M.G. Myriam Hunink

PURPOSE: To determine the most cost-effective strategy for preoperative imaging performed in potential living renal donors. MATERIALS AND METHODS: In a decision-analytic model, the societal cost-effectiveness of digital subtraction angiography (DSA), gadolinium-enhanced magnetic resonance (MR) angiography, contrast material–enhanced spiral computed tomographic (CT) angiography, and combinations of these imaging techniques was evaluated. Outcome measures included lifetime cost, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios. A base-case analysis was performed with a 40-year-old female donor and a 40-year-old female recipient. RESULTS: For the donor, MR angiography (24.05 QALYs and $9,000) dominated all strategies except for MR angiography with CT angiography, which had an incremental ratio of $245,000 per QALY. For the recipient, DSA and DSA with MR angiography yielded similar results (10.46 QALYs and $179,000) and dominated all other strategies. When results for donor and r...

With current constraints on health care resources and emphasis on value for money, new diagnostic imaging technologies must be assessed and their value demonstrated. The state of the art in the field of diagnostic imaging technology assessment advocates a hierarchical step-by-step approach. Although rigorous, such a hierarchical assessment is time-consuming, and, given the current rapid advances in technology, results are often too late to influence management and policy decisions. The purpose of this article is to discuss a study design in which development, assessment, and implementation of new diagnostic imaging technology take place concurrently in one integrated process. An empirically based pragmatic study design is proposed for imaging technology assessment. To minimize bias and enable comparison with current technology, a randomized controlled design is used whenever feasible and ethical. Outcome measures should reflect the clinical decision-making process based on imaging information and acceptance of the new test. Outcome measures can include additional imaging studies requested, costs of diagnostic work-up and treatment, physicians' confidence in therapeutic decision making, recruitment rate, and patient outcome measures related to the clinical problem. The key feature of the proposed study design is analysis of trends in outcome measures over time.

New tests, such as magnetic resonance imaging (MRI) and electron-beam computed tomography (CT), are being developed for the diagnosis of coronary artery disease. To determine the conditions that a new test must meet to be a cost-effective alternative to established imaging tests. Decision model and cost-effectiveness analysis. Literature review and meta-analysis. 55-year-old men and 65-year-old women presenting with chest pain. Lifetime of the patient. Health care policy. MRI, electron-beam CT, exercise echocardiography, exercise single-photon emission CT, and coronary angiography. Target sensitivity and specificity values for a new noninvasive test. Assuming that society is willing to pay $75 000 per quality-adjusted life-year (QALY) gained, a new test that costs $1000 would need a sensitivity of 94% and a specificity of 90% to be cost-effective. Assuming that society is willing to pay $50 000 per QALY gained, a new test that costs $1000 or more would never be cost-effective. For a test that costs $500, the sensitivity and specificity must each be 95%. New imaging techniques, such as MRI and electron-beam CT, must be relatively inexpensive and have excellent sensitivity and specificity to be cost-effective compared with other techniques for the diagnosis of coronary artery disease. Similar analyses in other areas of health care may help to focus the development of new diagnostic technology.

Purpose. To illustrate the use of a nonparametric bootstrap method in the evaluation of uncertainty in decision models analyzing cost-effectiveness. Methods. The authors reevaluated a previously published cost-effectiveness analysis that used a Markov model comparing initial percutaneous transluminal angioplasty with bypass surgery for femoropopliteal lesions. Each probability in the model was simulated with a first-order Monte Carlo simulation to represent sampling uncertainty. Superimposed on this, a second-order Monte Carlo simulation was performed to represent parameter uncer tainty, drawing the probability values from nonparametric distributions based on published data or from primary collected data as available. After simulation of a mixed (i.e., non-idenfical) cohort of 30,000 patients, 3,000 bootstrap samples of 1,000 pa tients each were drawn and the joint distribution of mean incremental costs and mean effectiveness gained was evaluated. Results. Using a bootstrap sample size of 1,000 patients, 92.7% of the joint distribution of mean incremental costs and mean effective ness gained fell in the quadrant where angioplasty dominated bypass surgery. Another 6.9% of samples demonstrated either greater effectiveness with an incremental cost- effectiveness ratio of at most $20,000/QALY gained, or cost savings with a ratio of at least $20,000 saved/QALY lost. Conclusion. A nonparametric bootstrap method can be used to estimate the joint distribution of mean incremental costs and mean effec tiveness gained, and the results can provide an understanding of the uncertainty in a cost-effectiveness analysis based on a decision model. Key words: cost-effective ness analysis; sensitivity analysis; Monte Carlo simulation; Markov models; bootstrap ping. (Med Decis Making 1998;18:337-346)