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Practical methods for incorporating summary time-to-event data into meta-analysis: updated guidance
Jayne F Tierney1, Sarah Burdett2, David J Fisher2
1MRC Clinical Trials Unit, Medical Research Council Clinical Trials Unit, University College London, London, UK. jayne.tierney@ucl.ac.uk.
Systematic Reviews
|April 11, 2025
Summary
This updated guide clarifies methods for estimating hazard ratios (HRs) from published time-to-event data. It includes new scenarios and a spreadsheet tool for meta-analysis, aiding researchers using aggregate data.
Area of Science:
- Biostatistics
- Medical Research Methodology
Background:
- Estimating hazard ratios (HRs) from aggregate data is crucial for meta-analyses.
- Previous guidance on this topic requires updates due to user difficulties and evolving methods.
Purpose of the Study:
- To comprehensively update guidance on estimating HRs and logrank variance (V) from published time-to-event data.
- To provide enhanced tools and address challenges in extracting and utilizing data from publications and Kaplan-Meier (KM) curves.
Main Methods:
- Incorporation of previous scenarios for deriving HR and logrank variance.
- Inclusion of additional scenarios, clarification of ambiguities, and guidance on data extraction from publications and KM curves.
- Development of a new, user-friendly calculations spreadsheet for various data inputs.
Main Results:
- The updated guidance offers comprehensive clarification and additional scenarios for HR estimation.
- New methods and alternatives to existing Kaplan-Meier (KM) approaches are discussed.
- A versatile calculation spreadsheet is provided to assist users.
Conclusions:
- This updated guidance and accompanying spreadsheet are valuable resources for meta-analyses using published summary time-to-event data.
- The tools aim to improve the accuracy and ease of estimating hazard ratios from aggregate data.
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