter;19
3.10;Acknowledgements;22
4;About the Companion Website;23
5;1 Philosophy of Science and Introduction to Epidemiology;25
5.1;Introduction and Learning Objectives;25
5.2;1.1 Approaches to Scientific Research;26
5.2.1;1.1.1 History and Nature of Scientific Research;26
5.2.2;1.1.2 What is Epidemiology?;30
5.2.3;1.1.3 What are Statistics?;31
5.2.4;1.1.4 Approach to Learning;32
5.3;1.2 Formulating a Research Question;32
5.3.1;1.2.1 Importance of a Well-Defined Research Question;32
5.3.2;1.2.2 Development of Research Ideas;34
5.4;1.3 Rates: Incidence and Prevalence;35
5.4.1;1.3.1 Why Do We Need Rates?;35
5.4.2;1.3.2 Measures of Disease Frequency;36
5.4.3;1.3.3 Prevalence Rate;36
5.4.4;1.3.4 Incidence Rate;36
5.4.5;1.3.5 Relationship Between Incidence, Duration, and Prevalence;39
5.5;1.4 Concepts of Prevention;40
5.5.1;1.4.1 Introduction;40
5.5.2;1.4.2 Primary, Secondary, and Tertiary Prevention;41
5.6;1.5 Answers to Self-Assessment Exercises;42
6;2 Routine Data Sources and Descriptive Epidemiology;49
6.1;Introduction and Learning Objectives;49
6.2;2.1 Routine Collection of Health Information;50
6.2.1;2.1.1 Deaths (Mortality);50
6.2.2;2.1.2 Compiling Mortality Statistics: The Example of England and Wales;52
6.2.3;2.1.3 Suicide Among Men;53
6.2.4;2.1.4 Suicide Among Young Women;55
6.2.5;2.1.5 Variations in Deaths of Very Young Children;55
6.3;2.2 Descriptive Epidemiology;57
6.3.1;2.2.1 What is Descriptive Epidemiology?;57
6.3.2;2.2.2 International Variations in Rates of Lung Cancer;57
6.3.3;2.2.3 Illness (Morbidity);58
6.3.4;2.2.4 Sources of Information on Morbidity;59
6.3.5;2.2.5 Notification of Infectious Disease;59
6.3.6;2.2.6 Illness Seen in General Practice;62
6.4;2.3 Information on the Environment;63
6.4.1;2.3.1 Air Pollution and Health;63
6.4.2;2.3.2 Routinely Available Data on Air Pollution;63
6.5;2.4 Displaying, Describing, and Presenting Data;65
6.5.1;2.4.1 Displaying the Data;65
6.5.2;2.4.2 Calculating the Frequency Distribution;66
6.5.3;2.4.3 Describing the Frequency Distribution;68
6.5.4;2.4.4 The Relative Frequency Distribution;81
6.5.5;2.4.5 Scatterplots, Linear Relationships and Correlation;84
6.6;2.5 Routinely Available Health Data;93
6.6.1;2.5.1 Introduction;93
6.6.2;2.5.2 Classification of Routine Health Information Sources;93
6.6.3;2.5.3 Demographic Data;94
6.6.4;2.5.4 Health Event Data;97
6.6.5;2.5.5 Population-Based Health Information;102
6.6.6;2.5.6 Deprivation Indices;103
6.6.7;2.5.7 Routine Data Sources for Countries Other Than the UK;104
6.7;2.6 Descriptive Epidemiology in Action;104
6.7.1;2.6.1 The London Smogs of the 1950s;104
6.7.2;2.6.2 Ecological Studies;106
6.8;2.7 Overview of Epidemiological Study Designs;108
6.9;2.8 Answers to Self-Assessment Exercises;110
7;3 Standardisation;125
7.1;Introduction and Learning Objectives;125
7.2;3.1 Health Inequalities in Merseyside;125
7.2.1;3.1.1 Socio-Economic Conditions and Health;125
7.2.2;3.1.2 Comparison of Crude Death Rates;126
7.2.3;3.1.3 Usefulness of a Summary Measure;128
7.3;3.2 Indirect Standardisation: Calculation of the Standardised Mortality Ratio (SMR);129
7.3.1;3.2.1 Mortality in Liverpool;129
7.3.2;3.2.2 Interpretation of the SMR;131
7.3.3;3.2.3 Dealing With Random Variation: The 95 per cent Confidence Interval;131
