Defense Date

2010

Document Type

Dissertation

Degree Name

Doctor of Philosophy

Department

Biostatistics

First Advisor

Donna McClish

Second Advisor

Christine Schubert

Abstract

The practice of sequential testing is followed by the evaluation of accuracy, but often not by the evaluation of cost. 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. All three strategies were used to combine results of two medical tests to diagnose a disease or medical condition. 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. 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. 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. 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. 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. 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 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 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. It was found that the BN and BE strategies were the most consistently accurate and least expensive choice.

Rights

© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

June 2010

Included in

Biostatistics Commons

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