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Comparative optimism in models involving both classical clinical and gene expression information
Caroline Truntzer1, Delphine Maucort-Boulch, Pascal Roy
1Hospices Civils de Lyon, Service de Biostatistique, Lyon, France. caroline.truntzer@clipproteomic.fr
BMC Bioinformatics
|October 16, 2008
Summary
Transcriptomic models in cancer prognosis may overestimate gene significance due to selection bias. Clinical variables offer more reliable predictions, as their established relevance avoids this overestimation.
Area of Science:
- Oncology
- Bioinformatics
- Biostatistics
Background:
- Established clinical variables are crucial in cancer research.
- Transcriptomic variables present challenges in selection and validation.
- Current transcriptomic models may overestimate prognostic contributions.
Purpose of the Study:
- To assess optimism in transcriptomic models for cancer prognosis.
- To quantify overestimation of transcriptomic variables' contribution to survival.
Main Methods:
- Cox proportional hazards models incorporating clinical and transcriptomic data.
- Univariate and multivariate gene selection methods.
- Simulations and Kent and O'Quigley rho2 for optimism assessment.
Main Results:
- Clinical variables showed low optimism as they were not selected.
- Gene selection introduced significant optimism, increasing with fewer genes of interest.
- Increased sample size can reduce optimism in gene-based models.
Conclusions:
- Gene selection inflates predictive power, while omitting relevant clinical genes underestimates their value.
- Validated clinical variables provide reliable, non-overestimated prognostic predictions.
- Future cancer prognosis studies should consider both clinical and transcriptomic variable selection biases.
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