Use of the Metropolis 2010 CHILCOTT.pdf (1.09 MB)
Download fileUse of the Metropolis-Hastings algorithm in the calibration of a patient level simulation of prostate cancer screening
poster
posted on 2020-02-25, 10:26 authored by James ChilcottJames Chilcott, Matthew Mildred, Silvia HummelIntroduction
• Designing cancer screening programmes
requires an understanding of
epidemiology, disease natural history
and screening test characteristics.
• Many of these aspects of the decision
problem are unobservable and data can
only tell us about their joint uncertainty.
• A Metropolis-Hastings algorithm
was used to calibrate a patient level
simulation model of the natural history
of prostate cancer to national cancer
registry and international trial data.
• This method correctly represents
the joint uncertainty amongst the
model parameters by drawing
efficiently from a high dimensional
correlated parameter space.
• The calibration approach estimates
the probability of developing prostate
cancer, the rate of disease progression
and sensitivity of the screening test.
• This is then used to estimate the impact
of prostate cancer screening in the UK.
• This case study demonstrates that the
Bayesian approach to calibration can be
used to appropriately characterise the
uncertainty alongside computationally
expensive simulation models.
History
Ethics
There is no personal data or any that requires ethical approvalPolicy
The data complies with the institution and funders' policies on access and sharingSharing and access restrictions
The data can be shared openlyData description
- The file formats are open or commonly used
Methodology, headings and units
- Headings and units are explained in the files