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A Comparison of Two Strategies for Building an Exposure Prediction Model.
Marina Heiden1, Svend Erik Mathiassen2, Jennifer Garza3
11.Centre for Musculoskeletal Research, Department of Occupational and Public Health Sciences, University of Gävle, 801 76 Gävle, Sweden; marina.heiden@hig.se.
The Annals of Occupational Hygiene
|October 2, 2015
Summary
Predicting job exposures using accessible data requires careful predictor selection. Both stepwise testing and cluster analysis showed similar, poor predictive performance when validated internally, highlighting challenges in cost-efficient exposure assessment.
Area of Science:
- Occupational health
- Epidemiology
- Biostatistics
Background:
- Accurate job exposure assessment is crucial for large populations.
- Cost-efficient methods are needed, often relying on predictive models.
- Model development requires selecting optimal predictors from extensive candidate data.
Purpose of the Study:
- To compare two predictor selection strategies for job exposure modeling.
- To evaluate stepwise hypothesis testing against cluster analysis.
- To assess the impact of predictor selection on model generalizability.
Main Methods:
- Applied stepwise hypothesis testing and cluster analysis to a dataset on biomechanical exposure.
- Used questionnaire and company data as candidate predictors.
- Employed bootstrap resampling for internal validation, including predictor selection in validation for one strategy.
Main Results:
- Identified largely different predictors between the two strategies.
- Stepwise testing initially yielded better model fit.
- Internal validation revealed similar, reduced model fit for both strategies, especially when predictor selection was part of validation.
Conclusions:
- Both stepwise testing and cluster analysis demonstrated limitations in predicting biomechanical exposure.
- Predictor selection strategies significantly impact model validation and generalizability.
- Further research is needed to improve cost-efficient exposure assessment models.
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