Implementation of an automatic quality control of derived data files for NONMEM

Abstract: A pharmacometric analysis must be based on correct data to be valid. Source clinical data is rarely ready to be modelled as is, but rather needs to be reprogrammed to fit the format required by the pharmacometric modelling software. The reprogramming steps include selecting the subsets of data relevant for modelling, deriving new information from the source and adjusting units and encoding. Sometimes, the source data may also be flawed, containing vague definitions and missing or confusing values. In either setting, the source data needs to be reprogrammed to remedy this, followed by extensive quality control to capture any errors or inconsistencies produced along the way. The quality control is a lengthy task which is often performed manually, either by the scientists conducting the pharmacometric study or by independent reviewers. This project presents an automatic data quality control with the purpose of aiding the data curation process, as to minimize any potential errors that would otherwise have to be detected by the manual quality control. The automatic quality control is implemented as an R-package and is specifically tailored for the needs of Pharmetheus.