7.3.4;3.2.4 Increasing Precision of the SMR Estimate;132
7.3.5;3.2.5 Mortality in Sefton;132
7.3.6;3.2.6 Comparison of SMRs;134
7.3.7;3.2.7 Indirectly Standardised Mortality Rates;134
7.4;3.3 Direct Standardisation;134
7.4.1;3.3.1 Introduction;134
7.4.2;3.3.2 An Example: Changes in Deaths From Stroke Over Time;135
7.4.3;3.3.3 Using the European Standard Population;136
7.4.4;3.3.4 Direct or Indirect: Which Method is Best?;137
7.5;3.4 Standardisation for Factors Other Than Age;138
7.6;3.5 Answers to Self-Assessment Exercises;139
8;4 Surveys;147
8.1;Introduction and Learning Objectives;147
8.2;Resource Papers;148
8.3;4.1 Purpose and Context;148
8.3.1;4.1.1 Defining the Research Question;148
8.3.2;4.1.2 Political Context of Research;150
8.4;4.2 Sampling Methods;151
8.4.1;4.2.1 Introduction;151
8.4.2;4.2.2 Sampling;151
8.4.3;4.2.3 Probability;153
8.4.4;4.2.4 Simple Random Sampling;154
8.4.5;4.2.5 Stratified Sampling;155
8.4.6;4.2.6 Cluster Random Sampling;156
8.4.7;4.2.7 Multistage Random Sampling;157
8.4.8;4.2.8 Systematic Sampling;157
8.4.9;4.2.9 Convenience Sampling;157
8.4.10;4.2.10 Sampling People Who are Difficult to Contact;157
8.4.11;4.2.11 Quota Sampling;158
8.4.12;4.2.12 Sampling in Natsal-3;159
8.5;4.3 The Sampling Frame;161
8.5.1;4.3.1 Why Do We Need a Sampling Frame?;161
8.5.2;4.3.2 Losses in Sampling;161
8.6;4.4 Sampling Error, Confidence Intervals, and Sample Size;163
8.6.1;4.4.1 Sampling Distributions and the Standard Error;163
8.6.2;4.4.2 The Standard Error;164
8.6.3;4.4.3 Key Properties of the Normal Distribution;169
8.6.4;4.4.4 Confidence Interval (CI) for the Sample Mean;170
8.6.5;4.4.5 Estimating Sample Size;173
8.6.6;4.4.6 Sample Size for Estimating a Population Mean;173
8.6.7;4.4.7 Standard Error and 95 per cent CI for a Population Proportion;174
8.6.8;4.4.8 Sample Size to Estimate a Population Proportion;175
8.7;4.5 Response;177
8.7.1;4.5.1 Determining the Response Rate;177
8.7.2;4.5.2 Assessing Whether the Sample is Representative;178
8.7.3;4.5.3 Maximising the Response Rate;178
8.8;4.6 Measurement;181
8.8.1;4.6.1 Introduction: The Importance of Good Measurement;181
8.8.2;4.6.2 Interview or Self-Completed Questionnaire?;181
8.8.3;4.6.3 Principles of Good Questionnaire Design;182
8.8.4;4.6.4 Development of a Questionnaire;185
8.8.5;4.6.5 Checking How Well the Interviews and Questionnaires Have Worked;185
8.8.6;4.6.6 Assessing Measurement Quality;189
8.8.7;4.6.7 Overview of Sources of Error;193
8.9;4.7 Data Types and Presentation;195
8.9.1;4.7.1 Introduction;195
8.9.2;4.7.2 Types of Data;196
8.9.3;4.7.3 Displaying and Summarising the Data;197
8.10;4.8 Answers to Self-Assessment Exercises;200
9;5 Cohort Studies;209
9.1;Introduction and Learning Objectives;209
9.2;Resource Papers;210
9.3;5.1 Why Do a Cohort Study?;210
9.3.1;5.1.1 Objectives of the Study;210
9.3.2;5.1.2 Study Structure;212
9.4;5.2 Obtaining the Sample;212
9.4.1;5.2.1 Introduction;212
9.4.2;5.2.2 Sample Size;214
9.5;5.3 Measurement;214
9.5.1;5.3.1 Importance of Good Measurement;214
9.5.2;5.3.2 Identifying and Avoiding Measurement Error;214
9.5.3;5.3.3 The Measurement of Blood Pressure;215
9.5.4;5.3.4 Case Definition;216
9.6;5.4 Follow-Up;217
9.6.1;5.4.1 Nature of the Task;217
