We evaluated the implications of different approaches to characterize
uncertainty of calibrated parameters of stochastic decision models (DMs) in the
quantified value of such uncertainty in decision making. We used a
microsimulation DM of colorectal cancer (CRC) screening to conduct a
cost-effectiveness analysis (CEA) of a 10-year colonoscopy screening. We
calibrated the natural history model of CRC to epidemiological data with
different degrees of uncertainty and obtained the joint posterior distribution
of the parameters using a Bayesian approach. We conducted a probabilistic
sensitivity analysis (PSA) on all the model parameters with different
characterizations of uncertainty of the calibrated parameters and estimated the
value of uncertainty of the different characterizations with a value of
information analysis. All analyses were conducted using high performance
computing resources running the Extreme-scale Model Exploration with Swift
(EMEWS) framework. The posterior distribution had high correlation among some
parameters. The parameters of the Weibull hazard function for the age of onset
of adenomas had the highest posterior correlation of -0.958. Considering full
posterior distributions and the maximum-a-posteriori estimate of the calibrated
parameters, there is little difference on the spread of the distribution of the
CEA outcomes with a similar expected value of perfect information (EVPI) of
\$653 and \$685, respectively, at a WTP of \$66,000/QALY. Ignoring correlation
on the posterior distribution of the calibrated parameters, produced the widest
distribution of CEA outcomes and the highest EVPI of \$809 at the same WTP.
Different characterizations of uncertainty of calibrated parameters have
implications on the expect value of reducing uncertainty on the CEA. Ignoring
inherent correlation among calibrated parameters on a PSA overestimates the
value of uncertainty.Comment: 17 pages, 6 figures, 3 table