Maelstrom Research guidelines for rigorous retrospective data harmonization

International Journal of Epidemiology

Volumn 46, Issue 1, 2017, Pages 103-115

  Fortier, Isabel ^a   Raina, Parminder ^b   Van den Heuvel, Edwin R ^c   Griffith, Lauren E ^b   Craig, Camille ^a   Saliba, Matilda ^a   Doiron, Dany ^a   Stolk, Ronald P ^d   Knoppers, Bartha M ^e   Ferretti, Vincent ^f   Granda, Peter ^g   Burton, Paul ^h  

^a RESEARCH INSTITUTE OF THE MCGILL UNIVERSITY HEALTH CENTRE   (Canada)

^b MCMASTER UNIVERSITY   (Canada)

^c EINDHOVEN UNIVERSITY OF TECHNOLOGY   (Netherlands)

^d UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^e MCGILL UNIVERSITY   (Canada)

^f ONTARIO INSTITUTE FOR CANCER RESEARCH   (Canada)

^g UNIVERSITY OF MICHIGAN   (United States)

^h UNIVERSITY OF BRISTOL   (United Kingdom)

Author keywords

Data harmonization; Data integration; Data processing; Individual participant data; Meta analysis; Retrospective harmonization

Indexed keywords

ANALYTICAL METHOD; DATA PROCESSING; DATA QUALITY; DATA SET; GUIDELINE; INTEGRATED APPROACH; LITERATURE REVIEW; META-ANALYSIS; RESEARCH WORK;

ARTICLE; CASE STUDY; DOCUMENTATION; HEALTH SURVEY; HUMAN; KNOWLEDGE; MAELSTROM RESEARCH GUIDELINE; NOMENCLATURE; PRACTICE GUIDELINE; PRIORITY JOURNAL; PROCEDURES; PUBLICATION; RESEARCH; RETROSPECTIVE STUDY; WORKSHOP; INFORMATION PROCESSING; MEDICAL RESEARCH; METHODOLOGY; STANDARDS; STATISTICAL ANALYSIS; STATISTICS AND NUMERICAL DATA;

BIOMEDICAL RESEARCH; DATA COLLECTION; DATA INTERPRETATION, STATISTICAL; GUIDELINES AS TOPIC; HUMANS; RESEARCH DESIGN; RETROSPECTIVE STUDIES;

EID: 85019727094     PISSN: 03005771     EISSN: 14643685     Source Type: Journal    
DOI: 10.1093/ije/dyw075     Document Type: Article

References (64)

