A computer simulation of medical decision strategy performance

This study demonstrates a computer model which can be used to compare the effects of errors in probability and utility estimation on the performance of Bayesian and alternative medical decision strategies. The model task requires choosing one of three treatments for a patient with one of three diseases based on the patient's state with respect to five binary cues and estimates of the prior probabilities of disease, the conditional probabilities of the cues and the utilities of the treatments. A classic decision analytic strategy uses Bayes' formula to calculate posterior probabilities of disease and chooses treatments based on maximization of expected value. Alternative strategies use likelihood ratios to calculate disease scores for each patient state and choose the treatment with highest payoff for the disease with the highest score. Two strategies with different cutoffs for the ratios are compared with a random strategy and a classic decision analytic strategy. The simulation results show that the payoffs for all strategies except the random strategy decline with increasing estimation error. The decision analytic strategy has the highest mean payoff at all levels of error. The differences between this optical strategy and the alternatives, however, decrease as estimation error increases, and the frequency with which the strategies based on simple diagnostic scoring rules outperform the formal Bayesian strategy increases.