Biomedical subjects

J W Ulvila

Publications and source records attributed to J W Ulvila.

2 recordsLinked to original sources

PubMed2001

Bayesian cost-effectiveness analysis. An example using the GUSTO trial.

A desirable element of cost-effectiveness analysis (CEA) modeling is a systematic way to relate uncertainty about input parameters to uncertainty in the computational results of the CEA model. Use of Bayesian statistical estimation and Monte Carlo simulation provides a natural way to compute a posterior probability distribution for each CEA result. We demonstrate this approach by reanalyzing a previously published CEA evaluating the incremental cost-effectiveness of tissue plasminogen activator compared to streptokinase for thrombolysis in acute myocardial infarction patients using data from the GUSTO trial and other auxiliary data sources. We illustrate Bayesian estimation for proportions, mean costs, and mean quality-of-life weights. The computations are performed using the Bayesian analysis software WinBUGS, distributed by the MRC Biostatistics Unit, Cambridge, England.

Bayes Theorem↗

PubMedDate not supplied

Decision analysis comes of age.

Ten years ago, decision analysis was still an experimental management technique. But even then supporters claimed that eventually it would become for the manager what calculus is to the engineer. According to the authors of this article, decision analysis that incorporates personal judgment has not yet become what some expected, but it has, nonetheless, gained acceptance in many large corporations and government departments. One of the reasons for this acceptance is the greater flexibility and sensitivity of decision analysis to managers' needs than earlier forms had. In other words, today's decision analysis techniques can better take into account the people, the politics, the time pressures, and all the messy but critical factors that managers have to contend with. In this article, the authors describe three major forms of decision analysis and show how real managers have used decision tree analysis, probabilistic forecasting, and multiattribute analysis to solve real business problems.

Decision Making↗