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
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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.
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