***TASK*** the task is to summarize an input biomedical literature in six sentences 
    ***INPUT*** the input is a biomedical literature 
    ***OUTPUT*** the output is the summary of an input biomedical literature in six sentences 
    ***DOCUMENTATION*** 
    ***EXAMPLES*** 
    Input: A receiver operating characteristic ( ROC ) curve is commonly used to measure the accuracy of a medical test . It is a plot of the true positive fraction ( sensitivity ) against the false positive fraction ( 1-specificity ) for increasingly stringent positivity criterion . Bias can occur in estimation of an ROC curve if only some of the tested patients are selected for disease verification and if analysis is restricted only to the verified cases . This bias is known as verification bias . In this paper , we address the problem of correcting for verification bias in estimation of an ROC curve when the verification process and efficacy of the diagnostic test depend on covariates . Our method applies the EM algorithm to ordinal regression models to derive ML estimates for ROC curves as a function of covariates , adjusted for covariates affecting the likelihood of being verified . Asymptotic variance estimates are obtained using the observed information matrix of the observed data . These estimates are derived under the missing-at-r and om assumption , which means that selection for disease verification depends only on the observed data , i.e. , the test result and the observed covariates . We also address the issues of model selection and model checking . Finally , we illustrate the proposed method on data from a two-phase study of dementia disorders , where selection for verification depends on the screening test result and age The receiver-operating characteristic surface is frequently used for presenting the accuracy of a diagnostic test for three-category classification problems . One common problem that can complicate the estimation of the volume under receiver-operating characteristic surface is that not all subjects receive the verification of the true disease status . Estimation based only on data from subjects with verified disease status may be biased , which is referred to as verification bias . In this article , we propose new verification bias correction methods to estimate the volume under receiver-operating characteristic surface for a continuous diagnostic test . We assume the verification process is missing not at r and om , which means the missingness might be related to unobserved clinical characteristics . Three classes of estimators are proposed , namely , inverse probability weighted , imputation-based , and doubly robust estimators . A jackknife estimator of variance is derived for all the proposed volume under receiver-operating characteristic surface estimators . The finite sample properties of the new estimators are examined via simulation studies . We illustrate our methods with data collected from Alzheimer ’s disease research Results obtained using a reference st and ard may be missing for some participants in diagnostic accuracy studies . This paper looks at methods for dealing with such missing data when design ing or conducting a prospect i ve diagnostic accuracy BACKGROUND & AIMS Liver fibrosis stage is traditionally assessed with biopsy , an imperfect gold st and ard . Two widely used techniques , FibroTest ® , and  liver stiffness measurement ( LSM ) using Fibroscan ® have been vali date d using biopsy , and therefore the true performances of these estimates are still unknown in the absence of a perfect reference . The aim was to assess the relative accuracy of  FibroTest , LSM  , and biopsy  using methods without gold st and ard in patients with chronic hepatitis C ( CHC ) and controls . METHODS A total of 1289 patients with CHC and 604 healthy volunteers , with assessment of fibrosis stage by the three techniques , and alanine aminotransferase ( ALT ) taken as a control test , were analyzed by latent class method with r and om effects . In the volunteers , the false positive risk of biopsy was obtained from a large surgical sample of four normal livers . RESULTS The latent class model with r and om effects permitted to conciliate the observed data and estimates of test performances . For advanced fibrosis , the specificity/sensitivity was for FibroTest 0.93/0.70 , LSM 0.96/0.45 , ALT 0.79/0.78 and biopsy 0.67/0.63 , and for cirrhosis FibroTest 0.87/0.41 , LSM 0.93/0.39 , ALT 0.78/0.08 and biopsy 0.95/0.51 . The analysis of the discordances between pairs suggested that the variability of the model was mainly related to the discordances between biopsy and LSM ( residuals>10 ; p<0.0001 ) . CONCLUSIONS A method without the use of a gold st and ard confirmed the accuracy of FibroTest and Fibroscan for the diagnosis of advanced fibrosis and  cirrhosis in patients with chronic hepatitis C. The variability of the model was mostly due to the discordances between Fibroscan and biopsy Abstract