An elicitation method for multiple linear regression models

Using elicitation techniques to estimate the value of ambulatory treatments for major depression

Estimating the value of spending on medical treatments in a health care system involves relating output, measured in terms of effectiveness, to cost, measured in terms of spending. Although information on spending at the system level often exists in administrative data, such as insurance claims, information on effectiveness is not always available. An inferential tool available to researchers in this context is elicitation. The authors develop an approach to elicit effectiveness parameters and apply it to a panel of 10 experts to estimate predictive Hamilton Depression Rating Scale scores representing postambulatory treatment outcomes. The elicited parameters are used to estimate outcomes associated with 120 acute phase treatments for major depression within a privately insured health insurance system. The outcome-adjusted price per full remission episode is estimated for each acute treatment, and corresponding 95% percentile bootstrap intervals are calculated. The average spending for all observed treatments was $473 (SE = 478), with a depression-free adjusted price per case of $5,995 (95% confidence interval = $5,959-$6,031).