A capture–recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 × 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture–recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture–recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

A capture-recapture approach for screening using two diagnostic tests with availability of disease status for the test positives only

The article considers screening human populations with two screening tests. If any of the two tests is positive, then full evaluation of the disease status is undertaken; however, if both diagnostic tests are negative, then disease status remains unknown. This procedure leads to a data constellation in which, for each disease status, the 2 x 2 table associated with the two diagnostic tests used in screening has exactly one empty, unknown cell. To estimate the unobserved cell counts, previous approaches assume independence of the two diagnostic tests and use specific models, including the special mixture model of Walter or unconstrained capture-recapture estimates. Often, as is also demonstrated in this article by means of a simple test, the independence of the two screening tests is not supported by the data. Two new estimators are suggested that allow associations of the screening test, although the form of association must be assumed to be homogeneous over disease status. These estimators are modifications of the simple capture-recapture estimator and easy to construct. The estimators are investigated for several screening studies with fully evaluated disease status in which the superior behavior of the new estimators compared to the previous conventional ones can be shown. Finally, the performance of the new estimators is compared with maximum likelihood estimators, which are more difficult to obtain in these models. The results indicate the loss of efficiency as minor.

Bayesian Estimation for Performance Measures of Two Diagnostic Tests in the Presence of Verification Bias

Sensitivity and specificity are measures that allow us to evaluate the performance of a diagnostic test. In practice, it is common to have situations where a proportion of selected individuals cannot have the real state of the disease verified, since the verification could be an invasive procedure, as occurs with biopsy. This happens, as a special case, in the diagnosis of prostate cancer, or in any other situation related to risks, that is, not practicable, nor ethical, or in situations with high cost. For this case, it is common to use diagnostic tests based only on the information of verified individuals. This procedure can lead to biased results or workup bias. In this paper, we introduce a Bayesian approach to estimate the sensitivity and the specificity for two diagnostic tests considering verified and unverified individuals, a result that generalizes the usual situation based on only one diagnostic test.

Comparing diagnostic tests with missing data

When missing data occur in studies designed to compare the accuracy of diagnostic tests, a common, though naive, practice is to base the comparison of sensitivity, specificity, as well as of positive and negative predictive values on some subset of the data that fits into methods implemented in standard statistical packages. Such methods are usually valid only under the strong missing completely at random (MCAR) assumption and may generate biased and less precise estimates. We review some models that use the dependence structure of the completely observed cases to incorporate the information of the partially categorized observations into the analysis and show how they may be fitted via a two-stage hybrid process involving maximum likelihood in the first stage and weighted least squares in the second. We indicate how computational subroutines written in R may be used to fit the proposed models and illustrate the different analysis strategies with observational data collected to compare the accuracy of three distinct non-invasive diagnostic methods for endometriosis. The results indicate that even when the MCAR assumption is plausible, the naive partial analyses should be avoided.

Accuracy of routine diagnostic tests used in paracoccidioidomycosis patients at a university hospital

The identification of appropriate laboratory measures to confirm clinical hypotheses is important in routine paracoccidioidomycosis medical care. The clinical records and laboratory reports of 401 paracoccidioidomycosis patients attended at the Tropical Diseases Area, Faculdade de Medicina de Botucatu, from 1974 to 2008 were reviewed. Direct mycological (DM), cell block (CB), histopathological (HP), and double immunodiffusion (DID) tests were evaluated before treatment. Typical Paracoccidioides brasiliensis yeast forms were observed in clinical specimens of 86% of the patients, but 14% were detected only by serological test. DM of 51 different tissue specimens produced 74.5% sensitivity, and 62.5% sensitivity was observed in 112 sputum samples. CB in 483 sputum samples generated 55.3% sensitivity. HP performed in 239 samples from different tissues revealed 96.7% sensitivity. Serology carried out in 351 patients and 200 healthy controls provided 90.0% sensitivity, 100.0% specificity, 100.0% positive predictive value, 85.1% negative predictive value and 93.6% accuracy. Comparisons of laboratory measurements performed in the same patient showed that sensitivity decreases from HP to DID to CB and DM, with the last two assays providing similar sensitivities. This study demonstrated that P. brasiliensis identification by HP, CB, and/or DM associated with DID is sufficient to establish the laboratorial diagnosis of paracoccidioidomycosis in practically all cases. (C) 2011 Royal Society of Tropical Medicine and Hygiene. Published by Elsevier Ltd. All rights reserved.

