![]() ![]() N2 - In noninferiority studies, a limit of indifference is used to express a tolerance in results such that the clinician would regard such results as being acceptable or 'not worse'. These patterns were also observed as applied to sequential strategies used to diagnose diabetes in the Pima Indians.", Cost was reduced at high false positive rates (FPRs) at higher limit of indifference (0.999) and at small FPRs as the limit of indifference decreased (0.95). The limit of indifference tended to have less of an effect on the MCMROC curves than on the associated cost curves that were greatly affected. We compared the MCMROC and its associated cost curve between limits of indifference set to 0.999, 0.95 (a 5% reduction in TPR), and 1 (no reduction in TPR). In doing so, we generated the minimum cost maximum ROC (MCMROC) curve, which reflects the reduced sensitivity and cost of testing. We expressed a limit of indifference for the range of acceptable sensitivity values and examined the associated cost of testing within this range. ![]() We applied this concept to a measure of accuracy, the Receiver Operating Characteristic (ROC) curve, for a sequence of tests. These patterns were also observed as applied to sequential strategies used to diagnose diabetes in the Pima Indians.Ībstract = "In noninferiority studies, a limit of indifference is used to express a tolerance in results such that the clinician would regard such results as being acceptable or 'not worse'. It was found that the BN and BE strategies were the most consistently accurate and least expensive choice.In noninferiority studies, a limit of indifference is used to express a tolerance in results such that the clinician would regard such results as being acceptable or 'not worse'. The use of BMI and plasma glucose concentration to diagnose diabetes in Pima Indians was presented as an example of a real-world application of these strategies. The AUC values and the ratio of standard deviations both had a greater effect on cost curves, MROC curves, and MCMROC curves than prevalence and correlation. The parameters tended to have less of an effect on the MROC and MCMROC curves than they had on the cost curves, which were affected greatly. The following patterns were noted: Under all parameter settings, the MROC curve of the BE strategy never performed worse than the BN and BP strategies, and it most frequently had the lowest cost. Within these strategies, four different parameters that could influence the performance of the combined tests were examined: the area under the curve (AUC) of each individual test, the ratio of standard deviations (b) from assumed underlying disease and non-disease populations, correlation (rho) between underlying disease populations, and disease prevalence. This research introduced a newly-developed ROC curve reflecting this reduced sensitivity and cost of testing called the Minimum Cost Maximum Receiver Operating Characteristic (MCMROC) curve. It was shown that the cost of the test sequence could be reduced without sacrificing accuracy beyond an acceptable range by setting an acceptable tolerance (q) on maximum test sensitivity. Descriptions of these strategies were provided in terms of accuracy (using the maximum receiver operating curve or MROC) and cost of testing (defined as the proportion of subjects who need 2 tests to diagnose disease), with the goal to minimize the number of tests needed for each subject while maintaining test accuracy. All three strategies were used to combine results of two medical tests to diagnose a disease or medical condition. This research described and compared three sequential testing strategies: believe the negative (BN), believe the positive (BP) and believe the extreme (BE), the latter being a less-examined strategy. The practice of sequential testing is followed by the evaluation of accuracy, but often not by the evaluation of cost. ![]()
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