无金标准条件下的诊断试验一致性检验

正确地评价诊断试验在临床上的作用对于现代医学有着重要的意义。对诊断试验的分析评价通常都借助于灵敏度、特异度、预测值以及ROC曲线下的面积等指标进行评价。不过，这些指标在实际应用中存在比较大的限制。首先，上述指标的确定往往要求有“金标准”的参考。     

潜在分类模型在无金标准诊断试验中的评价

目的探讨无金标准诊断试验下灵敏度与特异度的估计方法。方法以三个诊断试验为例,利用潜在分类模型估计无金标准诊断试验的灵敏度、特异度及其95%可信区间。并将潜在分类模型估计的结果与Bayesian方法估计结果进行比较。结果三个诊断试验的灵敏度与特异度的95%可信区间均存在一定的重叠,表明两种方法估计结果较为相似。结论诊断试验的灵敏度与特异度在无金标准时是无法直接计算的,可利用潜在分类模型与Bayesian方法估算诊断试验的灵敏度与特异度。     

无金标准情况下诊断试验的评价方法

目的 探讨无金标准情况下诊断试验的评价方法及患病率的估计。方法 从Ｂｅｙｅｓｉａｎ参数估计原理出发，用Ｇｉｂｂｓ抽样法完成参数后验密度的估计。结果 将有金标准的资料作为无金标准处理，所得参数估计值前后吻合，效果良好，并可得到参数的后验密度分布图。结论 上述方法可有效地估计无金标准情况下诊断试验的评价参数。     

无金标准情况下三个诊断试验评价方法

目的探讨三个诊断试验在无金标准情况下诊断试验方法及检出率、灵敏度、特异度的估计。方法根据Bayesian参数估计原理,利用Gibbs抽样方法得到后验密度估计。结果可计算出后验参数估值及其95?yesian可信区间。结论文中提出的方法可有效地估计无金标准情况下三个诊断试验的后验参数。     

基于潜分类方法评价无金标准条件下诊断试验的准确性

[目的]探讨无金标准条件下诊断试验准确性评价的潜分类方法。[方法]介绍潜分类模型在无金标准诊断试验评价中的原理、试验设计和评价方法,用两人群两试验实例说明潜分类方法的应用。[结果]对于二分类反应变量,假设条件独立和试验准确性稳定,至少需要两个人群两种试验方法或一个人群三种试验方法才能满足模型可识别性并用于频率学派统计评价;贝叶斯统计不需满足模型的可识别性,但需引入先验分布,且存在先验依赖性。[结论]潜分类方法可用于无金标准时的诊断试验评价,但要选择适合的试验设计和评价方法。     

医院感染病例上报的灵敏度与特异度分析

医院感染病例的监测是医院感染的重要组成部分，是医院感染控制的基础，为医院感染管理提供科学依据，而临床医务人员积极主动地填报感染病例又是我们开展医院感染病例监测的关键，感染病例上报不准确、不及时，医院感染管理工作将无从谈起。必须对此引起重视，采取针对性措施，提高医院感染病例上报的灵敏度与特异度，降低感染病例漏报与错报。     

如何提高医院感染监测系统灵敏度和特异度

医院感染已经成为当今突出的公共卫生问题,是当前医院管理中的难题,是影响医疗质量的重要问题[1],因此加强对医院感染的管理、预防、控制,降低医院感染率,可有效地提高医疗质量、缩短平均住院日、保障医疗安全。开展有效的医院感染监测能及时发现医院感染病例及相关因素,及时发现医院感染的聚集性发生或暴发流行,为医院感染的预防和控制提供可靠信息和科学依据。     

广州市新型冠状病毒肺炎病例密切接触者核酸检测灵敏度和特异度分析

目的:分析广州市新型冠状病毒肺炎（COVID-19）疫情防控不同阶段中,密切接触者新型冠状病毒（新冠病毒）核酸检测的灵敏度和特异度,为优化疫情防控策略提供科学依据。方法:2020年2月21日至9月22日广州市COVID-19病例的密切接触者20 348例,均已接受新冠病毒核酸检测。针对疫情防控的不同阶段,比较核酸检测的...     