For a continuous-scale diagnostic test , the receiver operating characteristic ( ROC ) curve is a popular tool for displaying the ability of the test to discriminate between healthy and diseased subjects . In some studies , verification of the true disease status is performed only for a subset of subjects , possibly depending on the test result and other characteristics of the subjects . Estimators of the ROC curve based only on this subset of subjects are typically biased ; this is known as verification bias . Methods have been proposed to correct verification bias , in particular under the assumption that the true disease status , if missing , is missing at r and om ( MAR ) . MAR assumption means that the probability of missingness depends on the true disease status only through the test result and observed covariate information . However , the existing methods require parametric models for the ( conditional ) probability of disease and /or the ( conditional ) probability of verification , and hence are subject to model misspecification : a wrong specification of such parametric models can affect the behavior of the estimators , which can be inconsistent . To avoid misspecification problems , in this paper we propose a fully nonparametric method for the estimation of the ROC curve of a continuous test under verification bias . The method is based on nearest-neighbor imputation and adopts generic smooth regression models for both the probability that a subject is diseased and the probability that it is verified . Simulation experiments and an illustrative example show the usefulness of the new method . Variance estimation is also discussed In ROC analysis , covariate adjustment is advocated when the covariates impact the magnitude or accuracy of the test under study . Meanwhile , for many large scale screening tests , the true condition status may be subject to missingness because it is expensive and /or invasive to ascertain the disease status . The complete-case analysis may end up with a biased inference , also known as " verification bias . " To address the issue of covariate adjustment with verification bias in ROC analysis , we propose several estimators for the area under the covariate-specific and covariate-adjusted ROC curves ( AUCx and AAUC ) . The AUCx is directly modeled in the form of binary regression , and the estimating equations are based on the U statistics . The AAUC is estimated from the weighted average of AUCx over the covariate distribution of the diseased subjects . We employ reweighting and imputation techniques to overcome the verification bias problem . Our proposed estimators are initially derived assuming that the true disease status is missing at r and om ( MAR ) , and then with some modification , the estimators can be extended to the not missing at r and om ( NMAR ) situation . The asymptotic distributions are derived for the proposed estimators . The finite sample performance is evaluated by a series of simulation studies . Our method is applied to a data set in Alzheimer 's disease research OBJECTIVE To determine the accuracy of an immunochromatographic cartridge ( IC ) test for blood typing feline type A , B , and AB blood sample s. DESIGN Prospect i ve observational study . SETTING University teaching hospital . SAMPLE S Fifty-one nonanemic and 19 anemic feline blood sample s. INTERVENTIONS Sample s were blood typed by both card agglutination ( CA ) and IC techniques . Discordant results were analyzed using a back-typing technique for the presence of alloantibodies . Repeatability and reproducibility of the IC method were evaluated . Accuracy of the IC method was determined for feline whole blood anticoagulated with EDTA and citrate phosphate dextrose adenine ( CPDA1 ) , for feline-packed RBCs with CPDA1 and saline adenine glucose-mannitol , and for autoagglutinated and hemolytic sample s. Accuracy of IC testing was determined for feline blood after room temperature , 4 ± 2 , -20 , and -80 ° C storage following 24 , 48 , 72 , and 96 hours , and after 1 , 2 , 3 , and 4 weeks of storage . MEASUREMENTS AND MAIN RESULTS In anemic and nonanemic sample s the IC technique had a specificity , sensitivity , positive predictive value , and negative predictive value of 100 % in detecting feline blood types A , B , and AB , outperforming a CA test . Results were repeatable and reproducible . Using IC it was possible to blood type sample s anticoagulated with EDTA and CPDA1 , packed RBC sample s with CPDA1 and saline adenine glucose-mannitol , autoagglutinated and hemolytic sample s , and sample s stored at 4 ± 2 ° C and at room temperature for up to 1 month . CONCLUSIONS The IC technique is an accurate assay for the identification of A , B , and AB blood types in anemic and nonanemic feline blood . It has a higher sensitivity and specificity than the CA test , and can be used in sample s stored with common anticoagulants or preservative solutions used in feline transfusion