Automatic identification of variables in epidemiological datasets using logic regression

textabstractBackground: 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

Excess Enthalpies in Binary Systems of Isomeric C₈ Aliphatic Monoethers with Acetonitrile and Their Description by the COSMO-SAC Model

The excess enthalpies at 298.15 and 308.15 K for six binary mixtures of acetonitrile + C₈ aliphatic ether {heptyl methyl ether CH₃O^<i>n</i>C₇H₁₅, or ethylhexyl ether C₂H₅O^<i>n</i>C₆H₁₃, or pentyl propyl ether ^<i>n</i>C₃H₇O^<i>n</i>C₅H₁₁, or isopentyl propyl ether ^<i>n</i>C₃H₇O^<i>i</i> C₅H₁₁, or dibutyl ether ^<i>n</i>C₄H₉O^<i>n</i>C₄H₉, or butyl isobutyl ether ^<i>n</i>C₄H₉O^<i>i</i>C₄H₉} have been determined by isothermal titration calorimetry using the TA Instruments (model TAM III) calorimeter. The possibility of the COSMO-SAC model to account for the thermodynamic differences between these systems has been tested