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• W4383873419 abstract "Abstract Existing methods for fitting continuous time Markov models (CTMM) in the presence of covariates suffer from scalability issues due to high computational cost of matrix exponentials calculated for each observation. In this article, we propose an optimization technique for CTMM which uses a stochastic gradient descent algorithm combined with differentiation of the matrix exponential using a Padé approximation. This approach makes fitting large scale data feasible. We present two methods for computing standard errors, one novel approach using the Padé expansion and the other using power series expansion of the matrix exponential. Through simulations, we find improved performance relative to existing CTMM methods, and we demonstrate the method on the large-scale multiple sclerosis NO.MS data set." @default.
• W4383873419 created "2023-07-12" @default.
• W4383873419 creator A5042704365 @default.
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• W4383873419 date "2023-07-11" @default.
• W4383873419 modified "2023-09-25" @default.
• W4383873419 title "A scalable approach for continuous time Markov models with covariates" @default.
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• W4383873419 doi "https://doi.org/10.1093/biostatistics/kxad012" @default.
• W4383873419 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37433567" @default.
• W4383873419 hasPublicationYear "2023" @default.
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