Data linkage in pharmacoepidemiology: A call for rigorous evaluation and reporting.
data linkage
data quality
pharmacoepidemiology
reporting
research quality
Journal
Pharmacoepidemiology and drug safety
ISSN: 1099-1557
Titre abrégé: Pharmacoepidemiol Drug Saf
Pays: England
ID NLM: 9208369
Informations de publication
Date de publication:
01 2020
01 2020
Historique:
received:
20
05
2019
revised:
17
09
2019
accepted:
21
10
2019
pubmed:
19
11
2019
medline:
21
10
2020
entrez:
19
11
2019
Statut:
ppublish
Résumé
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. 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. 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. 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.
Types de publication
Consensus Development Conference
Journal Article
Practice Guideline
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
9-17Informations de copyright
© 2019 John Wiley & Sons, Ltd.
Références
Dusetzina S, Tyree M, Meyer A, Meyer A, Green L, Carpenter W. Linking Data for Health Services Research: A Framework and Instructional Guide [Internet]. Rockville (MD): Agency for Healthcare Research and Quality (US). Available from: https://www.ncbi.nlm.nih.gov/books/NBK253313/2014.
Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-world evidence-what is it and what can it tell us? N Engl J Med. 2016;375(23):2293-2297.
Rivera DR, Gokhale MN, Reynolds MW, et al. Linking Electronic Health Data in Pharmacoepidemiology: Appropriateness and Feasibility Pharmacoepidemiology and Drug Safety, (Epub ahead of print: October 16, 2019)
Langan SM, Schmidt SA, Wing K, et al. The reporting of studies conducted using observational routinely collected health data statement for pharmacoepidemiology (RECORD-PE). BMJ. 2018;363:k3532.
Benchimol EI, Smeeth L, Guttmann A, et al. The reporting of studies conducted using observational routinely-collected health data (RECORD) statement. PLoS medicine. 2015;12(10):e1001885.
von Elm E, Altman DG, Egger M, et al. The strengthening the reporting of observational studies in epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457.
Benchimol EI, Langan S, Guttmann A, Committee RS. Call to RECORD: the need for complete reporting of research using routinely collected health data. Journal of clinical epidemiology. 2013;66(7):703-705.
Harron K, Goldstein H, Wade A, Muller-Pebody B, Parslow R, Gilbert R. Linkage, evaluation and analysis of national electronic healthcare data: application to providing enhanced blood-stream infection surveillance in paediatric intensive care. PloS one. 2013;8(12):e85278.
Administration UFaD. General principles of software validation; final guidance for industry and FDA Staff. 2002; https://www.fda.gov/RegulatoryInformation/Guidances/ucm085281.htm#_Toc517237938.
Beauchamp A, Tonkin AM, Kelsall H, et al. Validation of de-identified record linkage to ascertain hospital admissions in a cohort study. BMC Med Res Methodol. 2011;11:42.
Setoguchi S, Zhu Y, Jalbert JJ, Williams LA, Chen CY. Validity of deterministic record linkage using multiple indirect personal identifiers: linking a large registry to claims data. Circ Cardiovasc Qual Outcomes. 2014;7(3):475-480.
Zhu Y, Chen CY, Matsuyama Y, Ohashi Y, Franklin JM, Setoguchi S. Comparative validity of methods to select appropriate cutoff weight for probabilistic linkage without unique personal identifiers. Pharmacoepidemiol Drug Saf. 2016;25(4):444-452.
Tromp M, Ravelli AC, Meray N, Reitsma JB, Bonsel GJ. An efficient validation method of probabilistic record linkage including readmissions and twins. Methods Inf Med. 2008;47(4):356-363.
Mears GD, Rosamond WD, Lohmeier C, et al. A link to improve stroke patient care: a successful linkage between a statewide emergency medical services data system and a stroke registry. Acad Emerg Med. 2010;17(12):1398-1404.
Corbell C, Katjitae I, Mengistu A, et al. Records linkage of electronic databases for the assessment of adverse effects of antiretroviral therapy in sub-Saharan Africa. Pharmacoepidemiol Drug Saf. 2012;21(4):407-414.
Baldwin E, Johnson K, Berthoud H, Dublin S. Linking mothers and infants within electronic health records: a comparison of deterministic and probabilistic algorithms. Pharmacoepidemiol Drug Saf. 2015;24(1):45-51.
Hammill BG, Hernandez AF, Peterson ED, Fonarow GC, Schulman KA, Curtis LH. Linking inpatient clinical registry data to Medicare claims data using indirect identifiers. American heart journal. 2009;157(6):995-1000.
