Prevalence estimation and validation of new instruments in psychiatric research: an application of latent class analysis and sensitivity analysis.

Prevalence estimation and validation of new instruments in psychiatric research: an application of latent class analysis and sensitivity analysis.
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精神病学研究中新工具的患病率估计和验证:潜在类别分析和敏感性分析的应用。

DOI:
10.1037/a0015686
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发表时间:
2009
影响因子:
3.6
通讯作者:
Gaynes,BradleyN
Gaynes,BradleyN
中科院分区:
心理学2区
文献类型:
--
作者:
Pence,BrianWells;Miller,WilliamC;Gaynes,BradleyN

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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]; MH First, RL 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.(PsycINFO Database Record (c) 2019 APA, all rights reserved)
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