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Comparing Logit and Hinge Surrogate Loss Functions in Outcome Weighted Learning

Eisner, Mariah Claire
http://orcid.org/0000-0002-4703-4342
http://rave.ohiolink.edu/etdc/view?acc_num=osu1585657996755039

Abstract Details

2020, Master of Science, Ohio State University, Public Health.
Outcome weighted learning is a weighted classification-based approach for finding the optimal individualized treatment regime to prolong survival when subject characteristics impact response to different treatment options. Previous research on this method utilizes the hinge loss from machine learning to perform classification. However, there are other loss functions for binary classification that could be leveraged, such as the logit loss from logistic regression. This study compares the performance of outcome weighted learning models via simulations with different surrogate loss functions to determine whether the logit loss is a reasonable alternative to the hinge loss. Data are right censored with two possible treatments and decision functions are assumed to be linear. Simulations are conducted under three forms of the true decision function, using a correctly specified model with two covariates and an incorrectly specified model with an extra nuisance covariate. Logit loss and hinge loss outcome weighted learning models are applied to data from a randomized trial on aortic stenosis. Results indicate that the logit loss offers comparable performance to the hinge loss and therefore can be used as an alternative to the latter, though the performance of both outcome weighted learning models may suffer from instability when they are misspecified.
Andy Ni, PhD (Advisor)
Bo Lu, PhD (Committee Member)
49 p.
Biostatistics
outcome weighted learning; surrogate loss function; hinge loss; logit loss

Recommended Citations

Citations

  • Eisner, M. C. (2020). Comparing Logit and Hinge Surrogate Loss Functions in Outcome Weighted Learning [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1585657996755039

    APA Style (7th edition)

  • Eisner, Mariah. Comparing Logit and Hinge Surrogate Loss Functions in Outcome Weighted Learning. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1585657996755039.

    MLA Style (8th edition)

  • Eisner, Mariah. "Comparing Logit and Hinge Surrogate Loss Functions in Outcome Weighted Learning." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1585657996755039

    Chicago Manual of Style (17th edition)

osu1585657996755039
414
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This open access ETD is published by The Ohio State University and OhioLINK.