1. Gallacher JE. The case for large scale fungible cohorts. Eur J Public Health 2007;17:548-49
2. Gallacher J, Hofer SM. Generating large-scale longitudinal data resources for aging research. J Gerontol B Psychol Sci Soc Sci 2011;66(Suppl 1):i172-79
3. Frank C, Battista R, Butler L et al. Making an Impact: A Preferred Framework and Indicators to Measure Returns on Investment in Health Research. Ottawa, ON: CanadianAcademy of Health, 2009
4. Dalziel M, Roswell J, Tahmina TN, Xiao Zhao. Impact of government investments in research & innovation: review of academic investigations. Optimum ONline 2012;42(2)
5. Wolfson M, Wallace SE, Masca N et al. DataSHIELD: resolving a conflict in contemporary bioscience-performing a pooled analysis of individual-level data without sharing the data. Int J Epidemiol 2010;39:1372-82
6. Gaye A, Marcon Y, Isaeva J et al. DataSHIELD: taking the analysis to the data, not the data to the analysis. Int J Epidemiol 2014;43:1929-44
7. Wong MY, Day NE, Luan JA, Chan KP, Wareham NJ. The detection of gene-environment interaction for continuous traits: should we deal with measurement error by bigger studies or better measurement? Int J Epidemiol 2003;32:51-7
8. Granda P, Blasczyk E. Data Harmonization. Guidelines for Best Practice in Cross-Cultural Surveys. 3rd edn. Ann Arbor, MI: Survey Research Centre, Institute for Social Reseach, University of Michigan, 2011
9. Fortier I, Doiron D, Burton P, Raina P. Invited commentary: consolidating data harmonization-how to obtain quality and applicability? Am J Epidemiol 2011;61: 264-66
10. Hutchinson DM, Silins E, Mattick RP et al. How can data harmonisation benefit mental health research? An example of the Cannabis Cohorts Research Consortium. Aust N Z J Psychiatry 2015;49:317-23
11. Minicuci N, Noale M, Bardage C et al. Cross-national determinants of quality of life from six longitudinal studies on aging: the CLESA project. Aging Clin Exp Res 2003;15: 187-202
12. Pigeot I, Barba G, Chadjigeorgiou, et al. Prevalence and determinants of childhood overweight and obesity in European countries: pooled analysis of the existing surveys within the IDEFICS Consortium. Int J Obes 2009;33:1103-10
13. Schaap LA, Peeters GM, Dennison EM et al. European Project on Osteoarthritis (EPOSA): Methodological challenges in harmonization of existing data from five European population-based cohorts on aging. BMC Musculoskel Disord 2011;12:272
14. Horwood LJ, Fergusson DM, Coffey C et al. Cannabis and depression: an integrative data analysis of four Australasian cohorts. Drug Alcohol Depend 2012;126:369-78
15. Silins E, Horwood LJ, Patton GC et al. Young adult sequelae of adolescent cannabis use: an integrative analysis. Lancet Psychiatry 2014;1:286-93
16. Smith-Warner SA, Spiegelman D, Ritz J et al. Methods for pooling results of epidemiologic studies: the Pooling Project of Prospective Studies of Diet and Cancer. Am J Epidemiol 2006;163:1053-64
17. Emerging Risk Factor Collaboration, Danesh J, Erqou S et al. The Emerging Risk Factors Collaboration: analysis of individual data on lipid, inflammatory and other markers in over 1.1 million participants in 104 prospective studies of cardiovascular diseases. Eur J Epidemiol 2007;22:839-69
18. Gehring U, Casas M, Brunekreef B et al. Environmental exposure assessment in European birth cohorts: results from the ENRIECO project. Environ Health 2013;12:8
19. Boffetta P, McLerran D, Chen Y et al. Body mass index and diabetes in Asia: a cross-sectional pooled analysis of 900 000 individuals in the Asia cohort consortium. PLoS One 2011;6:e19930
20. Hohmann C, Govarts E, Bergström A et al. Joint data analyses of European birth cohorts: two different approaches. WebmedCentral EPIDEMIOLOGY 2012;3:WMC003869
21. Morabia A, Northridge ME, Beer-Borst S et al. ftES. Harmonising Local Health Survey Data: The EURALIM Experience. London: Springer, 2003
22. ALPHA Network. 2016 http://alpha.lshtm.ac.uk/ (27 August 2015, date last accessed)
23. Zaba B, Calvert C, Marston M et al. Effect of HIV infection on pregnancy-related mortality in sub-Saharan Africa: secondary analyses of pooled community-based data from the network for Analysing Longitudinal Populationbased HIV/AIDS data on Africa (ALPHA). Lancet 2013;381:1763-71
24. Leroy V, Newell ML, Dabis F et al. International multicentre pooled analysis of late postnatal mother-to-child transmission of HIV-1 infection. Ghent International Working Group on Mother-to-Child Transmission of HIV. Lancet 1998;352: 597-600
25. Akobeng AK. Principles of evidence based medicine. Arch Dis Child 2005;90:837-40
26. Moher D, Liberati A, Tetzlaff J et al. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann Intern Med 2009;151:264-69
27. Stroup DF, Berlin JA, Morton SC et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Metaanalysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008-12
28. Moher D, Shamseer L, Clarke M et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev 2015;4:1
29. Bennett SN, Caporaso N, Fitzpatrick AL et al. Phenotype harmonization and cross-study collaboration in GWAS consortia: the GENEVA experience. Genet Epidemiol 2011;35: 159-73
30. Rolland B, Reid S, Stelling D et al. Toward rigorous data harmonization in cancer epidemiology research: one approach. Am J Epidemiol 2015;182:1033-38
31. Norwegian Institute of Public Health. PHOEBE-Promoting Harmonisation of Epidemiological Biobanks in Europe 2009. http://www.fhi.no/artikler/?id = 73793 (27 August 2015, date last accessed)
32. Fortier I, Doiron D, Little J et al. Is rigorous retrospective harmonization possible? Application of the DataSHaPER approach across 53 large studies. Int J Epidemiol 2011;40:1314-28
33. Public Population Project in Genomics and Society. Public Population Project in Genomics and Society 2014. http://www. p3g.org/ (27 August 2015, date last accessed)
34. Canadian Partnership for Tomorrow Project. Canadian Partnership for Tomorrow Project 2015. http://www.partner shipfortomorrow.ca/ (27 August 2015, date last accessed)