Diagnostic tests for hepatitis C: Recent trends in electrochemical immunosensor and genosensor analysis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Comparison of diferent diagnostic tests in dogs uninfected and naturally infected with visceral leishmaniasis

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A General Latent Class Model for Performance Evaluation of Diagnostic Tests in the Absence of a Gold Standard: An Application to Chagas Disease

We propose a new general Bayesian latent class model for evaluation of the performance of multiple diagnostic tests in situations in which no gold standard test exists based on a computationally intensive approach. The modeling represents an interesting and suitable alternative to models with complex structures that involve the general case of several conditionally independent diagnostic tests, covariates, and strata with different disease prevalences. The technique of stratifying the population according to different disease prevalence rates does not add further marked complexity to the modeling, but it makes the model more flexible and interpretable. To illustrate the general model proposed, we evaluate the performance of six diagnostic screening tests for Chagas disease considering some epidemiological variables. Serology at the time of donation (negative, positive, inconclusive) was considered as a factor of stratification in the model. The general model with stratification of the population performed better in comparison with its concurrents without stratification. The group formed by the testing laboratory Biomanguinhos FIOCRUZ-kit (c-ELISA and rec-ELISA) is the best option in the confirmation process by presenting false-negative rate of 0.0002% from the serial scheme. We are 100% sure that the donor is healthy when these two tests have negative results and he is chagasic when they have positive results.

Performance of six diagnostic tests to screen for Chagas disease in blood banks and prevalence of Trypanosoma cruzi infection among donors with inconclusive serology screening based on the analysis of epidemiological variables

OBJECTIVE: The frequent occurrence of inconclusive serology in blood banks and the absence of a gold standard test for Chagas'disease led us to examine the efficacy of the blood culture test and five commercial tests (ELISA, IIF, HAI, c-ELISA, rec-ELISA) used in screening blood donors for Chagas disease, as well as to investigate the prevalence of Trypanosoma cruzi infection among donors with inconclusive serology screening in respect to some epidemiological variables. METHODS: To obtain estimates of interest we considered a Bayesian latent class model with inclusion of covariates from the logit link. RESULTS: A better performance was observed with some categories of epidemiological variables. In addition, all pairs of tests (excluding the blood culture test) presented as good alternatives for both screening (sensitivity > 99.96% in parallel testing) and for confirmation (specificity > 99.93% in serial testing) of Chagas disease. The prevalence of 13.30% observed in the stratum of donors with inconclusive serology, means that probably most of these are non-reactive serology. In addition, depending on the level of specific epidemiological variables, the absence of infection can be predicted with a probability of 100% in this group from the pairs of tests using parallel testing. CONCLUSION: The epidemiological variables can lead to improved test results and thus assist in the clarification of inconclusive serology screening results. Moreover, all combinations of pairs using the five commercial tests are good alternatives to confirm results.

Accuracy of diagnostic tests for clinically suspected upper extremity deep vein thrombosis: a systematic review

The best available test for the diagnosis of upper extremity deep venous thrombosis (UEDVT) is contrast venography. The aim of this systematic review was to assess whether the diagnostic accuracy of other tests for clinically suspected UEDVT is high enough to justify their use in clinical practise and to evaluate if any test can replace venography.

Bivariate meta-analysis of predictive values of diagnostic tests can be an alternative to bivariate meta-analysis of sensitivity and specificity

Meta-analysis of predictive values is usually discouraged because these values are directly affected by disease prevalence, but sensitivity and specificity sometimes show substantial heterogeneity as well. We propose a bivariate random-effects logitnormal model for the meta-analysis of the positive predictive value (PPV) and negative predictive value (NPV) of diagnostic tests.

RANDOM EFFECTS MODELS IN A META-ANALYSIS OF THE ACCURACY OF DIAGNOSTIC TESTS WITHIN A GOLD STANDARD IN THE PRESENCE OF MISSING DATA

In evaluating the accuracy of diagnosis tests, it is common to apply two imperfect tests jointly or sequentially to a study population. In a recent meta-analysis of the accuracy of microsatellite instability testing (MSI) and traditional mutation analysis (MUT) in predicting germline mutations of the mismatch repair (MMR) genes, a Bayesian approach (Chen, Watson, and Parmigiani 2005) was proposed to handle missing data resulting from partial testing and the lack of a gold standard. In this paper, we demonstrate an improved estimation of the sensitivities and specificities of MSI and MUT by using a nonlinear mixed model and a Bayesian hierarchical model, both of which account for the heterogeneity across studies through study-specific random effects. The methods can be used to estimate the accuracy of two imperfect diagnostic tests in other meta-analyses when the prevalence of disease, the sensitivities and/or the specificities of diagnostic tests are heterogeneous among studies. Furthermore, simulation studies have demonstrated the importance of carefully selecting appropriate random effects on the estimation of diagnostic accuracy measurements in this scenario.