9.6.2;5.4.2 Deaths (Mortality);217
9.6.3;5.4.3 Non-Fatal Cases (Morbidity);218
9.6.4;5.4.4 Challenges Faced with Follow-Up of a Cohort in a Different Setting;218
9.6.5;5.4.5 Assessment of Changes During Follow-Up Period;220
9.7;5.5 Basic Presentation and Analysis of Results;222
9.7.1;5.5.1 Initial Presentation of Findings;222
9.7.2;5.5.2 Relative Risk;223
9.7.3;5.5.3 Hypothesis Test for Categorical Data: The Chi-Squared Test;225
9.7.4;5.5.4 Hypothesis Tests for Continuous Data: The z-Test and the t-Test;233
9.8;5.6 How Large Should a Cohort Study Be?;238
9.8.1;5.6.1 Perils of Inadequate Sample Size;238
9.8.2;5.6.2 Sample Size for a Cohort Study;239
9.8.3;5.6.3 Example of Output from Sample Size Calculation;240
9.9;5.7 Assessing Whether an Association is Causal;242
9.9.1;5.7.1 The Hill Viewpoints;242
9.9.2;5.7.2 Confounding: What Is It and How Can It Be Addressed?;244
9.9.3;5.7.3 Does Smoking Cause Heart Disease?;246
9.9.4;5.7.4 Confounding in the Physical Activity and Cancer Study;246
9.9.5;5.7.5 Methods for Dealing with Confounding;248
9.10;5.8 Simple Linear Regression;248
9.10.1;5.8.1 Approaches to Describing Associations;248
9.10.2;5.8.2 Finding the Best Fit for a Straight Line;250
9.10.3;5.8.3 Interpreting the Regression Line;251
9.10.4;5.8.4 Using the Regression Line;252
9.10.5;5.8.5 Hypothesis Test of the Association Between the Explanatory and Outcome Variables;252
9.10.6;5.8.6 How Good is the Regression Model?;253
9.10.7;5.8.7 Interpreting SPSS Output for Simple Linear Regression Analysis;255
9.10.8;5.8.8 First Table: Variables Entered/Removed;256
9.11;5.9 Introduction to Multiple Linear Regression;259
9.11.1;5.9.1 Principles of Multiple Regression;259
9.11.2;5.9.2 Using Multivariable Linear Regression to Study Independent Associations;259
9.11.3;5.9.3 Investigation of the Effect of Work Stress on Bodyweight;259
9.11.4;5.9.4 Multiple Regression in the Cancer Study;263
9.11.5;5.9.5 Overview of Regression Methods for Different Types of Outcome;264
9.12;5.10 Answers to Self-Assessment Exercises;266
10;6 Case-Control Studies;275
10.1;Introduction and Learning Objectives;275
10.2;Resource Papers;276
10.3;6.1 Why do a Case-Control Study?;277
10.3.1;6.1.1 Study Objectives;277
10.3.2;6.1.2 Study Structure;278
10.3.3;6.1.3 Approach to Analysis;279
10.3.4;6.1.4 Retrospective Data Collection;281
10.3.5;6.1.5 Applications of the Case-Control Design;282
10.4;6.2 Key Elements of Study Design;283
10.4.1;6.2.1 Selecting the Cases;283
10.4.2;6.2.2 The Controls;284
10.4.3;6.2.3 Exposure Assessment;286
10.4.4;6.2.4 Bias in Exposure Assessment;287
10.5;6.3 Basic Unmatched and Matched Analysis;289
10.5.1;6.3.1 The Odds Ratio (OR);289
10.5.2;6.3.2 Calculation of the OR-Simple Matched Analysis;293
10.5.3;6.3.3 Hypothesis Tests for Case-Control Studies;295
10.6;6.4 Sample Size for a Case-Control Study;297
10.6.1;6.4.1 Introduction;297
10.6.2;6.4.2 What Information is Required?;297
10.6.3;6.4.3 An Example of Sample Size Calculation Using OpenEpi;298
10.7;6.5 Confounding and Logistic Regression;300
10.7.1;6.5.1 Introduction;300
10.7.2;6.5.2 Stratification;301
10.7.3;6.5.3 Logistic Regression;302
10.7.4;6.5.4 Example: Multivariable Logistic Regression;305
10.7.5;6.5.5 Matched Studies - Conditional Logistic Regression;311
