Initial Data Analysis for Cancer Registries: A Structured Framework and Demonstration Using Slovenian Cancer Registry
Maja Jurtela1,2, Lara Lusa3,4, Tina Žagar1
1Slovenian Cancer Registry, Institute of Oncology Ljubljana, 1000 Ljubljana, Slovenia.
Cancers
|July 28, 2026
Summary
This study introduces a new Initial Data Analysis (IDA) framework specifically designed for cancer registries (CRs). The framework enhances data transparency and reproducibility for researchers using CR data.
Area of Science:
- Biostatistics
- Data Management
- Cancer Epidemiology
Background:
- Existing Initial Data Analysis (IDA) frameworks are not optimized for the dynamic nature of cancer registries (CRs).
- CRs present unique challenges due to continuous updates, multiple data uses, and evolving classification systems.
- A tailored IDA framework is needed to ensure valid and reproducible statistical analyses from CR data.
Purpose of the Study:
- To develop and present a structured Initial Data Analysis (IDA) framework specifically adapted for cancer registries (CRs).
- To address the limitations of existing IDA frameworks in the context of complex CR data environments.
- To enhance the transparency and reproducibility of data preparation and analysis for CR datasets.
Main Methods:
- Conceptualized IDA across three data states: operational registry, extracted, and analysis-ready datasets.
- Adapted existing IDA principles into a four-stage framework: metadata, cleaning, screening, and reporting.
- Employed predefined, versioned rule sets, structured data processing records, and metadata linkage for traceability.
Main Results:
- The developed framework outlines specific activities and outputs for structured IDA in CRs.
- Metadata captured dataset scope, intended use, variables, and coding context.
- Implementation demonstrated traceable data cleaning, documented decisions, screening outputs, and a comprehensive IDA report.
Conclusions:
- The proposed framework extends current IDA methodologies to meet the specific demands of cancer registries.
- It facilitates consistent dataset preparation, leading to improved transparency in data utilization.
- The framework ultimately supports more reproducible research using cancer registry data.
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