Integrating Clinical and Epidemiological Data on Allergic Diseases Across Birth Cohorts: a MeDALL Harmonization Study.
Authors of this article are:
Benet M,, Albang R, Pinart M,, Hohmann C, Tischer CG,, Annesi-Maesano I, Baïz N, Bindslev-Jensen C, Lødrup Carlsen KC, Carlsen KH, Cirugeda L,, Eller E, Fantini MP0, Gehring U, Gerhard B, Gori D0, Hallner E, Kull I, Lenzi J0, McEachan R, Minina E, Momas I, Narduzzi S, Petherick ES0, Porta D, Rancière F, Standl M, Torrent M, Wijga AH, Wright J, Kogevinas M, Guerra S,, Sunyer J,, Keil T, Bousquet J, Maier D, Anto JM,, Garcia-Aymerich J.
A summary of the article is shown below:
International collaborations among birth cohorts to better understand asthma and allergies have increased in the last years. However, differences in definitions and methods preclude direct pooling of original individual participant data. We harmonized data from 14 birth cohorts, with three to 20 follow-ups, from nine European countries, as part of the Mechanisms of the Development of Asthma and Allergies (MeDALL) project. The harmonization process followed six steps: organization of the harmonization panel; identification of variables relevant to MeDALL objectives (candidate variables); proposal of a definition for each candidate variable (reference definition); assessment of the compatibility of each cohort variable to its reference definition (inferential equivalence) and classifications of this inferential equivalence as complete, partial, or impossible; workshop to agree on the reference definitions and classifications of inferential equivalence; and data preparation and delivery through a knowledge management portal. We agreed on 137 reference definitions. The inferential equivalence of 3,551 cohort variables to their corresponding reference definition was classified as complete, partial and impossible for 70%, 15% and 15% of the variables, respectively. A harmonized database was delivered. In birth cohorts of asthma and allergies, the harmonization of data for pooled analyses is feasible and may achieve high inferential comparability. The MeDALL harmonization approach can be used in other collaborative projects.
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