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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Sparse estimation for case-control studies with multiple disease subtypes.

Nadim Ballout1, Cedric Garcia2, Vivian Viallon3

  • 1IFSTTAR, TS2, UMRESTTE, Université Claude Bernard Lyon 1, 25, avenue François Mitterrand, Case24, Cité des mobilités, 69675 Bron Cedex, France.

Biostatistics (Oxford, England)
|January 25, 2020
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Summary

Analyzing cancer epidemiology data, this study introduces data shared lasso for stratified regression models. It improves estimation and prediction accuracy by accounting for disease subtype homogeneity in matched and unmatched case-control studies.

Keywords:
Conditional logistic regressionLassoMultinomial logistic regressionSparsityStructured sparsity

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Case-control studies with multiple disease subtypes are common in cancer epidemiology.
  • Matched and unmatched designs require different analytical strategies.

Purpose of the Study:

  • To adapt data shared lasso for stratified regression models in matched designs.
  • To compare penalized multinomial logistic regression methods for unmatched designs.
  • To provide practical guidance for analyzing disease subtypes.

Main Methods:

  • Stratified conditional logistic regression for matched designs.
  • Data shared lasso adaptation for homogeneity.
  • $L_1$-norm penalized multinomial logistic regression for unmatched designs.

Main Results:

  • Data shared lasso improves estimation and prediction accuracy by accounting for subtype homogeneity.
  • The symmetric formulation of multinomial logistic regression reduces to a data shared lasso version of the non-symmetric one.
  • Non-symmetric formulations are not recommended for moderate to high homogeneity.

Conclusions:

  • Accounting for disease subtype homogeneity is crucial for accurate analysis in both matched and unmatched designs.
  • The study provides valuable insights for identifying metabolites associated with breast cancer subtypes using the EPIC cohort data.