A novel design for estimating relative accuracy of screening tests when complete disease verification is not feasible. (English) Zbl 1097.62120

Alonzo, Todd A.; Kittelson, John M.

Biometrics 62, No. 2, 605-612 (2006).

Summary: The accuracy (sensitivity and specificity) of a new screening test can be compared with that of a standard test by applying both tests to a group of subjects in which disease status can be determined by a gold standard (GS) test. However, it is not always feasible to administer a GS test to all study subjects. For example, a study is planned to determine whether a new screening test for cervical cancer (“ThinPrep”) is better than the standard test (“Pap”), and in this setting it is not feasible (or ethical) to determine disease status by biopsy in order to identify women with and without disease for participation in a study. When determination of disease status is not possible for all study subjects, the relative accuracy of two screening tests can still be estimated by using a paired screen-positive (PSP) design in which all subjects receive both screening tests, but only have the GS test if one of the screening tests is positive. Unfortunately in the cervical cancer example, the PSP design is also infeasible because it is not technically possible to administer both the ThinPrep and the Pap at the same time.
We describe a randomized paired screen-positive (RPSP) design in which subjects are randomized to receive one of the two screening tests initially, and only receive the other screening test and GS if the first screening test is positive. We derive maximum likelihood estimators and confidence intervals for the relative accuracy of the two screening tests, and assess the small sample behavior of these estimators using simulation studies. Sample size formulae are derived and applied to the cervical cancer screening trial example, and the efficiency of the RPSP design is compared with other designs.

MSC:

62P10

Applications of statistics to biology and medical sciences; meta analysis

Keywords:

cervical cancer; inverse probability; weighting; Pap; screening; sensitivity; specificity; ThinPrep; verification bias

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