35. Biobank Standardisation and Harmonisation for Research Excellence in the European Union. BioSHaRE 2013. https:// www.bioshare.eu/ (27 August 2015, date last accessed)
36. Biobanking and Biomolecular Resources Research Infrastructure-Large Prospective Cohorts. BBMRI-LPC 2015 [cited 2015 August 27]. Available from: http://www.bbmri-lpc.org/ (27 August 2015, date last accessed)
37. InterConnect. InterConnect: a global initiative on diabetes geneenvironment interaction 2015 [cited 2015 August 27]. Available from: http://www.interconnect-diabetes.eu/ (27 August 2015, date last accessed)
38. Fortier I, Burton PR, Robson PJ, et al. Quality, quantity and harmony: the DataSHaPER approach to integrating data across bioclinical studies. Int J Epidemiol 2010;39(5):1383-1393
39. AirPROM. 2013 [cited 2015 August 27]. Available from: http:// www.europeanlung.org/en/projects-and-research/projects/air prom/home (27 August 2015, date last accessed)
40. Woodward M, Barzi F, Martiniuk A, et al. Cohort profile: the Asia Pacific Cohort Studies Collaboration. Int J Epidemiol 2006;35(6):1412-16
41. Doiron D, Burton P, Marcon Y, et al. Data harmonization and federated analysis of population-based studies: the BioSHaRE project. Emerg Themes Epidemiol 2013;10(1):12
42. Boffetta P, Bobak M, Borsch-Supan A, et al. The Consortium on Health and Ageing: Network of Cohorts in Europe and the United States (CHANCES) project-design, population and data harmonization of a large-scale, international study. Eur J Epidemiol 2014;29(12):929-36
43. Cohort & Longitudinal Studies Enhancement Resources. 2012. http://www.closer.ac.uk /(27 August 2015, date last accessed)
44. Sachdev PS, Lipnicki DM, Kochan NA et al. COSMIC (Cohort Studies of Memory in an International Consortium): an international consortium to identify risk and protective factors and biomarkers of cognitive ageing and dementia in diverse ethnic and sociocultural groups. BMC Neurol 2013; 13:165
45. Anstey KJ, Byles JE, Luszcz MA et al. Cohort Profile: The Dynamic Analyses to Optimize Ageing (DYNOPTA) project. Int J Epidemiol 2010;39:44-51
46. Fall T, Hägg S, Mägi R et al. the role of adiposity in cardiometabolic traits: a mendelian randomization analysis. PLoS Med 2013;10:e1001474
47. Riboli E, Hunt KJ, Slimani N et al. European Prospective Investigation into Cancer and Nutrition (EPIC): study populations and data collection. Public Health Nutr 2002;5:1113-24
48. Muilu J, Peltonen L, Litton JE. The federated database-a basis for biobank-based post-genome studies, integrating phenome and genome data from 600, 000 twin pairs in Europe. Eur J Hum Genet 2007;15:718-23
49. Cooper R, Hardy R, Aihie Sayer A et al. Age and gender differences in physical capability levels from mid-life onwards: the harmonisation and meta-analysis of data from eight UK cohort studies. PLoS One 2011;6:e27899
50. Hofer SM, Piccinin AM. Integrative data analysis through coordination of measurement and analysis protocol across independent longitudinal studies. Psychol Methods 2009;1:15-164
51. Hashibe M, Brennan P, Benhamou S et al. Alcohol drinking in never users of tobacco, cigarette smoking in never drinkers, and the risk of head and neck cancer: pooled analysis in the International Head and Neck Cancer Epidemiology Consortium. J Natl Cancer Inst 2007;99:777-89
52. Griffith L, van den Heuvel E, Fortier I et al. Harmonization of Cognitive Measures in Individual Participant Data and Aggregate Data Meta-analysis. Rockville, MD: Agency for Healthcare Research and Quality, 2013
53. Bath PA, Deeg D, Poppelaars J. The harmonisation of longitudinal data: a case study using data from cohort studies in The Netherlands and the United Kingdom. Ageing & Society 2010;30:1419-37
54. Meta-analysis Global Group in Chronic Heart Failure (MAGGIC).The survival of patients with heart failure with preserved or reduced left ventricular ejection fraction: an individual patient data meta-analysis. Eur Heart J 2012;33:1750-57
55. Whalley GA, Gamble GD, Dini FL et al. Individual patient meta-analyses of restrictive diastolic filling pattern and mortality in patients post acute myocardial infarction and in patients with chronic heart failure. Int J Cardiol 2007;122: 207-15
56. Evans A, Salomaa V, Kulathinal S et al. MORGAM (an international pooling of cardiovascular cohorts). Int J Epidemiol 2005;34:21-7
57. Matise TC, Ambite JL, Buyske S et al. The Next PAGE in understanding complex traits: design for the analysis of Population Architecture Using Genetics and Epidemiology (PAGE) Study. Am J Epidemiol 2011;174:849-59
58. Lorenz MW, Polak JF, Kavousi M et al. Carotid intima-media thickness progression to predict cardiovascular events in the general population (the PROG-IMT collaborative project): a meta-analysis of individual participant data. Lancet 2012;379: 2053-62
59. French DJ, Browning C, Kendig H et al. A simple measure with complex determinants: investigation of the correlates of selfrated health in older men and women from three continents. BMC Public Health 2012;12:649
60. McEniryM, Moen S, McDermott J. Methods report on the compilation of the RELATE cross national data set on older adults from 20 low, middle and high income countries. University of Michigan, 2013. http://www.disc.wisc.edu/codebooks/qm512 001.pdf (27 August 2015, date last accessed)
61. Silander K, Eklund N, Laukkanen M. Protocol for retrospective harmonization of phenotype data in epidemiological sample collections. 2012 (unpublished)
62. Kern ML, Hampson SE, Goldberg LR, Friedman HS. Integrating prospective longitudinal data: modeling personality and health in the Terman Life Cycle and Hawaii Longitudinal Studies. Dev Psychol 2014;50:1390-406
63. Rodondi N, den Elzen WP, Bauer D, et al. Subclinical hypothyroidism and the risk of coronary heart disease and mortality. JAMA 2010;304:1365-74
64. Allen J, Inder KJ, Lewin TJ et al. Integrating and extending cohort studies: lessons from the eXtending Treatments, Education and Networks in Depression (xTEND) study. BMC Med Res Methodol 2013; 122