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Addressing Confounding in Predictive Models with an Application to Neuroimaging

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Listed:
  • Linn Kristin A.
  • Shinohara Russell T.

    (Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania)

  • Gaonkar Bilwaj
  • Doshi Jimit
  • Davatzikos Christos

    (Department of Radiology, Perelman School of Medicine, University of Pennsylvania)

Abstract

Understanding structural changes in the brain that are caused by a particular disease is a major goal of neuroimaging research. Multivariate pattern analysis (MVPA) comprises a collection of tools that can be used to understand complex disease efxcfects across the brain. We discuss several important issues that must be considered when analyzing data from neuroimaging studies using MVPA. In particular, we focus on the consequences of confounding by non-imaging variables such as age and sex on the results of MVPA. After reviewing current practice to address confounding in neuroimaging studies, we propose an alternative approach based on inverse probability weighting. Although the proposed method is motivated by neuroimaging applications, it is broadly applicable to many problems in machine learning and predictive modeling. We demonstrate the advantages of our approach on simulated and real data examples.

Suggested Citation

  • Linn Kristin A. & Shinohara Russell T. & Gaonkar Bilwaj & Doshi Jimit & Davatzikos Christos, 2016. "Addressing Confounding in Predictive Models with an Application to Neuroimaging," The International Journal of Biostatistics, De Gruyter, vol. 12(1), pages 31-44, May.
    • Handle: RePEc:bpj:ijbist:v:12:y:2016:i:1:p:31-44:n:13
    DOI: 10.1515/ijb-2015-0030
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    References listed on IDEAS

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    1. Reiss, Philip T. & Ogden, R. Todd, 2007. "Functional Principal Component Regression and Functional Partial Least Squares," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 984-996, September.
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