Pence Brian Wells, Miller William C, Gaynes Bradley N
Department of Community and Family Medicine, Duke Global Health Institute, Duke University, Durham, NC, 27705, USA.
Psychol Assess. 2009 Jun;21(2):235-9. doi: 10.1037/a0015686.
Prevalence and validation studies rely on imperfect reference standard (RS) diagnostic instruments that can bias prevalence and test characteristic estimates. The authors illustrate 2 methods to account for RS misclassification. Latent class analysis (LCA) combines information from multiple imperfect measures of an unmeasurable latent condition to estimate sensitivity (Se) and specificity (Sp) of each measure. With simple algebraic sensitivity analysis (SA), one uses researcher-specified RS misclassification rates to correct prevalence and test characteristic estimates and can succinctly summarize a range of scenarios with Monte Carlo simulation. The authors applied LCA to a validation study of a new substance use disorder (SUD) screener and a larger prevalence study. With a traditional validation study analysis in which an error-free RS (Structured Clinical Interview for DSM-IV Axis I Disorders [SCID]; M. H. First, R. L. Spitzer, M. Gibbon, & J. Williams, 1990) is assumed, the authors estimated the screener had 86% Se and 75% Sp. Validation study estimates from LCA were 91% Se, 81% Sp (screener), 73% Se, and 98% Sp (SCID). SA in the prevalence study suggested the prevalence of SUD was underestimated by 22% because SCID was assumed to be error-free. LCA and SA can assist investigators in relaxing the unrealistic assumption of perfect RSs in reporting prevalence and validation study results.
患病率和验证研究依赖于不完善的参考标准（RS）诊断工具，这些工具可能会使患病率和检验特征估计产生偏差。作者阐述了两种应对RS错误分类的方法。潜在类别分析（LCA）结合了来自对不可测量的潜在状况的多个不完善测量的信息，以估计每个测量的灵敏度（Se）和特异度（Sp）。通过简单的代数灵敏度分析（SA），研究者可以使用指定的RS错误分类率来校正患病率和检验特征估计，并能通过蒙特卡洛模拟简洁地总结一系列情况。作者将LCA应用于一项新的物质使用障碍（SUD）筛查工具的验证研究以及一项更大规模的患病率研究。在传统的验证研究分析中，假设存在无误差的RS（《精神疾病诊断与统计手册》第四版轴I障碍结构化临床访谈[SCID]；M. H. 弗斯特、R. L. 斯皮策、M. 吉本和J. 威廉姆斯，1990年），作者估计该筛查工具的Se为86%，Sp为75%。LCA得出的验证研究估计值为Se = 91%，Sp = 81%（筛查工具），Se = 73%，Sp = 98%（SCID）。患病率研究中的SA表明，由于假设SCID无误差，SUD的患病率被低估了22%。LCA和SA可以帮助研究者在报告患病率和验证研究结果时放宽对完美RS这一不切实际的假设。
