Fusion learning algorithm to combine partially heterogeneous Cox models

Lu Tang (), Ling Zhou () and Peter X. K. Song ()
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Lu Tang: University of Michigan
Ling Zhou: University of Michigan
Peter X. K. Song: University of Michigan

Computational Statistics, 2019, vol. 34, issue 1, No 17, 395-414

Abstract: Abstract We propose a fusion learning procedure to perform regression coefficients clustering in the Cox proportional hazards model when parameters are partially heterogeneous across certain predefined subgroups, such as age groups. One major issue pertains to the fact that the same covariate may have different influence on the survival time across different subgroups. Learning differences in covariate effects is of critical importance to understand the model heterogeneity resulted from the between-group heterogeneity, especially when the number of subgroups is large. We establish a computationally efficient procedure to learn the heterogeneous patterns of regression coefficients across the subgroups in Cox proportional hazards model. Utilizing a fusion learning algorithm coupled with the estimated parameter ordering, the proposed method mitigates greatly computational burden with little loss of statistical power. Extensive simulation studies are conducted to evaluate the performance of our method. Finally with a comparison to some popular conventional methods, we illustrate the proposed method by a vehicle leasing contract renewal analysis.

Keywords: Fused lasso; Regression coefficient clustering; Extended BIC; Cox proportional hazards model (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s00180-018-0827-6

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