Publication in refereed journal

Times Cited
Web of Science11WOS source URL (as at 14/01/2021) Click here for the latest count
Altmetrics Information

Other information
AbstractInvestigating the prevalence of a disease is an important topic in medical studies. Such investigations are usually based on the classification results of a group of subjects according to whether they have the disease. To classify subjects, screening tests that are inexpensive and nonintrusive to the test subjects are frequently used to produce results in a timely manner. However, such screening tests may suffer from high levels of misclassification. Although it is often possible to design a gold-standard test or device that is not subject to misclassification, such devices are usually costly and time-consuming, and in some cases intrusive to the test subjects. As a compromise between these two approaches, it is possible to use data that are obtained by the method of double-sampling. In this article, we derive and investigate four test statistics for testing a hypothesis on disease prevalence with double-sampling data. The test statistics are implemented through both the asymptotic method suitable for large samples and approximate unconditional method suitable for small samples. Our simulation results show that the approximate unconditional method usually produces a more satisfactory empirical type I error rate and power than its asymptotic counterpart, especially for small to moderate sample sizes. The results also suggest that the score test and the Wald test based on an estimate of variance with parameters estimated under the null hypothesis outperform the others. An real example is used to illustrate the proposed methods.
All Author(s) ListTang ML, Qiu SF, Poon WY, Tang NS
Journal nameJournal of Biopharmaceutical Statistics
Volume Number22
Issue Number2
PublisherTaylor & Francis: STM, Behavioural Science and Public Health Titles
Pages368 - 386
LanguagesEnglish-United Kingdom
KeywordsApproximate unconditional method; Disease prevalence; Double sampling; Partially validated data
Web of Science Subject CategoriesMathematics; Pharmacology & Pharmacy; PHARMACOLOGY & PHARMACY; Statistics & Probability; STATISTICS & PROBABILITY

Last updated on 2021-14-01 at 23:44