<p> The accuracy of a diagnostic test is often evaluated with the measures of sensitivity
and specificity and the joint dependence between these two measures is captured
by the receiver operating characteristic (ROC) curve. To combine multiple testing
results from studies that are assumed to follow the same underlying probability law,
a smooth summary receiver operating characteristic (SROC) curve can be fitted.
Moses et al. (1993) proposed a least squares approach to fit the smooth SROC
curve. </p> <p> In this thesis we overview the summary measures for the ROC curve in single
study data as well as the summary statistics for the SROC curves in meta-analysis.
These summary statistics include, the area under the curve (AUC), Q* statistic,
area swept under the curve (ASC) and the partial area under the curve (pAUC). </p> <p> Our focus, however is mainly on the partial area under the SROC curve as it
is being used frequently in meta-analysis of diagnostic testing. The appeal to use
the pAUC instead of the full AUC is that the partial area can be used to focus on a clinically relevant region of the SROC curve where false positive rate (FPR)
is small. Simulations and considerations for the use of the summary indices of the
ROC and SROC curves are presented here. </p> / Thesis / Master of Science (MSc)
| Identifer | oai:union.ndltd.org:mcmaster.ca/oai:macsphere.mcmaster.ca:11375/21254 |
| Date | 12 1900 |
| Creators | Fill, Roxanne |
| Contributors | Macdonald, Peter, Statistics |
| Source Sets | McMaster University |
| Language | English |
| Detected Language | English |
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