02626nas a2200277 4500008004100000022001400041245009400055210006900149260001600218300000700234490000700241520180500248100002402053700002602077700002402103700001602127700002302143700002602166700002002192700001702212700002102229700001902250700001802269710002502287856003602312 2017 eng d a1472-694700aAutomatic identification of variables in epidemiological datasets using logic regression.0 aAutomatic identification of variables in epidemiological dataset c2017 Apr 13 a400 v173 a

BACKGROUND: 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.

METHODS: 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.

RESULTS: 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.

CONCLUSIONS: 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.

1 aLorenz, Matthias, W1 aAbdi, Negin, Ashtiani1 aScheckenbach, Frank1 aPflug, Anja1 aBülbül, Alpaslan1 aCatapano, Alberico, L1 aAgewall, Stefan1 aEzhov, Marat1 aBots, Michiel, L1 aKiechl, Stefan1 aOrth, Andreas1 aPROG-IMT Study Group uhttps://chs-nhlbi.org/node/7574