Modeling conditional dependence among multiple diagnostic tests

Wang, Zhuoyu; Dendukuri, Nandini; Zar, Heather J.; Joseph, Lawrence

doi:10.1002/sim.7449

Cited by 24 publications

(28 citation statements)

References 16 publications

(79 reference statements)

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“…The conditional independence assumption can be relaxed by extending the model to add correlated errors, as and when there are extra df available for fitting, or possibly by assuming correlation values to be known, 28‐30 by making alternative assumptions, 31 or by adopting Bayesian methods 32‐34 . In fact, under the reasonable assumption of exchangeability of the repeated observations (ie, whenever their order is not relevant), it should be sufficient to add just one extra parameter to represent the correlation between any given pair of tests on an individual.…”

Section: Discussionmentioning

confidence: 99%

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Walter

2021

Statistics in Medicine

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This article considers how to estimate the accuracy of a diagnostic test when there are repeated observations, but without the availability of a gold standard or reference test. We identify conditions under which the structure of the observed data is rich enough to provide sufficient degrees of freedom, such that a suitable latent class model can be fitted with identifiable accuracy parameters. We show that a Rule of Three applies, specifying that accuracy can be evaluated as long as there are at least three observations per individual with the given test. This rule also applies if the three observations arise from combinations of different test methods, or from a sequential design in which individuals are tested for a maximum number of times with the same test but stopping if a positive (or negative) result occurs. The rule pertains to tests having an arbitrary number of response categories. Accuracy is evaluated by parameters reflecting rates of misclassification among the response categories, and the model also provides estimates of the underlying distribution of the true disease state. These ideas are illustrated by data from two medical studies. Issues discussed include the advantages and disadvantages of analyzing the response variable as binary or multinomial, as well as the feasibility of testing goodness of fit when the model incorporates a large number of parameters. Comparisons are possible between models that do or do not assume equal accuracy rates for the observations, and between models where certain misclassification parameters are or are not assumed to be zero.

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Section: Discussionmentioning

confidence: 99%

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Walter

2021

Statistics in Medicine

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“…To mitigate that bias and incorporate variability due to error in the testing results, we will take a Bayesian latent class approach for modeling multiple diagnostic tests. Our approach will be based on combining a fixed effects framework for modeling conditional dependence across multiple diagnostic tests (22,23) with a model-based analysis using multilevel regression and poststratification (24) to acknowledge the complex design aspects of the survey.…”

Section: Methodsmentioning

confidence: 99%

“…This latent indicator of prior infection is our primary outcome of interest. Analysis methods for multiple diagnostic tests without a gold standard hinge on assumptions related to conditional independence (23). We will assume that (T i1 , T i2 ) and T i3 are independent given the true infection status.…”

Section: Modelmentioning

confidence: 99%

Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests

Kline

Chu

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

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Globally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has infected more than 59 million people and killed more than 1.39 million. Designing and monitoring interventions to slow and stop the spread of the virus require knowledge of how many people have been and are currently infected, where they live, and how they interact. The first step is an accurate assessment of the population prevalence of past infections. There are very few population-representative prevalence studies of SARS-CoV-2 infections, and only two states in the United States—Indiana and Connecticut—have reported probability-based sample surveys that characterize statewide prevalence of SARS-CoV-2. One of the difficulties is the fact that tests to detect and characterize SARS-CoV-2 coronavirus antibodies are new, are not well characterized, and generally function poorly. During July 2020, a survey representing all adults in the state of Ohio in the United States collected serum samples and information on protective behavior related to SARS-CoV-2 and coronavirus disease 2019 (COVID-19). Several features of the survey make it difficult to estimate past prevalence: 1) a low response rate; 2) a very low number of positive cases; and 3) the fact that multiple poor-quality serological tests were used to detect SARS-CoV-2 antibodies. We describe a Bayesian approach for analyzing the biomarker data that simultaneously addresses these challenges and characterizes the potential effect of selective response. The model does not require survey sample weights; accounts for multiple imperfect antibody test results; and characterizes uncertainty related to the sample survey and the multiple imperfect, potentially correlated tests.

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“…Theoretical results on the performance of the diagnostic tests under multiple test repetitions can be found, among many others, in Albert ( 2007 ), Chiang ( 1951 ), Hui and Zhou ( 1988 ), Lachenbruch ( 1988 ), Nissen-Meyer ( 1964 ), Politser ( 1982 ), Wang et al. ( 2017 ), Wang and Hanson ( 2019 ).…”

Section: Introductionmentioning

confidence: 99%

“… 2014 ), childhood pulmonary tuberculosis (Wang et al. 2017 ), Alzheimer’s disease as well as myocardial infarction (Hui and Zhou 1988 ), or the determination of the hydroxymethylation status of a given cytosine (Slynko and Benner 2019 ).…”

Section: Introductionmentioning

confidence: 99%

Asymptotic analysis of reliability measures for an imperfect dichotomous test

Slynko

2021

Stat Papers

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To access the reliability of a new dichotomous test and to capture the random variability of its results in the absence of a gold standard, two measures, the inconsistent acceptance probability (IAP) and inconsistent rejection probability (IRP), were introduced in the literature. In this paper, we first analyze the limiting behavior of both measures as the number of test repetitions increases and derive the corresponding accuracy estimates and rates of convergence. To overcome possible limitations of IRP and IAP, we then introduce a one-parameter family of refined reliability measures, . Such measures characterize the consistency of the results of a dichotomous test in the absence of a gold standard as the threshold for a positive aggregate test result varies. Similar to IRP and IAP, we also derive corresponding accuracy estimates and rates of convergence for as the number k of test repetitions increases. Supplementary Information The online version supplementary material available at 10.1007/s00362-021-01266-9.

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Modeling conditional dependence among multiple diagnostic tests

Cited by 24 publications

References 16 publications

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Estimation of diagnostic test accuracy: A “Rule of Three” for data with repeated observations but without a gold standard

Estimating seroprevalence of SARS-CoV-2 in Ohio: A Bayesian multilevel poststratification approach with multiple diagnostic tests

Asymptotic analysis of reliability measures for an imperfect dichotomous test

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