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Re-design and improvement of animal experiments, using Bayesian methods

Spiliopoulos, Lampros LU (2019) In Master's Theses in Mathematical Sciences MASM01 20191
Mathematical Statistics
Abstract
The pharmaceutical industry uses animal models in a variety of settings
including safety, pharmacodynamic modelling and efficacy. Conventionally,
a frequentist design of standard animal experiments of human asthma replication
includes repeatedly running treatment groups with the exact layout, focusing on
effect size rather than capturing historical data of animal responses on compounds
of interest. Here, we propose a Bayesian framework which is used as an alternative
to the frequentist approach. This allows for the incorporation of prior beliefs into
the experimental process. Specifically, non-informative, semi-informative and informative
prior distributions are assigned to Single-Level Normal models. Given the
priors... (More)
The pharmaceutical industry uses animal models in a variety of settings
including safety, pharmacodynamic modelling and efficacy. Conventionally,
a frequentist design of standard animal experiments of human asthma replication
includes repeatedly running treatment groups with the exact layout, focusing on
effect size rather than capturing historical data of animal responses on compounds
of interest. Here, we propose a Bayesian framework which is used as an alternative
to the frequentist approach. This allows for the incorporation of prior beliefs into
the experimental process. Specifically, non-informative, semi-informative and informative
prior distributions are assigned to Single-Level Normal models. Given the
priors aforementioned, it was found that using semi-informative priors leads to the
creation of consistent historical data. Given these beliefs, we combine the results
of all experiments by implementing a Two-Level Bayesian Meta-Analysis, achieved
by adding the extra level of experimental studies to the data structure. The end
point of this project showed that simulation trials of all experimental studies with
the assignment of semi-informative priors can result in the reduction of animals per
treatment group by a margin of 10 %. (Less)
Please use this url to cite or link to this publication:
author
Spiliopoulos, Lampros LU
supervisor
organization
course
MASM01 20191
year
type
H2 - Master's Degree (Two Years)
subject
publication/series
Master's Theses in Mathematical Sciences
report number
LUNFMS-3082-2019
ISSN
1404-6342
other publication id
2019:E32
language
English
id
8987595
date added to LUP
2020-10-05 14:32:49
date last changed
2024-09-30 12:24:07
@misc{8987595,
  abstract     = {{The pharmaceutical industry uses animal models in a variety of settings
including safety, pharmacodynamic modelling and efficacy. Conventionally,
a frequentist design of standard animal experiments of human asthma replication
includes repeatedly running treatment groups with the exact layout, focusing on
effect size rather than capturing historical data of animal responses on compounds
of interest. Here, we propose a Bayesian framework which is used as an alternative
to the frequentist approach. This allows for the incorporation of prior beliefs into
the experimental process. Specifically, non-informative, semi-informative and informative
prior distributions are assigned to Single-Level Normal models. Given the
priors aforementioned, it was found that using semi-informative priors leads to the
creation of consistent historical data. Given these beliefs, we combine the results
of all experiments by implementing a Two-Level Bayesian Meta-Analysis, achieved
by adding the extra level of experimental studies to the data structure. The end
point of this project showed that simulation trials of all experimental studies with
the assignment of semi-informative priors can result in the reduction of animals per
treatment group by a margin of 10 %.}},
  author       = {{Spiliopoulos, Lampros}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Re-design and improvement of animal experiments, using Bayesian methods}},
  year         = {{2019}},
}