无金标准诊断实验条件下贝叶斯先验参数的确定方法及SAS实现

目的探讨无金标准诊断实验条件下,贝叶斯先验参数的确定方法,并比较不同方法的应用条件。方法根据贝叶斯共轭先验分布原理,对二项分布的共轭贝塔分布中的α、β两个先验参数的确定方法进行分析比较,编写SAS程序确定先验参数。结果在共轭先验分布的条件下,先验矩、分位数、众数与分位数三种方法确定的先验分布参数结果一致。结论在实际工作中,应根据已知条件和具体情况决定采用何种方法计算先验分布参数。     

基于灵敏度和特异度任意赋权的广义标准化诊断符合率

目的建立一种基于灵敏度(SEN)和特异度(SPE)任意赋权的广义标准化诊断符合率统计方法。方法广义标准化诊断符合率(e’)的构造满足以下两个原则:(1)灵敏度和特异度的权重(w)之和为1;(2)满足特殊性:当灵敏度和特异度等权时,广义标准化诊断符合率等于标准化诊断符合率(e),即有e’=e。所构造广义标准化诊断符合率为:e1’=[(n1+m1)·2w·SEN1+(m2+n2)·2(1-w)·SPE1]/(n+m),e2’=[(n1+m1)·2w·SEN2+(m2+n2)·2(1-w)·SPE]/(2n+m),(0≤w≤1)。根据中心极限定理,推导出e’的标准误和两个e’比较的Z统计推断方法,进一步推导出权重w的变化对检验统计量Z的影响。结果所构造的广义标准化诊断符合率满足上述构造两个原则。结论本研究所建立的广义标准化诊断符合率方法解决了应用中对灵敏度和特异度有不同赋权要求的问题,为诊断试验评价提供了新的手段。     

Challenges and opportunities in evaluating diagnostic tests

The field of clinical research conducted to evaluate diagnostic tests has evolved substantially over the last 25 years. This article discusses three current challenges and opportunities in conducting such research: the limitations of the “traditional focus” of clinical epidemiology in evaluating tests; how different decision makers use data produced by clinical epidemiology research, and what those uses mean for future research; and special challenges and opportunities in evaluating molecular-based tests.     

Biases in the assessment of diagnostic tests

Diagnostic tests are traditionally characterized by simple measures of efficacy such as the sensitivity and the specificity. These measures, though widely recognized and easy to understand, are subject to definitional arbitrariness. Moreover, studies constructed to estimate the sensitivity and specificity are susceptible to a variety of biases. In this paper the various potential problems are described with reference to examples from the diagnostic literature. These difficulties have implications for the design of diagnostic test evaluations, and the choice of suitable measures of test efficacy.     

Evaluating accuracy of diagnostic tests with intermediate results in the absence of a gold standard

Intermediate test results often occur with diagnostic tests. When assessing diagnostic accuracy, it is important to properly report and account for these results. In the literature, these results are commonly discarded prior to analysis or treated as either a positive or a negative result. Although such adjustments allow sensitivity and specificity to be computed in the standard way, these forced decisions limit the interpretability and usefulness of the results. Estimation of diagnostic accuracy is further complicated when tests are evaluated without a gold standard. Although traditional latent class modeling can be readily applied to analyze these data and account for intermediate results, these models assume that tests are independent conditional on the true disease status, which is rarely valid in practice. We extend both the log‐linear latent class model and the probit latent class model to accommodate the conditional dependence among tests while taking the intermediate results into consideration. We illustrate our methods using a simulation study and a published medical study on the detection of epileptiform activity in the brain. Copyright © 2012 John Wiley & Sons, Ltd.     

Beyond sensitivity,specificity and statistical independence

Sensitivity and specificity have clear definitions when there is a single test for one disease, and the test is either positive or negative. This paper presents a unified appraoch for obtaining posterior probabilities (predictive values) when there are more than two test outcomes and/or more than one disease state. In these cases, sensitivity and specificity do not have clear definitions. Three examples from the literature demonstrate how this approach simplifies the presentation of Bayesian revision of prior probabilities. Use of proper care in data collection for the purpose of estimating conditional probabilities can avoid assumptions of statistical independence.     

