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

Diagnostic accuracy of maternal serum multiple marker screening for early detection of gestational diabetes mellitus in the absence of a gold standard test

Background

Gestational diabetes mellitus (GDM) is associated with adverse diabetic complications for both mother and child during pregnancy. The common Gold Standard (GS) for diagnosis of GDM is 75 g oral glucose tolerance test (OGTT) during 24–28 gestational weeks which seems a little late for any proper intervention. This study aimed to employ the Bayesian latent class models (LCMs) for estimating the early diagnostic power of combination of serum multiple marker in detecting GDM during 14–17 weeks of gestation.

Methods

Data from a sample of 523 pregnant women who participated in gestational diabetes screening tests at health centers affiliated to Shahid Beheshti University of Medical Sciences in Tehran, Iran from 2017 to 2018 were used. The beta-human chorionic gonadotropin (β-hCG), unconjugated estriol (uE3), and alfa-fetoprotein (AFP) values were extracted from case records for all participants. The Bayesian LCMs were applied for estimating sensitivity, specificity, and area under receiver operating characteristic curve (AUC) of combining the three biomarkers’ results in the absence of GS, adjusting for maternal age and body mass index.

Results

The mean (standard deviation) maternal age of the participants was 28.76 (±5.33) years. Additionally, the mean (standard deviation) BMI was 24.57 (±3.22) kg/m². According to the Bayesian model, the cSensitivity, cSpecificity, and cAUC for the optimal composite diagnostic test were estimated as 94% (95% credible interval (CrI) [0.91–0.99]), 86% (95% CrI [0.80–0.92]), and 0.92 (95% CrI [0.87–0.98]), respectively.

Conclusions

Overall, the findings revealed that the combination of uE3, AFP, and β-hCG results might be considered as an acceptable predictor for detecting GDM with a rather high level of accuracy in the early second trimester of pregnancy without a GS.

Diagnostic test evaluation methodology: A systematic review of methods employed to evaluate diagnostic tests in the absence of gold standard - An update

OBJECTIVE

To systematically review methods developed and employed to evaluate the diagnostic accuracy of medical test when there is a missing or no gold standard.

STUDY DESIGN AND SETTINGS

Articles that proposed or applied any methods to evaluate the diagnostic accuracy of medical test(s) in the absence of gold standard were reviewed. The protocol for this review was registered in PROSPERO (CRD42018089349).

RESULTS

Identified methods were classified into four main groups: methods employed when there is a missing gold standard; correction methods (which make adjustment for an imperfect reference standard with known diagnostic accuracy measures); methods employed to evaluate a medical test using multiple imperfect reference standards; and other methods, like agreement studies, and a mixed group of alternative study designs. Fifty-one statistical methods were identified from the review that were developed to evaluate medical test(s) when the true disease status of some participants is unverified with the gold standard. Seven correction methods were identified and four methods were identified to evaluate medical test(s) using multiple imperfect reference standards. Flow-diagrams were developed to guide the selection of appropriate methods.

CONCLUSION

Various methods have been proposed to evaluate medical test(s) in the absence of a gold standard for some or all participants in a diagnostic accuracy study. These methods depend on the availability of the gold standard, its' application to the participants in the study and the availability of alternative reference standard(s). The clinical application of some of these methods, especially methods developed when there is missing gold standard is however limited. This may be due to the complexity of these methods and/or a disconnection between the fields of expertise of those who develop (e.g. mathematicians) and those who employ the methods (e.g. clinical researchers). This review aims to help close this gap with our classification and guidance tools.

A new semiparametric transformation approach to disease diagnosis with multiple biomarkers

When multiple biomarkers are available for disease diagnosis, it is desirable to efficiently combine them to form a single index. Making use of the Neyman-Pearson paradigm, we propose a new combination/transformation approach to disease diagnosis that efficiently combines multiple biomarkers. The proposed method does not require that the biomarkers be jointly normally distributed or the covariance matrices for the diseased and the nondiseased are nondifferential. An R package is developed to implement the proposed method. Simulations and two real data examples demonstrate advantages of the new method over existing ones.

Updated Estimates Suggest a Much Higher Prevalence of Arthritis in United States Adults Than Previous Ones

OBJECTIVE

National estimates of arthritis prevalence rely on a single survey question about doctor-diagnosed arthritis without using survey information on joint symptoms, even though some subjects with only the latter have been shown to have arthritis. The sensitivity of the current surveillance definition is only 53% and 69% in subjects ages 45-64 years and ages ≥65 years, respectively, resulting in misclassification of nearly one-half and one-third of subjects in those age groups. This study was undertaken to estimate arthritis prevalence based on an expansive surveillance definition that is adjusted for the measurement errors in the current definition.

METHODS

Using the 2015 National Health Interview Survey, we developed a Bayesian multinomial latent class model for arthritis surveillance based on doctor-diagnosed arthritis, joint symptoms, and whether symptom duration exceeded 3 months.

RESULTS

Of 33,672 participants, 19.3% of men and 16.7% of women ages 18-64 years and 15.7% of men and 13.5% of women ages ≥65 years affirmed joint symptoms without doctor-diagnosed arthritis. The measurement error-adjusted prevalence of arthritis was 29.9% (95% Bayesian probability interval [95% PI] 23.4-42.3) in men ages 18-64 years, 31.2% (95% PI 25.8-44.1) in women ages 18-64 years, 55.8% (95% PI 49.9-70.4) in men ages ≥65 years, and 68.7% (95% PI 62.1-79.9) in women ages ≥65 years. Arthritis affected 91.2 million adults (of 247.7 million; 36.8%) in the US in 2015, which included 61.1 million persons between 18 and 64 years of age (of 199.9 million; 30.6%). Our arthritis prevalence estimate was 68% higher than the previously reported national estimate.

