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The CREATE Method for Expressing Continuous Outcome Data in Absolute Terms for Use in Patient Treatment Decision
Michael McGillion1, J Charles Victor2, Sandra L Carroll1
1Faculty of Health Sciences, McMaster University, ON, Canada, (MM, SLC, HMA)
Summary
The Conversion to Risk Estimates through Application of Normal Theory (CREATE) method accurately estimates absolute risk for continuous outcomes in patient decision aids. However, it should not be used for highly skewed or kurtotic data distributions.
Area of Science:
- Health Services Research
- Biostatistics
- Medical Decision Making
Background:
- Patient decision aids (PtDAs) traditionally present risks/benefits as binary outcomes.
- Continuous outcome data has posed a challenge for existing PtDA risk communication methods.
Purpose of the Study:
- To develop and validate the Conversion to Risk Estimates through Application of Normal Theory (CREATE) method.
- To enable the estimation of absolute risk from continuous outcome data for PtDAs.
Main Methods:
- A two-stage validation process was employed using real and simulated data.
- Real data from published trials compared actual and CREATE-derived estimates of clinically relevant degree of change (CRDoC).
- Simulated data (200,000 distributions) assessed CREATE's performance across varying distribution characteristics.
Main Results:
- The CREATE method demonstrated high accuracy, with absolute differences not exceeding 5% for real data.
- Simulations revealed that CREATE is unsuitable for outcome data with high skew or kurtosis.
- The CREATE method is generally valid for estimating absolute risk from continuous outcomes.
Conclusions:
- Standard statistical theory can accurately estimate continuous outcomes in absolute terms for PtDAs.
- Caution is advised when using the CREATE method if outcome distributions are highly skewed.
- The CREATE method offers a valuable tool for enhancing risk communication in shared decision-making.
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