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Model validation and selection for personalized medicine using dynamic-weighted ordinary least squares.
Michael P Wallace1, Erica Em Moodie1, David A Stephens2
11 Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
Statistical Methods in Medical Research
|May 11, 2017
Summary
This study introduces novel methods for assessing statistical models in dynamic treatment regimes. We leverage double-robustness for model assessment and quasilikelihood for selection in optimal treatment strategies.
Area of Science:
- Statistics
- Biostatistics
- Machine Learning
Background:
- Model assessment is crucial in statistical analysis but under-explored in dynamic treatment regimes.
- Optimal dynamic treatment regimes require robust model evaluation techniques.
Purpose of the Study:
- To introduce and evaluate methods for model assessment and selection within dynamic treatment regimes.
- To leverage the double-robustness property of dynamic-weighted ordinary least squares for model assessment.
- To apply quasilikelihood for model selection in this context.
Main Methods:
- Utilized the dynamic-weighted ordinary least squares (DWOLS) approach for optimal dynamic treatment regime estimation.
- Applied the double-robustness property of DWOLS for model assessment.
- Employed quasilikelihood methods for model selection.
Main Results:
- Demonstrated the utility of double-robustness for assessing models in dynamic treatment regimes.
- Showcased the effectiveness of quasilikelihood for model selection.
- Validated the proposed methods through simulation studies and real-world data.
Conclusions:
- The proposed methods enhance the reliability of dynamic treatment regime estimation.
- Double-robustness and quasilikelihood offer powerful tools for model assessment and selection in this field.
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