Beauchamp A, Tonkin AM, Kelsall H, et al. Validation of de-identified record linkage to ascertain hospital admissions in a cohort study. BMC medical research methodology. 2011;11(1):42.
Penberthy L, McClish D, Pugh A, Smith W, Manning C, Retchin S. Using hospital discharge files to enhance cancer surveillance. American journal of epidemiology. 2003;158(1):27-34.
Harron K, Gilbert R, Cromwell D, van der Meulen J. Linking data for mothers and babies in de-identified electronic health data. PloS one. 2016;11(10):e0164667.
Harron K, Hagger-Johnson G, Gilbert R, Goldstein H. Utilising identifier error variation in linkage of large administrative data sources. BMC Med Res Methodol. 2017;17(1):23.
Zhu Y, Matsuyama Y, Ohashi Y, Setoguchi S. When to conduct probabilistic linkage vs. deterministic linkage? A simulation study. J Biomed Inform. 2015;56:80-86.
Li B, Quan H, Fong A, Lu M. Assessing record linkage between health care and vital statistics databases using deterministic methods. BMC health services research. 2006;6(1):48.
Moore CL, Gidding HF, Law MG, Amin J. Poor record linkage sensitivity biased outcomes in a linked cohort analysis. Journal of clinical epidemiology. 2016;75:70-77.
Blakely T, Salmond C. Probabilistic record linkage and a method to calculate the positive predictive value. International Journal of Epidemiology. 2002;31(6):1246-1252.
Pacheco AG, Saraceni V, Tuboi SH, et al. Validation of a hierarchical deterministic record-linkage algorithm using data from 2 different cohorts of human immunodeficiency virus-infected persons and mortality databases in Brazil. American journal of epidemiology. 2008;168(11):1326-1332.
Setoguchi S, Zhu Y, Jalbert JJ, Williams LA, Chen C-Y. Validity of deterministic record linkage using multiple indirect personal identifiers. Circulation: Cardiovascular Quality and Outcomes. 2014;7(3):475-480.
Potosky AL, Riley GF, Lubitz JD, Mentnech RM, Kessler LG. Potential for cancer related health services research using a linked Medicare-tumor registry database. Medical care. 1993;31(8):732-748.
Cole SR, Stuart EA. Generalizing evidence from randomized clinical trials to target populations: The ACTG 320 trial. Am J Epidemiol. 2010;172(1):107-115.
Hernan MA, Hernandez-Diaz S, Robins JM. A structural approach to selection bias. Epidemiology. 2004;15(5):615-625.
Goldstein H, Harron K, Wade A. The analysis of record-linked data using multiple imputation with data value priors. Stat Med. 2012;31(28):3481-3493.
Harron KL, Doidge JC, Knight HE, et al. A guide to evaluating linkage quality for the analysis of linked data. Int J Epidemiol. 2017;46(5):1699-1710.
Silveira DP, Artmann E. Accuracy of probabilistic record linkage applied to health databases: systematic review. Rev Saude Publica. 2009;43(5):875-882.
O'Brien, E. C., et al. (2019). “Patient perpectives on the linkage of health data for reseach: Insights from an online patient community questionnaire.” Int J Med Inform 127:9-17.
Patients Like Me. Available at: https://www.patientslikeme.com/,. accessed: February 28, 2018.
Aitken M, de St Jorre J, Pagliari C, Jepson R, Cunningham-Burley S. Public responses to the sharing and linkage of health data for research purposes: a systematic review and thematic synthesis of qualitative studies. BMC Med Ethics. 2016;17(1):73.
Berger ML, Sox H, Willke RJ, et al. Good practices for real-world data studies of treatment and/or comparative effectiveness: recommendations from the joint ISPOR-ISPE Special Task Force on real-world evidence in health care decision making. Pharmacoepidemiol Drug Saf. 2017;26(9):1033-1039.
Randall SM, Boyd JH, Ferrante AM, Bauer JK, Semmens JB. Use of graph theory measures to identify errors in record linkage. Computer methods and programs in biomedicine. 2014;115(2):55-63.
Ristoski P, Bizer C, Paulheim H. Mining the web of linked data with rapidminer. Web Semantics: Science, Services and Agents on the World Wide Web. 2015;35:142-151.
Zaveri A, Rula A, Maurino A, Pietrobon R, Lehmann J, Auer S. Quality assessment for linked data: A survey. Semantic Web. 2016;7(1):63-93.
Paulheim H, Bizer C. Improving the quality of linked data using statistical distributions. International Journal on Semantic Web and Information Systems (IJSWIS). 2014;10(2):63-86.