Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers

Zhu, Ying; Chen, Chih‐Ying; Matsuyama, Yutaka; Ohashi, Yasuo; Franklin, Jessica M.; Setoguchi, Soko

doi:10.1002/pds.3832

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…If unique identifiers are not available, probabilistic matching methods link data sources through a range of indirect, non‐unique data elements that can identify an individual when used in combination with other non‐unique information (e.g., date of birth, place of residence, or demographic characteristics) …”

Section: Resultsmentioning

confidence: 99%

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility

Rivera

Gokhale

Reynolds

et al. 2020

Pharmacoepidemiology and Drug

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Purpose: To provide guidance on data linkage appropriateness and feasibility to plan purposeful and sustainable new linkages that advance pharmacoepidemiology and healthcare research. Planning a new data linkage requires careful evaluation to weigh the resources required with the potential overall benefits. Methods:In response to an International Society for Pharmacoepidemiology (ISPE) call for manuscripts, a working group comprised of members from academic, industry, and government determined priority content areas; appropriateness and feasibility of data linkage was selected. Within this topic, scientific and operational considerations were determined, reviewed, and formulated into key areas, and translated into 12 consensus recommendations.Results: Guidance for feasibility assessment was categorized into five key areas:(1) research objectives and justification; (2) data quality and completeness; (3) the linkage process; (4) data ownership and governance; and (5) overall value added by linkage. Within these key areas, recommendations to consider prior to initiation were developed to evaluate suitability of the linkage to meet research objectives, assess source data completeness and population coverage, and ensure well-defined data governance standards and protections. When creating novel linked datasets, researchers must assess the feasibility of both scientific (data quality and linkage methods) and operational (access, data use and transfer, governance, and cost) aspects. Conclusions:The data linkage feasibility assessment considerations outlined can be used as a guide when designing sustainable linked data resources to generate actionable evidence in healthcare research. These recommendations were constructed for wide applicability and can be adapted depending on the geographic, structural, and data components of the linkage. K E Y W O R D Sdata linkage, feasibility assessment, data methods, big data, healthcare research

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Section: Resultsmentioning

confidence: 99%

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility

Rivera

Gokhale

Reynolds

et al. 2020

Pharmacoepidemiology and Drug

View full text Add to dashboard Cite

show abstract

“…Reference standards may be internally derived from data elements that are available in both databases but were not used as part of the linkage algorithm. For example, combinations of hospital name + admission + discharge date or unique personal identifiers (eg, social security number in the United States) in combination with nonunique identifiers (eg, date of birth) can serve as a confirmation of successful linkage . Reference standards may be externally derived, for example, from an existing data source that can be linked to the individual data files and/or crosswalk .…”

Section: Resultsmentioning

confidence: 99%

Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting

Pratt

Mack

Meyer

et al. 2019

Pharmacoepidemiology and Drug

Self Cite

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Purpose: The purpose of this paper is to provide guidance on the evaluation of data linkage quality through the development of a checklist for reporting key elements of the linkage process. Methods:Responding to a call for manuscripts from the International Society for Pharmacoepidemiology (ISPE), a working group including international representation from the academic, industry, and contract research, and regulatory sectors was formed to develop a checklist for evaluation of data linkage performance and reporting data linkage specifically for pharmacoepidemiologic research. This checklist expands on the reporting of studies conducted using observational routinely collected health data specific to pharmacoepidemiology (RECORD-PE) guidelines. Results:A key aspect of data linkage evaluation for pharmacoepidemiology is to articulate how a linkage process was performed and its accuracy in terms of validation and verification of the resulting linked data. This study generates a checklist, which covers domains including data sources, linkage variables, linkage methods, linkage results, and linkage evaluation. For each domain, specific recommendations provide a clear and transparent assessment of the linkage process. Conclusions:Linking data sources can help to enrich analytic databases to more accurately define study populations, enable adjustment for confounding, and improve the capture of health outcomes. Clear and transparent reporting of data linkage processes will help to increase confidence in the evidence generated from these data by allowing researchers and end users to critically assess the potential for bias owing to the data linkage process.

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“…In theory, some MADM methods based on statistics and probability can be regarded as probabilistic linkage to different databases (Comber & Arribas‐Bel, 2019), and their performance depends greatly on the completeness and accuracy of data (Zhu et al, 2016). In addition, faced with many decision variables, some MADM methods encounter difficulty in the selection and calculation of attributes (Yu, Guikema, Briaud, & Burnett, 2012).…”

Section: Introductionmentioning

confidence: 99%

A points of interest matching method using a multivariate weighting function with gradient descent optimization

Yang

Wang

Zhang

et al. 2020

Transactions in GIS

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Volunteered geographic information contains abundant valuable data, which can be applied to various spatiotemporal geographical analyses. While the useful information may be distributed in different, low‐quality data sources, this issue can be solved by data integration. Generally, the primary task of integration is data matching. Unfortunately, due to the complexity and irregularities of multi‐source data, existing studies have found it difficult to efficiently establish the correspondence between different sources. Therefore, we present a multi‐stage method to match multi‐source data using points of interest. A spatial filter is constructed to obtain candidate sets for geographical entities. The weights of non‐spatial characteristics are examined by a machine learning‐related algorithm with artificially labeled random samples. A case study on Fuzhou reveals that an average of 95% of instances are accurately matched. Thus, our study provides a novel solution for researchers who are engaged in data mining and related work to accurately match multi‐source data via knowledge obtained by the idea and methods of machine learning.

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Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers

Abstract: Probabilistic linkage without UPI generated valid linkages when an optimal cutoff was chosen. Cutoff selection remains challenging; however, histogram inspection, the duplicate method, and the odds formula method can be used in conjunction when a gold standard is not available.

Cited by 5 publications

References 22 publications

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility

Linking electronic health data in pharmacoepidemiology: Appropriateness and feasibility

Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting

A points of interest matching method using a multivariate weighting function with gradient descent optimization

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