Lorenz, Matthias WAbdi, Negin AshtianiScheckenbach, FrankPflug, AnjaBülbül, AlpaslanCatapano, Alberico LAgewall, StefanEzhov, MaratBots, Michiel LKiechl, StefanOrth, AndreasPROG-IMT study group2024-06-112024-06-112017-041472-69471472-6947https://hdl.handle.net/10161/31168<h4>Background</h4>For an individual participant data (IPD) meta-analysis, multiple datasets must be transformed in a consistent format, e.g. using uniform variable names. When large numbers of datasets have to be processed, this can be a time-consuming and error-prone task. Automated or semi-automated identification of variables can help to reduce the workload and improve the data quality. For semi-automation high sensitivity in the recognition of matching variables is particularly important, because it allows creating software which for a target variable presents a choice of source variables, from which a user can choose the matching one, with only low risk of having missed a correct source variable.<h4>Methods</h4>For each variable in a set of target variables, a number of simple rules were manually created. With logic regression, an optimal Boolean combination of these rules was searched for every target variable, using a random subset of a large database of epidemiological and clinical cohort data (construction subset). In a second subset of this database (validation subset), this optimal combination rules were validated.<h4>Results</h4>In the construction sample, 41 target variables were allocated on average with a positive predictive value (PPV) of 34%, and a negative predictive value (NPV) of 95%. In the validation sample, PPV was 33%, whereas NPV remained at 94%. In the construction sample, PPV was 50% or less in 63% of all variables, in the validation sample in 71% of all variables.<h4>Conclusions</h4>We demonstrated that the application of logic regression in a complex data management task in large epidemiological IPD meta-analyses is feasible. However, the performance of the algorithm is poor, which may require backup strategies.PROG-IMT study groupHumansCarotid Artery DiseasesPrognosisLogistic ModelsPredictive Value of TestsEpidemiologic FactorsAlgorithmsDatabases, FactualMedical Informatics ApplicationsMeta-Analysis as TopicData MiningCarotid Intima-Media ThicknessJournal article