Alonzo Todd A, Pepe Margaret Sullivan, Lumley Thomas
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.
Biostatistics. 2003 Apr;4(2):313-26. doi: 10.1093/biostatistics/4.2.313.
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.
理想情况下，疾病患病率的估计应使用“金标准”来确定目标人群中所有受试者的真实疾病状态。然而，在实际操作中，金标准可能成本过高或具有侵入性，无法应用于所有受试者，在这种情况下，通常采用两阶段设计。第一阶段的数据由对所有研究对象进行的低成本、非侵入性筛查测试组成，用于确定在第二阶段接受金标准检测的对象。两阶段研究中患病率的简单估计可能存在偏差（验证偏差）。插补和重新加权估计器通常用于避免这种偏差。我们对比了各种患病率估计器的形式和属性。分布理论和模拟研究用于调查它们的偏差和效率。我们得出结论，半参数有效方法是两阶段研究中患病率估计的首选方法。它比插补和其他重新加权估计器更稳健，效率相当。它也易于实施。我们使用这种方法，利用大烟山研究的数据来研究青少年抑郁症的患病率。