Bayesian estimation of the receiver operating characteristic curve for a diagnostic test with a limit of detection in the absence of a gold standard

The receiver operating characteristic (ROC) curve is commonly used for evaluating the discriminatory ability of a biomarker. Measurements for a diagnostic test may be subject to an analytic limit of detection leading to immeasurable or unreportable test results. Ignoring the scores that are beyond the limit of detection of a test leads to a biased assessment of its discriminatory ability, as reflected by indices such as the associated area under the curve (AUC). We propose a Bayesian approach for the estimation of the ROC curve and its AUC for a test with a limit of detection in the absence of gold standard based on assumptions of normally and gamma‐distributed data. The methods are evaluated in simulation studies, and a truncated gamma model with a point mass is used to evaluate quantitative real‐time polymerase chain reaction data for bovine Johne's disease (paratuberculosis). Simulations indicated that estimates of diagnostic accuracy and AUC were good even for relatively small sample sizes (n=200). Exceptions were when there was a high per cent of unquantifiable results (60 per cent) or when AUC was ?0.6, which indicated a marked overlap between the outcomes in infected and non‐infected populations. Copyright © 2010 John Wiley & Sons, Ltd.     

Combining multiple continuous tests for the diagnosis of kidney impairment in the absence of a gold standard

Receiver operating characteristic (ROC) curves are commonly used to summarize the classification accuracy of diagnostic tests. It is not uncommon in medical practice that multiple diagnostic tests are routinely performed or multiple disease markers are available for the same individuals. When the true disease status is verified by a gold standard (GS) test, a variety of methods have been proposed to combine such potential correlated tests to increase the accuracy of disease diagnosis. In this article, we propose a method of combining multiple diagnostic tests in the absence of a GS. We assume that the test values and their classification accuracies are dependent on covariates. Simulation studies are performed to examine the performance of the combination method. The proposed method is applied to data from a population-based aging study to compare the accuracy of three screening tests for kidney function and to estimate the prevalence of moderate kidney impairment.     

Instrumental variables in the evaluation of diagnostic test procedures when the true disease state is unknown

We explore the estimation of sensitivity and specificity of diagnostic tests when the true disease state is unknown. Instrumental variables which subdivide the patient population are used. A logistic model, relating these instrumental variables to the (unknown) true disease state is proposed. It is shown that this procedure allows the goodness-of-fit to the resulting model to be tested.     

Estimating disease prevalence in the absence of a gold standard

When estimating disease prevalence, it is not uncommon to have data from conditionally dependent diagnostic tests. In such a situation, the estimation of prevalence is difficult if none of the tests is considered to be a gold standard. In this paper we develop a Bayesian approach to estimating disease prevalence based on the results of two diagnostic tests, allowing for the possibility that the tests are conditionally dependent, but not conditioning on any particular dependence structure. This involves the construction of four models with various forms of conditional dependence and uses Bayesian model averaging, enabled by reversible jump MCMC, to obtain an overall estimate of the prevalence. This methodology is demonstrated using a study on the prevalence of Strongyloides infection.     

Assessing validity of a depression screening instrument in the absence of a gold standard

PurposeWe evaluated the extent to which use of a hypothesized imperfect gold standard, the Composite International Diagnostic Interview (CIDI), biases the estimates of diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9). We also evaluate how statistical correction can be used to address this bias.MethodsThe study was conducted among 926 adults where structured interviews were conducted to collect information about participants' current major depressive disorder using PHQ-9 and CIDI instruments. First, we evaluated the relative psychometric properties of PHQ-9 using CIDI as a gold standard. Next, we used a Bayesian latent class model to correct for the bias.ResultsIn comparison with CIDI, the relative sensitivity and specificity of the PHQ-9 for detecting major depressive disorder at a cut point of 10 or more were 53.1% (95% confidence interval: 45.4%–60.8%) and 77.5% (95% confidence interval, 74.5%–80.5%), respectively. Using a Bayesian latent class model to correct for the bias arising from the use of an imperfect gold standard increased the sensitivity and specificity of PHQ-9 to 79.8% (95% Bayesian credible interval, 64.9%–90.8%) and 79.1% (95% Bayesian credible interval, 74.7%–83.7%), respectively.ConclusionsOur results provided evidence that assessing diagnostic validity of mental health screening instrument, where application of a gold standard might not be available, can be accomplished by using appropriate statistical methods.