Use of Individual-level Covariates to Improve Latent Class Analysis of Trypanosoma Cruzi Diagnostic Tests.
Use of Individual-level Covariates to Improve Latent Class Analysis of Trypanosoma Cruzi Diagnostic Tests.
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DOI：
10.1515/2161-962x.1005
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发表时间：
2012-08
影响因子：
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通讯作者：
Levy MZ
中科院分区：
文献类型：
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作者：
Tustin AW;Small DS;Delgado S;Neyra RC;Verastegui MR;Ancca Juárez JM;Quispe Machaca VR;Gilman RH;Bern C;Levy MZ
统计方法，如潜在类分析可以估计诊断测试的灵敏度和特异性时，没有完美的参考测试存在。传统的潜在类方法假设一个恒定的疾病流行率在一个或多个测试的人群。当疾病的风险以已知的方式变化时，这些模型无法利用通过测量个体水平的风险因素可以获得的额外信息。我们表明，通过整合复杂的基于现场的流行病学数据，其中疾病患病率作为个人水平协变量的连续函数而变化，我们的模型比以前的方法产生更准确的灵敏度和特异性估计。我们将这种技术应用到一个模拟的人口和实际查加斯病测试数据从阿雷基帕，秘鲁附近的一个社区。我们模型的结果估计，一线酶联免疫吸附试验的灵敏度为78%（95% CI：62-100%），特异性为100%（95% CI：99-100%）。估计确证性免疫荧光试验的灵敏度为73%（95% CI：65-81%），特异性为99%（95% CI：96-100%）。
Statistical methods such as latent class analysis can estimate the sensitivity and specificity of diagnostic tests when no perfect reference test exists. Traditional latent class methods assume a constant disease prevalence in one or more tested populations. When the risk of disease varies in a known way, these models fail to take advantage of additional information that can be obtained by measuring risk factors at the level of the individual. We show that by incorporating complex field-based epidemiologic data, in which the disease prevalence varies as a continuous function of individual-level covariates, our model produces more accurate sensitivity and specificity estimates than previous methods. We apply this technique to a simulated population and to actual Chagas disease test data from a community near Arequipa, Peru. Results from our model estimate that the first-line enzyme-linked immunosorbent assay has a sensitivity of 78% (95% CI: 62–100%) and a specificity of 100% (95% CI: 99–100%). The confirmatory immunofluorescence assay is estimated to be 73% sensitive (95% CI: 65–81%) and 99% specific (95% CI: 96–100%).