10.15131/shef.data.11890641.v1
James Chilcott
Matthew Mildred
Silvia Hummel
Use of the Metropolis-Hastings algorithm in the calibration of a patient level simulation of prostate cancer screening
2020
The University of Sheffield
Metropolis-Hastings algorithm
patient level simulation
prostate cancer
screening
screening programmes
calibration
2020-02-25 10:26:32
article
https://figshare.shef.ac.uk/articles/poster/Use_of_the_Metropolis-Hastings_algorithm_in_the_calibration_of_a_patient_level_simulation_of_prostate_cancer_screening/11890641
Introduction
• 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.