10.7.6;6.5.6 Interpretation of Adjusted Results from the New Zealand Study;311
10.8;6.6 Answers to Self-Assessment Exercises;313
11;7 Intervention Studies;321
11.1;Introduction and Learning Objectives;321
11.1.1;Typology of Intervention Study Designs Described in This Chapter;321
11.1.2;Terminology;322
11.2;Resource Papers;323
11.2.1;Principal References;323
11.2.2;Supplementary References;323
11.3;7.1 Why Do an Intervention Study?;323
11.3.1;7.1.1 Study Objectives;323
11.3.2;7.1.2 Structure of a Randomised, Controlled Intervention Study;324
11.4;7.2 Key Elements of Intervention Study Design;327
11.4.1;7.2.1 Defining Who Should be Included and Excluded;327
11.4.2;7.2.2 Intervention and Control;328
11.4.3;7.2.3 Randomisation;330
11.4.4;7.2.4 Outcome Assessment;331
11.4.5;7.2.5 Blinding;332
11.4.6;7.2.6 Ethical Issues for Intervention Studies;332
11.5;7.3 The Analysis of Intervention Studies;333
11.5.1;7.3.1 Review of Variables at Baseline;334
11.5.2;7.3.2 Loss to Follow-Up;335
11.5.3;7.3.3 Compliance with the Treatment Allocation;335
11.5.4;7.3.4 Analysis by Intention-to-Treat;336
11.5.5;7.3.5 Analysis per Protocol;337
11.5.6;7.3.6 What is the Effect of the Intervention?;337
11.5.7;7.3.7 Drawing Conclusions;339
11.5.8;7.3.8 Adjustment for Variables Known to Influence the Outcome;339
11.5.9;7.3.9 Paired Comparisons;339
11.5.10;7.3.10 The Crossover Trial;341
11.6;7.4 Testing More-Complex Interventions;342
11.6.1;7.4.1 Introduction;342
11.6.2;7.4.2 Randomised Trial of Individuals for a Complex Intervention;343
11.6.3;7.4.3 Factorial Design;346
11.6.4;7.4.4 Analysis and Interpretation;347
11.6.5;7.4.5 Departure from the Ideal Blinded RCT Design;351
11.6.6;7.4.6 The Cluster Randomised Trial;352
11.6.7;7.4.7 The Community (Cluster) Randomised Trial;354
11.6.8;7.4.8 Non-Randomised Intervention Designs;356
11.6.9;7.4.9 The Natural Experiment;357
11.7;7.5 Analysis of Intervention Studies Using a Cluster Design;358
11.7.1;7.5.1 Why Does the Use of Clusters Make a Difference?;358
11.7.2;7.5.2 Summarising Clustering Effects: The Intra-Class Correlation Coefficient;358
11.7.3;7.5.3 Multi-Level Modelling;359
11.7.4;7.5.4 Analysis of the Cluster RCT of Physical Activity;359
11.8;7.6 How Big Should the Intervention Study Be?;361
11.8.1;7.6.1 Introduction;361
11.8.2;7.6.2 Sample Size for a Trial with Categorical Data Outcomes;361
11.8.3;7.6.3 One-Sided and Two-Sided Tests;363
11.8.4;7.6.4 Sample Size for a Trial with Continuous Data Outcomes;363
11.8.5;7.6.5 Sample Size for an Intervention Study Using Cluster Design;364
11.8.6;7.6.6 Estimation of Sample Size is not a Precise Science;365
11.9;7.7 Intervention Study Registration, Management, and Reporting;365
11.9.1;7.7.1 Introduction;365
11.9.2;7.7.2 Registration;366
11.9.3;7.7.3 Trial Management;366
11.9.4;7.7.4 Reporting Standards (CONSORT);367
11.10;7.8 Answers to Self-Assessment Exercises;368
12;8 Life Tables, Survival Analysis, and Cox Regression;379
12.1;Introduction and Learning Objectives;379
12.2;Resource Papers;380
12.3;8.1 Survival Analysis;380
12.3.1;8.1.1 Introduction;380
12.3.2;8.1.2 Why Do We Need Survival Analysis?;380
12.3.3;8.1.3 Censoring;381
12.3.4;8.1.4 Kaplan-Meier Survival Curves;383
