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Semiparametric joint models of longitudinal and competing risks data arecomputationally costly and their current implementations do not scale well tomassive biobank data. This paper identifies and addresses some keycomputational barriers in a semiparametric joint model for longitudinal andcompeting risks survival data. By developing and implementing customized linearscan algorithms, we reduce the computational complexities from $O(n^2)$ or$O(n^3)$ to $O(n)$ in various components including numerical integration, riskset calculation, and standard error estimation, where $n$ is the number ofsubjects. Using both simulated and real world biobank data, we demonstrate thatthese linear scan algorithms generate drastic speed-up of up to hundreds ofthousands fold when $n>10^4$, sometimes reducing the run-time from days tominutes. We have developed an R-package, FastJM, based on the proposedalgorithms for joint modeling of longitudinal and time-to-event data with andwithout competing risks, and made it publicly available on the Comprehensive RArchive Network (CRAN).

computational and mathematical methods in medicine

Efficient Algorithms and Implementation of a Semiparametric Joint Model  for Longitudinal and Competing Risks Data: With Applications to Massive  Biobank Data