Besides the original planned research questions around the effectiveness of the intervention, I realised that so much more could be learned using data-driven approaches. Follow-up questions could address the mechanisms behind the limitations to the intervention and inform the design of more successful future interventions, if not by the team that designed the intervention and collected the data, then by others with relevant expertise.
The scale of the data from the hundreds of asia rcs data participants that took part in that cognitive training study pales in comparison to the scale of the administrative datasets that the MRC Pathfinder projects aimed to integrate and analyse.
The public interest research possibilities offered by de-identified linked administrative records, such as collected in health, education or social care settings, are infinite – such research can investigate dynamic influences on real-world outcomes in the relevant clinical or population samples.
But during my first Pathfinder task, I soon discovered that applying to link administrative health, education and social care data faced even greater challenges, such as whether the pseudonyms in the linked data can be traced back to real IDs and which party will link the pseudonyms from the different datasets.