CONCLUSION

Arthritis prevalence in the US population has been substantially underestimated, especially among adults younger than 65 years of age.

STARD-BLCM: Standards for the Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models

The Standards for the Reporting of Diagnostic Accuracy (STARD) statement, which was recently updated to the STARD2015 statement, was developed to encourage complete and transparent reporting of test accuracy studies. Although STARD principles apply broadly, the checklist is limited to studies designed to evaluate the accuracy of tests when the disease status is determined from a perfect reference procedure or an imperfect one with known measures of test accuracy. However, a reference standard does not always exist, especially in the case of infectious diseases with a long latent period. In such cases, a valid alternative to classical test evaluation involves the use of latent class models that do not require a priori knowledge of disease status. Latent class models have been successfully implemented in a Bayesian framework for over 20 years. The objective of this work was to identify the STARD items that require modification and develop a modified version of STARD for studies that use Bayesian latent class analysis to estimate diagnostic test accuracy in the absence of a reference standard. Examples and elaborations for each of the modified items are provided. The new guidelines, termed STARD-BLCM (Standards for Reporting of Diagnostic accuracy studies that use Bayesian Latent Class Models), will facilitate improved quality of reporting on the design, conduct and results of diagnostic accuracy studies that use Bayesian latent class models.

An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis

In estimating ROC curves of multiple tests, some a priori constraints may exist, either between the healthy and diseased populations within a test or between tests within a population. In this paper, we proposed an integrated modeling approach for ROC curves that jointly accounts for stochastic and variability orders. The stochastic order constrains the distributional centers of the diseased and healthy populations within a test, while the variability order constrains the distributional spreads of the tests within each of the populations. Under a Bayesian nonparametric framework, we used features of the Dirichlet process mixture to incorporate these order constraints in a natural way. We applied the proposed approach to data from the Physician Reliability Study that investigated the accuracy of diagnosing endometriosis using different clinical information. To address the issue of no gold standard in the real data, we used a sensitivity analysis approach that exploited diagnosis from a panel of experts. To demonstrate the performance of the methodology, we conducted simulation studies with varying sample sizes, distributional assumptions and order constraints. Supplementary materials for this article are available online.

Diagnostic performance analysis for diabetic cardiovascular autonomic neuropathy based on short-term heart rate variability using Bayesian methods: preliminary analysis

OBJECTIVES

This study aimed to estimate the performance of diabetic cardiovascular autonomic neuropathy (DCAN) diagnostic tests in the absence of a gold standard.

BACKGROUND

The DCAN prevalence is rapidly growing in all populations worldwide. No document has been reported about diagnostic performance for DCAN based on short-term HRV without a gold standard.

METHODS

We conducted a cross-sectional study to perform diagnostic test in Chinese diabetic patients. A dataset contained 56 subjects who completed both the short-term HRV test and Ewing's test. Simultaneous inferences about the population prevalence and the performance of each diagnostic test were possible using the Bayesian approach.

RESULTS

The HRV test had a high sensitivity (0.837 and 0.821 for independence model) and specificity (0.838 and 0.797 for dependence model) to DCAN. In addition, the non-inferiority test rejected the hypothesis that the performance of the HRV test was inferior to that of Ewing's test (P < 0.05). The estimated DCAN prevalence in our study sample was more than 0.400.

CONCLUSION

Our findings provided evidence that short-term HRV were used for the DCAN diagnostic test with a high sensitivity and specificity. ClinicalTrial.org ID: NCT02461381.

Biomarkers of tobacco smoke exposure

Diseases and death caused by exposure to tobacco smoke have become the single most serious preventable public health concern. Thus, biomarkers that can monitor tobacco exposure and health effects can play a critical role in tobacco product regulation and public health policy. Biomarkers of exposure to tobacco toxicants are well established and have been used in population studies to establish public policy regarding exposure to second-hand smoke, an example being the nicotine metabolite cotinine, which can be measured in urine. Biomarkers of biological response to tobacco smoking range from those indicative of inflammation to mRNA and microRNA patterns related to tobacco use and/or disease state. Biomarkers identifying individuals with an increased risk for a pathological response to tobacco have also been described. The challenge for any novel technology or biomarker is its translation to clinical and/or regulatory application, a process that requires first technical validation of the assay and then careful consideration of the context the biomarker assay may be used in the regulatory setting. Nonetheless, the current efforts to investigate new biomarker of tobacco smoke exposure promise to offer powerful new tools in addressing the health hazards of tobacco product use. This review will examine such biomarkers, albeit with a focus on those related to cigarette smoking.

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification.

Smooth ROC curves and surfaces for markers subject to a limit of detection using monotone natural cubic splines

The use of ROC curves in evaluating a continuous or ordinal biomarker for the discrimination of two populations is commonplace. However, in many settings, marker measurements above or below a certain value cannot be obtained. In this paper, we study the construction of a smooth ROC curve (or surface in the case of three populations) when there is a lower or upper limit of detection. We propose the use of spline models that incorporate monotonicity constraints for the cumulative hazard function of the marker distribution. The proposed technique is computationally stable and simulation results showed a satisfactory performance. Other observed covariates can be also accommodated by this spline-based approach.