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Effect estimates can be accurately calculated with data digitally extracted from interrupted time series graphs
Simon Lee Turner1, Elizabeth Korevaar1, Miranda S Cumpston1
1School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
Research Synthesis Methods
|June 9, 2023
Summary
Digital data extraction from interrupted time series (ITS) graphs is a viable method for meta-analysis. Despite minor extraction errors, effect estimates remain accurate, supporting the inclusion of ITS studies in reviews.
Area of Science:
- Epidemiology
- Biostatistics
Background:
- Interrupted time series (ITS) studies are crucial for evaluating population-level interventions.
- Systematic reviews and meta-analyses often require re-analysis of ITS data, but raw data is rarely available.
- Graphs from ITS publications offer a potential source for data extraction.
Approach:
- Extracted time series data from 43 ITS graphs using digital software.
- Analyzed data extraction errors and compared segmented linear regression models fitted to extracted versus original datasets.
- Assessed the accuracy of effect estimates (immediate level and slope change) derived from extracted data.
Key Points:
- Data extraction errors from ITS graphs were identified, particularly concerning time points.
- These errors did not significantly impact the accuracy of effect estimates or associated statistics.
- Digital data extraction from ITS graphs is a reliable method for obtaining data for meta-analysis.
Conclusions:
- Digital data extraction from ITS graphs is a feasible and recommended approach for systematic reviews and meta-analyses.
- Including studies with digitally extracted data, despite minor inaccuracies, is preferable to excluding them.
- This method enhances the evidence base for public health and policy decision-making.
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