Test procedures for disease prevalence with partially validated data

Man Lai TANG, Shi Fang Qiu, Wai Yin Poon*, Nian Sheng Tang

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

14 Citations (Scopus)


Investigating 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.

Original languageEnglish
Pages (from-to)368-386
Number of pages19
JournalJournal of Biopharmaceutical Statistics
Issue number2
Publication statusPublished - 1 Mar 2012

Scopus Subject Areas

  • Statistics and Probability
  • Pharmacology
  • Pharmacology (medical)

User-Defined Keywords

  • Approximate unconditional method
  • Disease prevalence
  • Double sampling
  • Partially validated data


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