Statistical
Methods for Cost-Effectiveness Analyses
Investigators: Carole Siegel, Ph.D., Eugene Laska, Ph.D., Morris Meisner, Ph.D.
Goals
Siegel et
al. describe a statistical framework for examining cost and effect data
on competing interventions obtained from a randomized clinical trial (RCT)
or from an observational study. Parameters of the joint distribution of
costs and effects or a regression function linking costs and effects are
used to define cost-effectiveness (c-e) measures. Several new c-e measures
are proposed that utilize the linkage between costs and effects on the patient
level. These measures reflect perspectives that are different from those
of the commonly used measures, such as the ratio of expected cost to expected
effect, and they can lead to different relative rankings of the interventions.
The cost-effectiveness of interventions are assessed statistically in a
two stage procedure that first eliminates clearly inferior interventions.
Members of the remaining admissible set are then rank ordered according
to a c-e preference measure. Statistical techniques, particularly in the
multivariate normal case, are given for several commonly used c-e measures.
These techniques provide methods for obtaining confidence intervals, for
testing the hypothesis of admissibility and for the equality of interventions,
and for ranking interventions. (Siegel, et al. Statistical Methods
for Cost-Effectiveness Analyses. Controlled Clinical Trials, 1996,
17:387-406)
Computer Programs
A procedure
for testing the equality of the mean cost to mean effectivness ratio for
normally distributed random variables was developed during this project.
The test has been programmed in both SAS and Mathematica. To download
these programs, click on the appropriate selection:
You may also view the program code on screen by selecting from the following:
