Biostatistics 4:313-326 (2003)
© 2003 Oxford University Press
Estimating disease prevalence in two-phase studies
Univeristy of Southern California, Keck School of Medicine, Children's Oncology Group, 440 Huntington Dr. Suite 300, P.O. Box 60012, Arcadia, CA 91066, USA talonzo{at}childrensoncologygroup.org
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA mspepe{at}u.washington.edu
Department of Biostatistics, University of Washington, Seattle, WA 98195, USA thomas{at}u.washington.edu
To whom correspondence should be addressed
Disease prevalence is ideally estimated using a ‘gold standard’ to ascertain true disease status on all subjects in a population of interest. In practice, however, the gold standard may be too costly or invasive to be applied to all subjects, in which case a two-phase design is often employed. Phase 1 data consisting of inexpensive and non-invasive screening tests on all study subjects are used to determine the subjects that receive the gold standard in the second phase. Naive estimates of prevalence in two-phase studies can be biased (verification bias). Imputation and re-weighting estimators are often used to avoid this bias. We contrast the forms and attributes of the various prevalence estimators. Distribution theory and simulation studies are used to investigate their bias and efficiency. We conclude that the semiparametric efficient approach is the preferred method for prevalence estimation in two-phase studies. It is more robust and comparable in its efficiency to imputation and other re-weighting estimators. It is also easy to implement. We use this approach to examine the prevalence of depression in adolescents with data from the Great Smoky Mountain Study.
Keywords: Double sampling; Imputation; Inverse probability weighting; Mean score; Semiparametric; Surrogate outcome; Verification bias
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