Methodological Developments in Data Linkage (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
296
Utgivningsdatum
2015-12-11
Upplaga
1
Förlag
John Wiley & Sons Inc
Dimensioner
254 x 177 x 25 mm
Vikt
589 g
Antal komponenter
1
ISBN
9781118745878
Methodological Developments in Data Linkage (inbunden)

Methodological Developments in Data Linkage

Inbunden Engelska, 2015-12-11
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A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features : Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.
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Övrig information

Editors: Katie Harron, London School of Hygiene and Tropical Medicine, UK Harvey Goldstein, University of Bristol and University College London, UK Chris Dibben, University of Edinburgh, UK

Innehållsförteckning

Foreword xi Contributors xiii 1 Introduction 1 Katie Harron, Harvey Goldstein and Chris Dibben 1.1 Introduction: data linkage as it exists 1 1.2 Background and issues 2 1.3 Data linkage methods 3 1.3.1 Deterministic linkage 3 1.3.2 Probabilistic linkage 3 1.3.3 Data preparation 4 1.4 Linkage error 5 1.5 Impact of linkage error on analysis of linked data 6 1.6 Data linkage: the future 7 2 Probabilistic linkage 8 William E. Winkler 2.1 Introduction 8 2.2 Overview of methods 10 2.2.1 The Fellegi Sunter model of record linkage 10 2.2.2 Learning parameters 13 2.2.3 Additional methods for matching 20 2.2.4 An empirical example 22 2.3 Data preparation 23 2.3.1 Description of a matching project 24 2.3.2 Initial file preparation 25 2.3.3 Name standardisation and parsing 26 2.3.4 Address standardisation and parsing 27 2.3.5 Summarising comments on preprocessing 27 2.4 Advanced methods 28 2.4.1 Estimating false ]match rates without training data 28 2.4.2 Adjusting analyses for linkage error 32 2.5 Concluding comments 35 3 The data linkage environment 36 Chris Dibben, Mark Elliot, Heather Gowans, Darren Lightfoot and Data Linkage Centres 3.1 Introduction 36 3.2 The data linkage context 37 3.2.1 Administrative or routine data 37 3.2.2 The law and the use of administrative (personal) data for research 38 3.2.3 The identifiability problem in data linkage 42 3.3 The tools used in the production of functional anonymity through a data linkage environment 42 3.3.1 Governance, rules and the researcher 43 3.3.2 Application process, ethics scrutiny and peer review 43 3.3.3 Shaping safe behaviour: training, sanctions, contracts and licences 43 3.3.4 Safe data analysis environments 44 3.3.5 Fragmentation: separation of linkage process and temporary linked data 47 3.4 Models for data access and data linkage 50 3.4.1 Single centre 50 3.4.2 Separation of functions: firewalls within single centre 51 3.4.3 Separation of functions: TTP linkage 53 3.4.4 Secure multiparty computation 53 3.5 Four case study data linkage centres 54 3.5.1 Population Data BC 54 3.5.2 The Secure Anonymised Information Linkage Databank, United Kingdom 58 3.5.3 Centre for Data Linkage (Population Health Research Network), Australia 59 3.5.4 The Centre for Health Record Linkage, Australia 61 3.6 Conclusion 62 4 Bias in data linkage studies 63 Megan Bohensky 4.1 Background 63 4.2 Description of types of linkage error 65 4.2.1 Missed matches from missing linkage variables 65 4.2.2 Missed matches from inconsistent case ascertainment 66 4.2.3 False matches: Description of cases incorrectly matched 66 4.3 How linkage error impacts research findings 68 4.3.1 Results 68 4.3.2 Assessment of linkage bias 75 4.4 Discussion 78 4.4.1 Potential biases in the review process 79 4.4.2 Recommendations and implications for practice 79 5 Secondary analysis of linked data 83 Raymond Chambers and Gunky Kim 5.1 Introduction 83 5.2 Measurement error issues arising from linkage 84 5.2.1 Correct links, incorrect links and non ]links 84 5.2.2 Characterising linkage errors 85 5.2.3 Characterising errors from non ]linkage 86 5.3 Models for different types of linking errors 86 5.3.1 Linkage errors under binary linking 86 5.3.2 Linkage errors under multi ]linking 88 5.3.3 Incomplete linking 88 5.3.4 Modelling the linkage error 89 5.4 Regression analysis using complete binary ]linked data 90 5.4.1 Linear regression 91 5.4.2 Logistic regression 95 5.5 Regression analysis using incomplete binary ]linked data 95 5.5.1 Linear regression using incomplete sample to register linked data 97 5.6 Regression analysis with multi ]linked data 99 5.6.1 Uncorrelated multi ]linking: Comple