BMC Med Inform Decis Mak - Harmonisation of variables names prior to conducting statistical analyses with multiple datasets: an automated approach.

Tópicos

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Resumo

CKGROUND: Data requirements by governments, donors and the international community to measure health and development achievements have increased in the last decade. Datasets produced in surveys conducted in several countries and years are often combined to analyse time trends and geographical patterns of demographic and health related indicators. However, since not all datasets have the same structure, variables definitions and codes, they have to be harmonised prior to submitting them to the statistical analyses. Manually searching, renaming and recoding variables are extremely tedious and prone to errors tasks, overall when the number of datasets and variables are large. This article presents an automated approach to harmonise variables names across several datasets, which optimises the search of variables, minimises manual inputs and reduces the risk of error.RESULTS: Three consecutive algorithms are applied iteratively to search for each variable of interest for the analyses in all datasets. The first search (A) captures particular cases that could not be solved in an automated way in the search iterations; the second search (B) is run if search A produced no hits and identifies variables the labels of which contain certain key terms defined by the user. If this search produces no hits, a third one (C) is run to retrieve variables which have been identified in other surveys, as an illustration. For each variable of interest, the outputs of these engines can be (O1) a single best matching variable is found, (O2) more than one matching variable is found or (O3) not matching variables are found. Output O2 is solved by user judgement. Examples using four variables are presented showing that the searches have a 100% sensitivity and specificity after a second iteration.CONCLUSION: Efficient and tested automated algorithms should be used to support the harmonisation process needed to analyse multiple datasets. This is especially relevant when the numbers of datasets or variables to be included are large.

Resumo Limpo

ckground data requir govern donor intern communiti measur health develop achiev increas last decad dataset produc survey conduct sever countri year often combin analys time trend geograph pattern demograph health relat indic howev sinc dataset structur variabl definit code harmonis prior submit statist analys manual search renam recod variabl extrem tedious prone error task overal number dataset variabl larg articl present autom approach harmonis variabl name across sever dataset optimis search variabl minimis manual input reduc risk errorresult three consecut algorithm appli iter search variabl interest analys dataset first search captur particular case solv autom way search iter second search b run search produc hit identifi variabl label contain certain key term defin user search produc hit third one c run retriev variabl identifi survey illustr variabl interest output engin can o singl best match variabl found o one match variabl found o match variabl found output o solv user judgement exampl use four variabl present show search sensit specif second iterationconclus effici test autom algorithm use support harmonis process need analys multipl dataset especi relev number dataset variabl includ larg

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