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Effect sizes can be calculated for studies reporting ranges for outcome variables in systematic reviews
1Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Ontario, Canada. walter@mcmaster.ca
Journal of Clinical Epidemiology
|July 4, 2007
Summary
This study introduces a method to convert outcome ranges into standard deviations (SDs), enabling the inclusion of diverse study data in systematic reviews. This approach enhances data synthesis for more comprehensive research analysis.
Area of Science:
- Biostatistics
- Evidence Synthesis
- Medical Research Methodology
Background:
- Systematic reviews often exclude studies reporting outcome variability solely as ranges.
- Standard deviations (SDs) are the conventional metric for outcome variability in meta-analyses.
- A gap exists in integrating studies with range-based outcome data into systematic reviews.
Purpose of the Study:
- To develop a method for converting reported outcome ranges into equivalent standard deviations (SDs).
- To facilitate the inclusion of studies reporting ranges in systematic reviews alongside those reporting SDs.
- To enhance the scope and comprehensiveness of systematic review data.
Main Methods:
- A numerical conversion approach is proposed to transform range data into an equivalent SD.
- The method involves estimating the SD from the observed range multiplied by a conversion factor.
- Calculations are demonstrated using two distinct clinical examples.
Main Results:
- The proposed method provides a straightforward way to estimate SDs from reported ranges.
- Two case studies illustrate the practical application of the conversion technique.
- The conversion allows studies with range data to be incorporated into meta-analyses.
Conclusions:
- A simple method is presented to convert outcome variability reported as ranges into equivalent SDs.
- This facilitates the inclusion of a wider range of studies in systematic reviews.
- The method is valid for normally distributed data, with potential need for caution with non-normal data.
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