medicine In diagnostic medicine , estimating the diagnostic accuracy of a group of raters or medical tests relative to the gold st and ard is often the primary goal . When a gold st and ard is absent , latent class models where the unknown gold st and ard test is treated as a latent variable are often used . However , these models have been criticized in the literature from both a conceptual and a robustness perspective . As an alternative , we propose an approach where we exploit an imperfect reference st and ard with unknown diagnostic accuracy and conduct sensitivity analysis by varying this accuracy over scientifically reasonable ranges . In this article , a latent class model with crossed r and om effects is proposed for estimating the diagnostic accuracy of regional obstetrics and gynaecological ( OB/GYN ) physicians in diagnosing endometriosis . To avoid the pitfalls of models without a gold st and ard , we exploit the diagnostic results of a group of OB/GYN physicians with an international reputation for the diagnosis of endometriosis . We construct an ordinal reference st and ard based on the discordance among these international experts and propose a mechanism for conducting sensitivity analysis relative to the unknown diagnostic accuracy among them . A Monte Carlo EM algorithm is proposed for parameter estimation and a BIC-type model selection procedure is presented . Through simulations and data analysis we show that this new approach provides a useful alternative to traditional latent class modeling approaches used in this setting BACKGROUND A physician is frequently unable to distinguish bacterial from viral infections . ImmunoXpert is a novel assay combining three proteins : tumour necrosis factor-related apoptosis-inducing lig and ( TRAIL ) , interferon gamma induced protein-10 ( IP-10 ) , and C-reactive protein ( CRP ) . We aim ed to externally vali date the diagnostic accuracy of this assay in differentiating between bacterial and viral infections and to compare this test with commonly used biomarkers . METHODS In this prospect i ve , double-blind , international , multicentre study , we recruited children aged 2 - 60 months with lower respiratory tract infection or clinical presentation of fever without source at four hospitals in the Netherl and s and two hospitals in Israel . A panel of three experienced paediatricians adjudicated a reference st and ard diagnosis for all patients ( ie , bacterial or viral infection ) using all available clinical and laboratory information , including a 28-day follow-up assessment . The panel was masked to the assay results . We identified majority diagnosis when two of three panel members agreed on a diagnosis and unanimous diagnosis when all three panel members agreed on the diagnosis . We calculated the diagnostic performance ( ie , sensitivity , specificity , positive predictive value , and negative predictive value ) of the index test in differentiating between bacterial ( index test positive ) and viral ( index test negative ) infection by comparing the test classification with the reference st and ard outcome . FINDINGS Between Oct 16 , 2013 and March 1 , 2015 , we recruited 777 children , of whom 577 ( mean age 21 months , 56 % male ) were assessed . The majority of the panel diagnosed 71 cases as bacterial infections and 435 as viral infections . In another 71 patients there was an inconclusive panel diagnosis . The assay distinguished bacterial from viral infections with a sensitivity of 86·7 % ( 95 % CI 75·8 - 93·1 ) , a specificity of 91·1 % ( 87·9 - 93·6 ) , a positive predictive value of 60·5 % ( 49·9 - 70·1 ) , and a negative predictive value of 97·8 % ( 95·6 - 98·9 ) . In the more clear cases with unanimous panel diagnosis ( n=354 ) , sensitivity was 87·8 % ( 74·5 - 94·7 ) , specificity 93·0 % ( 89·6 - 95·3 ) , positive predictive value 62·1 % ( 49·2 - 73·4 ) , and negative predictive value 98·3 % ( 96·1 - 99·3 ) . INTERPRETATION This external validation study shows the diagnostic value of a three-host protein-based assay to differentiate between bacterial and viral infections in children with lower respiratory tract infection or fever without source . This diagnostic based on CRP , TRAIL , and IP-10 has the potential to reduce antibiotic misuse in young children . FUNDING MeMed Diagnostics Sensitivity and specificity are common measures of the accuracy of a diagnostic test . The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample . In some studies , verification of the true disease status is performed only for a subset of subjects , possibly depending on the result of the diagnostic test and other characteristics of the subjects . Estimators of sensitivity and specificity based on this subset of subjects are typically biased ; this is 
    Output:
    
Conclusion Various methods have been proposed to evaluate medical test(s ) in the absence of a gold st and ard for some or all participants in a diagnostic accuracy study .