Methodological Developments in Data Linkage

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

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: Complete linkage 100 5.6.2 Uncorrelated multi ]linking: Sample to register linkage 101 5.6.3 Correlated multi ]linkage 105 5.6.4 Incorporating auxiliary population information 105