12.3.5;8.1.5 Kaplan-Meier Survival Curves;385
12.3.6;8.1.6 The Log-Rank Test;386
12.3.7;8.1.7 Interpretation of the Kaplan-Meier Survival Curve;389
12.4;8.2 Cox Regression;395
12.4.1;8.2.1 Introduction;395
12.4.2;8.2.2 The Hazard Function;395
12.4.3;8.2.3 Assumption of Proportional Hazards;396
12.4.4;8.2.4 The Cox Regression Model;396
12.4.5;8.2.5 Checking the Assumption of Proportional Hazards;396
12.4.6;8.2.6 Interpreting the Cox Regression Model;397
12.4.7;8.2.7 Prediction;398
12.4.8;8.2.8 Application of Cox Regression;399
12.5;8.3 Current Life Tables;401
12.5.1;8.3.1 Introduction;401
12.5.2;8.3.2 Current Life Tables and Life Expectancy at Birth;401
12.5.3;8.3.3 Life Expectancy at Other Ages;403
12.5.4;8.3.4 Healthy or Disability-Free Life Expectancy;403
12.5.5;8.3.5 Abridged Life Tables;404
12.5.6;8.3.6 Summary;405
12.6;8.4 Answers to Self-Assessment Exercises;405
13;9 Systematic Reviews and Meta-Analysis;409
13.1;Introduction and Learning Objectives;409
13.1.1;Increasing Power by Combining Studies;410
13.2;Resource Papers;411
13.3;9.1 The Why and How of Systematic Reviews;411
13.3.1;9.1.1 Why is it Important that Reviews be Systematic?;411
13.3.2;9.1.2 Method of Systematic Review - Overview and Developing a Protocol;412
13.3.3;9.1.3 Deciding on the Research Question and Objectives for the Review;413
13.3.4;9.1.4 Defining Criteria for Inclusion and Exclusion of Studies;414
13.3.5;9.1.5 Identifying Relevant Studies;415
13.3.6;9.1.6 Assessment of Methodological Quality;420
13.3.7;9.1.7 Extracting Data;423
13.3.8;9.1.8 Describing the Results;423
13.4;9.2 The Methodology of Meta-Analysis;426
13.4.1;9.2.1 Method of Meta-Analysis - Overview;426
13.4.2;9.2.2 Assessment of Publication Bias - the Funnel Plot;427
13.4.3;9.2.3 Heterogeneity;429
13.4.4;9.2.4 Calculating the Pooled Estimate;431
13.4.5;9.2.5 Presentation of Results: Forest Plot;432
13.4.6;9.2.6 Sensitivity Analysis;433
13.4.7;9.2.7 Statistical Software for the Conduct of Meta-Analysis;434
13.4.8;9.2.8 Another Example of the Value of Meta-Analysis - Identifying a Dangerous Treatment;435
13.5;9.3 Systematic Reviews and Meta-Analyses of Observational Studies;438
13.5.1;9.3.1 Introduction;438
13.5.2;9.3.2 Why Conduct a Systematic Review of Observational Studies?;438
13.5.3;9.3.3 Approach to Meta-Analysis of Observational Studies;439
13.5.4;9.3.4 Method of Systematic Review of Observational Studies;440
13.5.5;9.3.5 Method of Meta-Analysis of Observational Studies;440
13.6;9.4 Reporting and Publishing Systematic Reviews and Meta-Analyses;442
13.7;9.5 The Cochrane Collaboration;443
13.7.1;9.5.1 Introduction;443
13.7.2;9.5.2 Cochrane Collaboration Logo;446
13.7.3;9.5.3 Collaborative Review Groups;446
13.7.4;9.5.4 Cochrane Library;446
13.8;9.6 Answers to Self-Assessment Exercises;447
14;10 Prevention Strategies and Evaluation of Screening;453
14.1;Introduction and Learning Objectives;453
14.2;Resource Papers;454
14.3;10.1 Concepts of Risk;454
14.3.1;10.1.1 Relative and Attributable Risk;454
14.3.2;10.1.2 Calculation of AR;455
14.3.3;10.1.3 Attributable Fraction (AF) for a Dichotomous Exposure;456
14.3.4;10.1.4 Attributable Fraction for Continuous and Multiple Category Exposures;458
14.3.5;10.1.5 Years of Life Lost (YLL) and Years Lived with Disability (YLD);458
