100 research outputs found
Bayesian inference for stochastic differential equation mixed effects models of a tumor xenography study
We consider Bayesian inference for stochastic differential equation mixed
effects models (SDEMEMs) exemplifying tumor response to treatment and regrowth
in mice. We produce an extensive study on how a SDEMEM can be fitted using both
exact inference based on pseudo-marginal MCMC and approximate inference via
Bayesian synthetic likelihoods (BSL). We investigate a two-compartments SDEMEM,
these corresponding to the fractions of tumor cells killed by and survived to a
treatment, respectively. Case study data considers a tumor xenography study
with two treatment groups and one control, each containing 5-8 mice. Results
from the case study and from simulations indicate that the SDEMEM is able to
reproduce the observed growth patterns and that BSL is a robust tool for
inference in SDEMEMs. Finally, we compare the fit of the SDEMEM to a similar
ordinary differential equation model. Due to small sample sizes, strong prior
information is needed to identify all model parameters in the SDEMEM and it
cannot be determined which of the two models is the better in terms of
predicting tumor growth curves. In a simulation study we find that with a
sample of 17 mice per group BSL is able to identify all model parameters and
distinguish treatment groups.Comment: Minor revision: posterior predictive checks for BSL have ben updated
(both theory and results). Code on GitHub has ben revised accordingl
Accelerating delayed-acceptance Markov chain Monte Carlo algorithms
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a
probability distribution via a two-stages version of the Metropolis-Hastings
algorithm, by combining the target distribution with a "surrogate" (i.e. an
approximate and computationally cheaper version) of said distribution. DA-MCMC
accelerates MCMC sampling in complex applications, while still targeting the
exact distribution. We design a computationally faster, albeit approximate,
DA-MCMC algorithm. We consider parameter inference in a Bayesian setting where
a surrogate likelihood function is introduced in the delayed-acceptance scheme.
When the evaluation of the likelihood function is computationally intensive,
our scheme produces a 2-4 times speed-up, compared to standard DA-MCMC.
However, the acceleration is highly problem dependent. Inference results for
the standard delayed-acceptance algorithm and our approximated version are
similar, indicating that our algorithm can return reliable Bayesian inference.
As a computationally intensive case study, we introduce a novel stochastic
differential equation model for protein folding data.Comment: 40 pages, 21 figures, 10 table
Evaluation of Peak Reflux Velocities with Vector Flow Imaging and Spectral Doppler Ultrasound in Varicose Veins
Prolonged perceived stress and saliva cortisol in a large cohort of Danish public service employees:cross-sectional and longitudinal associations
Twelve weeks of treatment with empagliflozin in patients with heart failure and reduced ejection fraction:A double-blinded, randomized, and placebo-controlled trial
Vector and Doppler Ultrasound Velocities Evaluated in a Flow Phantom and the Femoropopliteal Vein
Women Born Preterm or with Inappropriate Weight for Gestational Age Are at Risk of Subsequent Gestational Diabetes and Pre-Eclampsia
Introduction: Low birthweight, which can be caused by inappropriate intrauterine growth or prematurity, is associated with development of gestational diabetes mellitus (GDM) as well as pre-eclampsia later in life, but the relative effects of prematurity and inappropriate intrauterine growth remain uncertain. Methods: Through nation-wide registries we identified all Danish mothers in the years 1989–2007. Two separate cohorts consisting mothers born 1974–1977 (n = 84219) and 1978–1981 (n = 32376) were studied, due to different methods o
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