This core dataset contains relevant variables that provides guidance for the collection of real-world data (RWD) to develop a relevant non-small cell lung cancer (NSCLC) patient registry and to enhance its fitness-for-purpose data. These RWD are intended to address most regulatory, health technology assessment (HTA), and healthcare questions, thereby supporting the generation of real-world evidence for the more appropriate use of NSCLC drugs.
Potentially relevant COS
Existing COS intended to assess the value of lung cancer drugs to support the structured collection of these data in clinical practice were searched in the COMET database. No COS were found that fully met our scope, nevertheless, two relevant lung cancer COS were included in our study to serve as the foundation of our core dataset. The aim of our core dataset differs from these two COS, since they focus on value-based healthcare across the entire treatment pathway (e.g. diagnostics, time to first treatment) for lung cancer patients, while we focus on drug assessment in NSCLC using registry based RWD.
1) K.S. Mak, et al. Defining a standard set of patient-centred outcomes for lung cancer, European Respiratory Journal 48 (2016) 852–860.
2) B.H. de Rooij, et al. Development of an updated, standardized, patient-centered outcome set for lung cancer, Lung Cancer 173 (2022) 5–13.
Geeske F Grit, Maaike van Dartel, Laura Rodwell, Hanneke van der Woude, Cornelis Boersma, Peter GM Mol (supervisor), Doranne Hilarius (supervisor)
Disease Category: Cancer
Disease Name: Non-small-cell lung cancer (NSCLC)
Age Range: 18 - 120
Sex: Either
Nature of Intervention: Drug
- Clinical experts
- Patient/ support group representatives
- Pharmaceutical industry representatives
- Policy makers
- Regulatory agency representatives
- Researchers
- COS for registry
- Literature review
- Semi structured discussion
The core dataset was developed through a literature review of existing core datasets, followed by two multi-stakeholder expert meetings to provide input for better understanding of important variables.