DESCRIPTION (Adapted from the applicant's abstract): The statistical
framework used in medical decision making is based on the theory of errors
developed 200 years ago by Legendre, Gauss and Laplace. This framework was
developed to combine observations with experimental or observational errors in
astronomy, geodesy and physics. The costs of errors were assumed to be
symmetric and quadratic. In medical decision making, costs of false negative
errors are quite different from the costs of false positive errors. Some
errors have no consequence for the final decision. Another problem with many
traditional statistical methods is that parameter estimates are developed to
minimize the estimation errors and not the prediction errors for a specific
class of decision problems. In this project, a new approach to medical
decision making is proposed. When the number of feasible medical conditions
is m, with one least cost treatment for each, the total number of correct
decision is m and the maximum number of errors is m(m-1). Parameter estimates
and threshold probabilities will be developed jointly to minimize the total
cost of errors and correct decisions. The popular estimation methods such as
the least squares and the maximum likelihood are special cases in this
approach. The estimates will in general be biased, but will minimize the
costs of treatment. It is similar to using weights in an estimation procedure
with a crucial difference: the weights here are not exogenously specified,
but endogenously determined and sensitive to the decision context. The
efficiency of the new approach will be studied using Monte Carlo techniques as
well as real data relating to two medical conditions, brain cancer and
diabetes. According to preliminary studies, this approach leads to a cost
reduction of 6.4% when compared with the logistic regression models. While
these methods can be used to determine the least cost decision rules, they can
even be used to diagnose the diagnostician's own behavior using actual cost to
point out the areas for improvement such as sensitivity and specificity.
Algorithms to compute the decision rules will be developed for frequently used
If you are accessing this page during weekend or evening hours, the database may currently be offline for maintenance and should operational within a few hours. Otherwise, we have been notified of this error and will be addressing it immediately.
Please contact us
if this error persists.
We apologize for the inconvenience.
- The DCCPS Team.