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Activity Number: 137 - Joint Modeling for Longitudinal and Survival Outcomes in Health Studies
Type: Topic Contributed
Date/Time: Monday, August 8, 2022 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #320848
Title: Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risks Data, with Applications to Massive Biobank Data
Author(s): Shanpeng Li* and Gang Li and Ning Li and Hong Wang and Jin Zhou and Hua Zhou
Companies: UCLA and University of California, Los Angeles and UCLA and Central South University and UCLA and UCLA
Keywords: Competing risks; Longitudinal data; Massive sample size; Nonignorable missing data; Semiparametric joint model

Semiparametric joint models of longitudinal and competing risks data are computationally costly and their current implementations do not scale well to massive biobank data. This paper identifies and addresses some key computational barriers in a semiparametric joint model for longitudinal and competing risks survival data. By developing and implementing customized linear scan algorithms, we reduce the computational complexities from $O(n^2)$ or $O(n^3)$ to $O(n)$ in various steps including numerical integration, risk set calculation, and standard error estimation, where $n$ is the number of subjects. Using both simulated and real world biobank data, we demonstrate that these linear scan algorithms can speed up the existing methods by a factor of up to hundreds of thousands when $n>10^4$, often reducing the runtime from days to minutes. We have developed an R-package, FastJM, based on the proposed algorithms for joint modeling of longitudinal and competing risks time-to-event data and made it publicly available on on the Comprehensive R Archive Network (CRAN) at \url{}.

Authors who are presenting talks have a * after their name.

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