The analysis of record-linked data using multiple imputation

Goldstein, Harvey and Harron, Katie and Wade, Angie (2012) The analysis of record-linked data using multiple imputation. Statistics in Medicine, n/a (n/a). n/a. ISSN 0277-6715

This is the latest version of this item.

Full text not available from this repository.

Official URL:


Probabilistic record linkage techniques assign match weights to one or more potential matches for those individual records that cannot be assigned ‘unequivocal matches’ across data files. Existing methods select the single record having the maximum weight provided this weight is higher than an assigned threshold. We argue that this procedure, which ignores all information from matches with lower weights, and for some individuals assigns no match, is inefficient and may also lead to biases in subsequent analysis of the linked data. It is proposed that a multiple imputation framework is utilised for data that belong to records that cannot be matched unequivocally. In this way the information from all potential matches is transferred through to the analysis stage. This procedure allows for the propagation of matching uncertainty through a full modelling process that preserves the data structure. For purposes of statistical modelling, results from a simulation example suggest that a full probabilistic record linkage is unnecessary and that standard multiple imputation will provide unbiased and efficient parameter estimates.

Item Type:Article
Subjects:5. Quantitative Data Handling and Data Analysis > 5.4 Microdata Methods > 5.4.1 Data linkage
ID Code:2874
Deposited By: LEMMA user
Deposited On:17 Aug 2012 13:42
Last Modified:17 Aug 2012 13:42

Available Versions of this Item

Repository Staff Only: item control page