14.3.6;10.1.6 Disability-Adjusted Life Years (DALYs);460
14.3.7;10.1.7 Burden Attributable to Specific Risk Factors;462
14.4;10.2 Strategies of Prevention;464
14.4.1;10.2.1 The Distribution of Risk in Populations;464
14.4.2;10.2.2 High-Risk and Population Approaches to Prevention;467
14.4.3;10.2.3 Safety and the Population Strategy;470
14.4.4;10.2.4 The High-Risk and Population Strategies Revisited;471
14.4.5;10.2.5 Implications of Genomic Research for Disease Prevention;472
14.5;10.3 Evaluation of Screening Programmes;474
14.5.1;10.3.1 Purpose of Screening;475
14.5.2;10.3.2 Criteria for Programme Evaluation;475
14.5.3;10.3.3 Assessing Validity of a Screening Test;476
14.5.4;10.3.4 Methodological Issues in Studies of Screening Programme Effectiveness;484
14.5.5;10.3.5 Are the Wilson-Jungner Criteria Relevant Today?;485
14.6;10.4 Cohort and Period Effects;487
14.6.1;10.4.1 Analysis of Change in Risk Over Time;487
14.6.2;10.4.2 Example: Suicide Trends in UK Men and Women;488
14.7;10.5 Answers to Self-Assessment Exercises;492
15;11 Probability Distributions, Hypothesis Testing, and Bayesian Methods;501
15.1;Introduction and Learning Objectives;501
15.2;Resource Papers;502
15.3;11.1 Probability Distributions;502
15.3.1;11.1.1 Probability - A Brief Review;502
15.3.2;11.1.2 Introduction to Probability Distributions;503
15.3.3;11.1.3 Types of Probability Distribution;505
15.3.4;11.1.4 Probability Distributions: Implications for Statistical Methods;511
15.4;11.2 Data That Do Not Fit a Probability Distribution;512
15.4.1;11.2.1 Robustness of an Hypothesis Test;512
15.4.2;11.2.2 Transforming the Data;512
15.4.3;11.2.3 Principles of Non-Parametric Hypothesis Testing;516
15.5;11.3 Hypothesis Testing: Summary of Common Parametric and Non-Parametric Methods;517
15.5.1;11.3.1 Introduction;517
15.5.2;11.3.2 Review of Hypothesis Tests;518
15.5.3;11.3.3 Fundamentals of Hypothesis Testing;518
15.5.4;11.3.4 Summary: Stages of Hypothesis Testing;519
15.5.5;11.3.5 Comparing Two Independent Groups;520
15.5.6;11.3.6 Comparing Two Paired (or Matched) Groups;524
15.5.7;11.3.7 Testing for Association Between Two Groups;530
15.5.8;11.3.8 Comparing More Than Two Groups;532
15.5.9;11.3.9 Association Between Categorical Variables;537
15.6;11.4 Choosing an Appropriate Hypothesis Test;541
15.6.1;11.4.1 Introduction;541
15.6.2;11.4.2 Using a Guide Table for Selecting a Hypothesis Test;541
15.6.3;11.4.3 The Problem of Multiple Significance Testing;544
15.7;11.5 Bayesian Methods;544
15.7.1;11.5.1 Introduction: A Different Approach to Inference;544
15.7.2;11.5.2 Bayes Theorem and Formula;545
15.7.3;11.5.3 Application and Relevance;546
15.8;11.6 Answers to Self-Assessment Exercises;549
16;Bibliography;553
17;Index;557
18;EULA;570mehr

Autor

Nigel Bruce, PhD is Emeritus Professor of Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Daniel Pope, PhD is Senior Lecturer in Epidemiology and Public Health at the Department of Public Health and Policy, University of Liverpool, UK.

Debbi Stanistreet, PhD is Senior Lecturer and Faculty Director of Widening Participation at the Department of Public Health and Policy, University of Liverpool, UK.
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