(11 Jan) The paper I mentioned in class (an application of Bayesian
decision theory in health policy) is available for downloading here . The citation is
Draper D, Fouskakis D (2000). A case study of stochastic optimization in
health policy: problem formulation and preliminary results. Journal of
Global Optimization, 18, 399-416.
The abstract for this paper is as follows.
We use Bayesian decision theory to address a variable
selection problem arising in attempts to indirectly measure the quality of
hospital care, by comparing observed mortality rates to expected values
based on patient sickness at admission. Our method weighs data collection
costs against predictive accuracy to find an optimal subset of the
available admission sickness variables. The approach involves maximizing
expected utility across possible subsets, using Monte Carlo methods based
on random division of the available data into N modeling and
validation splits to approximate the expectation. After exploring the
geometry of the solution space, we compare a variety of stochastic
optimization methods - including genetic algorithms (GA), simulated
annealing (SA), threshold acceptance (TA), messy simulated annealing
(MSA), and tabu search (TS) - on their performance in finding good subsets
of variables, and we clarify the role of N in the optimization.
Preliminary results indicate that TS is somewhat better than TA and SA in
this problem, with MSA and GA well behind the other three methods.
Sensitivity analysis reveals broad stability of our conclusions.