A review identifies and classifies reasons for ordering diagnostic tests

OBJECTIVE: To consider the reasons and context for test ordering by doctors when faced with an undiagnosed complaint in primary or secondary care. STUDY DESIGN AND SETTING: We reviewed any study of any design that discussed factors that may affect a doctor's decision to order a test. Articles were located through searches of electronic databases, authors' files on diagnostic methodology, and reference lists of relevant studies. We extracted data on: study design, type of analysis, setting, topic area, and any factors reported to influence test ordering. RESULTS: We included 37 studies. We carried out a thematic analysis to synthesize data. Five key groupings arose from this process: diagnostic factors, therapeutic and prognostic factors, patient-related factors, doctor-related factors, and policy and organization-related factors. To illustrate how the various factors identified may influence test ordering we considered the symptom low back pain and the diagnosis multiple sclerosis as examples. CONCLUSIONS: A wide variety of factors influence a doctor's decision to order a test. These are integral to understanding diagnosis in clinical practice. Traditional diagnostic accuracy studies should be supplemented with research into the broader context in which doctors perform their work.

Rapid diagnostic tests for the home-based management of malaria, in a high-transmission area

Rapid diagnostic tests (RDT) are sometimes recommended to improve the home-based management of malaria. The accuracy of an RDT for the detection of clinical malaria and the presence of malarial parasites has recently been evaluated in a high-transmission area of southern Mali. During the same study, the cost-effectiveness of a 'test-and-treat' strategy for the home-based management of malaria (based on an artemisinin-combination therapy) was compared with that of a 'treat-all' strategy. Overall, 301 patients, of all ages, each of whom had been considered a presumptive case of uncomplicated malaria by a village healthworker, were checked with a commercial RDT (Paracheck-Pf). The sensitivity, specificity, and positive and negative predictive values of this test, compared with the results of microscopy and two different definitions of clinical malaria, were then determined. The RDT was found to be 82.9% sensitive (with a 95% confidence interval of 78.0%-87.1%) and 78.9% (63.9%-89.7%) specific compared with the detection of parasites by microscopy. In the detection of clinical malaria, it was 95.2% (91.3%-97.6%) sensitive and 57.4% (48.2%-66.2%) specific compared with a general practitioner's diagnosis of the disease, and 100.0% (94.5%-100.0%) sensitive but only 30.2% (24.8%-36.2%) specific when compared against the fulfillment of the World Health Organization's (2003) research criteria for uncomplicated malaria. Among children aged 0-5 years, the cost of the 'test-and-treat' strategy, per episode, was about twice that of the 'treat-all' (U.S.$1.0. v. U.S.$0.5). In older subjects, however, the two strategies were equally costly (approximately U.S.$2/episode). In conclusion, for children aged 0-5 years in a high-transmission area of sub-Saharan Africa, use of the RDT was not cost-effective compared with the presumptive treatment of malaria with an ACT. In older patients, use of the RDT did not reduce costs. The question remains whether either of the strategies investigated can be made affordable for the affected population.

Natural anti-FcepsilonRIalpha autoantibodies may interfere with diagnostic tests for autoimmune urticaria.

IgG autoantibodies against the alpha-chain of the high affinity IgE receptor are claimed to play a pathogenetic role in autoimmune urticaria. The best methods for detection of functional autoantibodies are currently the autologous serum skin test and the basophil histamine release assay. A simplified and feasible screening test would facilitate the diagnosis of autoimmune urticaria. Here we offer an explanation for the difficulties in establishing a screening test for autoantibodies directed against the alpha-chain of the high affinity IgE receptor in autoimmune urticaria. Identical autoantibodies in chronic urticaria patients and healthy donors belonging to the natural autoantibody repertoire were found by sequence analysis of anti-alpha-chain autoantibodies isolated by repertoire cloning from antibody libraries. These natural autoantibodies bound to the receptor and triggered histamine release but only if IgE was previously removed from the receptor. Diagnostic assays used for detection of antibodies directed against the IgE receptor may require signal comparison with and without the artificial removal of IgE, immune complexes, and complement in order to avoid false positive or negative results. After IgE removal diagnostic tests will detect natural autoantibodies against the high affinity IgE receptor regardless of whether they are pathogenic or not.