Design and methodological characteristics of studies using observational routinely collected health data for investigating the link between cancer and neurodegenerative diseases: protocol for a meta-research study

Introduction Health services generate large amounts of routine health data (eg, administrative databases, disease registries and electronic health records), which have important secondary uses for research. Increases in the availability and the ability to access and analyse large amounts of data represent a major opportunity for conducting studies on the possible relationships between complex diseases. The objective of this study will be to evaluate the design, methods and reporting of studies conducted using observational routinely collected health data for investigating the link between cancer and neurodegenerative diseases. Methods and analysis This is the protocol for a meta-research study. We registered the study protocol within the Open Science Framework: https://ostio/h2qjg. We will evaluate observational studies (eg, cohort and case-control) conducted using routinely collected health data for investigating the associations between cancer and neurodegenerative diseases (such as Alzheimer's disease, amyotrophic lateral sclerosis/motor neuron disease, Huntington's disease, multiple sclerosis and Parkinson's disease). The following electronic databases will be searched (from their inception onwards): MEDLINE, Embase and Web of Science Core Collection. Screening and selection of articles will be conducted by at least two researchers. Potential discrepancies will be resolved via discussion. Design, methods and reporting characteristics in each article will be extracted using a standardised data extraction form. Information on general, methodological and transparency items will be reported. We will summarise our findings with tables and graphs (eg, bar charts, forest plots). Ethics and dissemination Due to the nature of the proposed study, no ethical approval will be required. We plan to publish the full study in an open access peer-reviewed journal and disseminate the findings at scientific conferences and via social media. All data will be deposited in a cross-disciplinary public repository.