Automatic identification of variables in epidemiological datasets using logic regression

作者：Lorenz Matthias W^*; Abdi Negin Ashtiani; Scheckenbach Frank; Pflug Anja; Buelbuel Alpaslan; Catapano Alberico L; Agewall Stefan; Ezhov Marat; Bots Michiel L; Kiechl Stefan; Orth Andreas

来源：BMC Medical Informatics and Decision Making, 2017, 17(1): 40.

DOI：10.1186/s12911-017-0429-1

• Meta-analysis
• Data management
• Logic regression
• Epidemiology

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.

• 出版日期2017-4-13