Rank methods for the analysis of clustered data in diagnostic trials

A semiparametric approach for meta-analysis of diagnostic accuracy studies with multiple cut-offs

The accuracy of a diagnostic test is often expressed using a pair of measures: sensitivity (proportion of test positives among all individuals with target condition) and specificity (proportion of test negatives among all individuals without target condition). If the outcome of a diagnostic test is binary, results from different studies can easily be summarized in a meta-analysis. However, if the diagnostic test is based on a discrete or continuous measure (e.g., a biomarker), several cut-offs within one study as well as among different studies are published. Instead of taking all information of the cut-offs into account in the meta-analysis, a single cut-off per study is often selected arbitrarily for the analysis, even though there are statistical methods for the incorporation of several cut-offs. For these methods, distributional assumptions have to be met and/or the models may not converge when specific data structures occur. We propose a semiparametric approach to overcome both problems. Our simulation study shows that the diagnostic accuracy is under-estimated, although this underestimation in sensitivity and specificity is relatively small. The comparative approach of Steinhauser et al. is better in terms of coverage probability, but may lead to convergence problems. In addition to the simulation results, we illustrate the application of the semiparametric approach using a published meta-analysis for a diagnostic test differentiating between bacterial and viral meningitis in children.

Diagnostic value of computed tomography combined with ultrasonography in detecting cervical recurrence in patients with thyroid cancer

BACKGROUND

To determine the diagnostic role of CT added to ultrasound for the diagnosis of recurrent differentiated thyroid cancer (DTC) and to evaluate potential benefits for patients.

METHODS

A total of 193 patients with recurrent DTC were retrospectively included. The diagnostic performances of ultrasound and combination of ultrasound and CT (ultrasound/CT) in detecting recurrence were compared. Benefits of CT were assessed based on the presence of any recurrence detected only with additional CT.

RESULTS

In detecting cervical recurrence, ultrasound/CT showed higher sensitivity (P = .001) and lower specificity (P < .001) than ultrasound alone, overall resulting in higher area under the curve (P < .001). Seventy-nine patients (40.9%) benefited from additional CT in detecting recurrence.

CONCLUSION

For reoperation of cervical recurrence in patients with DTC, addition of CT to ultrasound offers better surgical planning by enhancing detection of recurrent cancers that were overlooked with ultrasound alone.

Simultaneous inference for factorial multireader diagnostic trials

We study inference methods for the analysis of multireader diagnostic trials. In these studies, data are usually collected in terms of a factorial design involving the factors Modality and Reader. Furthermore, repeated measures appear in a natural way since the same patient is observed under different modalities by several readers and the repeated measures may have a quite involved dependency structure. The hypotheses are formulated in terms of the areas under the ROC curves. Currently, only global testing procedures exist for the analysis of such data. We derive rank-based multiple contrast test procedures and simultaneous confidence intervals which take the correlation between the test statistics into account. The procedures allow for testing arbitrary multiple hypotheses. Extensive simulation studies show that the new approaches control the nominal type 1 error rate very satisfactorily. A real data set illustrates the application of the proposed methods.

Nonparametric ROC summary statistics for correlated diagnostic marker data

We propose efficient nonparametric statistics to compare medical imaging modalities in multi-reader multi-test data and to compare markers in longitudinal ROC data. The proposed methods are based on the weighted area under the ROC curve, which includes the area under the curve and the partial area under the curve as special cases. The methods maximize the local power for detecting the difference between imaging modalities. We develop the asymptotic results of the proposed methods under a complex correlation structure. Our simulation studies show that the proposed statistics result in much better powers than existing statistics. We apply the proposed statistics to an endometriosis diagnosis study.

A General Class of Signed Rank Tests for Clustered Data when the Cluster Size is Potentially Informative

Rank based tests are alternatives to likelihood based tests popularized by their relative robustness and underlying elegant mathematical theory. There has been a serge in research activities in this area in recent years since a number of researchers are working to develop and extend rank based procedures to clustered dependent data which include situations with known correlation structures (e.g., as in mixed effects models) as well as more general form of dependence.The purpose of this paper is to test the symmetry of a marginal distribution under clustered data. However, unlike most other papers in the area, we consider the possibility that the cluster size is a random variable whose distribution is dependent on the distribution of the variable of interest within a cluster. This situation typically arises when the clusters are defined in a natural way (e.g., not controlled by the experimenter or statistician) and in which the size of the cluster may carry information about the distribution of data values within a cluster.Under the scenario of an informative cluster size, attempts to use some form of variance adjusted sign or signed rank tests would fail since they would not maintain the correct size under the distribution of marginal symmetry. To overcome this difficulty Datta and Satten (2008; Biometrics, 64, 501-507) proposed a Wilcoxon type signed rank test based on the principle of within cluster resampling. In this paper we study this problem in more generality by introducing a class of valid tests employing a general score function. Asymptotic null distribution of these tests is obtained. A simulation study shows that a more general choice of the score function can sometimes result in greater power than the Datta and Satten test; furthermore, this development offers the user a wider choice. We illustrate our tests using a real data example on spinal cord injury patients.