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Bayesian inference for stochastic differential equation mixed effects models of a tumour xenography study

Picchini, Umberto LU and Forman, Julie Lyng (2019) In Journal of the Royal Statistical Society. Series C: Applied Statistics 68(4). p.887-913
Abstract
We consider Bayesian inference for stochastic differential equation mixed effects
models (SDEMEMs) exemplifying tumour response to treatment and regrowth in mice. We produce an extensive study on how an SDEMEM can be fitted by using both exact inference based on pseudo-marginal Markov chain Monte Carlo sampling and approximate inference via Bayesian synthetic likelihood (BSL). We investigate a two-compartments SDEMEM, corresponding to the fractions of tumour cells killed by and survived on a treatment. Case-study data
consider a tumour 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 can reproduce the... (More)
We consider Bayesian inference for stochastic differential equation mixed effects
models (SDEMEMs) exemplifying tumour response to treatment and regrowth in mice. We produce an extensive study on how an SDEMEM can be fitted by using both exact inference based on pseudo-marginal Markov chain Monte Carlo sampling and approximate inference via Bayesian synthetic likelihood (BSL). We investigate a two-compartments SDEMEM, corresponding to the fractions of tumour cells killed by and survived on a treatment. Case-study data
consider a tumour 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 can reproduce the observed growth patterns and that BSL is a robust tool for inference in SDEMEMs. Finally, we compare the fit of the SDEMEM with a similar ordinary differential equation
model. Because of 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 tumour growth curves. In a simulation study we find that with a sample of 17 mice per group BSL can identify all model parameters and distinguish treatment groups. (Less)
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author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Intractable likelihood, Pseudo-marginal Markov chain Monte Carlo sampling, Repeated measurements, State space model, Synthetic likelihood
in
Journal of the Royal Statistical Society. Series C: Applied Statistics
volume
68
issue
4
pages
27 pages
publisher
Wiley-Blackwell
external identifiers
  • scopus:85063353705
ISSN
0035-9254
DOI
10.1111/rssc.12347
project
Stochastic modelling of protein folding and likelihood-free statistical inference methods
language
English
LU publication?
yes
id
d3759aa5-9aa6-4043-a72a-36f3d6961dd9
date added to LUP
2019-04-04 14:01:47
date last changed
2023-04-09 07:04:21
@article{d3759aa5-9aa6-4043-a72a-36f3d6961dd9,
  abstract     = {{We consider Bayesian inference for stochastic differential equation mixed effects<br/>models (SDEMEMs) exemplifying tumour response to treatment and regrowth in mice. We produce  an  extensive  study  on  how  an  SDEMEM  can  be  fitted  by  using  both  exact  inference based on pseudo-marginal Markov chain Monte Carlo sampling and approximate inference via Bayesian synthetic likelihood (BSL). We investigate a two-compartments SDEMEM, corresponding to the fractions of tumour cells killed by and survived on a treatment. Case-study data<br/>consider a tumour 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 can reproduce the observed growth patterns and that BSL is a robust tool for inference in SDEMEMs. Finally, we compare the fit of the SDEMEM with a similar ordinary differential equation<br/>model. Because of 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 tumour growth curves. In a simulation study we find that with a sample of 17 mice per group BSL can identify all model parameters and distinguish treatment groups.}},
  author       = {{Picchini, Umberto and Forman, Julie Lyng}},
  issn         = {{0035-9254}},
  keywords     = {{Intractable likelihood; Pseudo-marginal Markov chain Monte Carlo sampling; Repeated measurements; State space model; Synthetic likelihood}},
  language     = {{eng}},
  number       = {{4}},
  pages        = {{887--913}},
  publisher    = {{Wiley-Blackwell}},
  series       = {{Journal of the Royal Statistical Society. Series C: Applied Statistics}},
  title        = {{Bayesian inference for stochastic differential equation mixed effects models of a tumour xenography study}},
  url          = {{http://dx.doi.org/10.1111/rssc.12347}},
  doi          = {{10.1111/rssc.12347}},
  volume       = {{68}},
  year         = {{2019}},
}