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Xiao-hua Zhou - One of the best experts on this subject based on the ideXlab platform.
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covariate adjustment in estimating the area under roc curve with partially missing gold standard
Biometrics, 2013Co-Authors: Danping Liu, Xiao-hua ZhouAbstract: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 random (MAR), and then with some modification, the estimators can be extended to the not missing at random (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.
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A model for adjusting for nonignorable Verification Bias in estimation of the ROC curve and its area with likelihood-based approach.
Biometrics, 2010Co-Authors: Danping Liu, Xiao-hua ZhouAbstract:In estimation of the ROC curve, when the true disease status is subject to nonignorable missingness, the observed likelihood involves the missing mechanism given by a selection model. In this article, we proposed a likelihood-based approach to estimate the ROC curve and the area under the ROC curve when the Verification Bias is nonignorable. We specified a parametric disease model in order to make the nonignorable selection model identifiable. With the estimated Verification and disease probabilities, we constructed four types of empirical estimates of the ROC curve and its area based on imputation and reweighting methods. In practice, a reasonably large sample size is required to estimate the nonignorable selection model in our settings. Simulation studies showed that all four estimators of ROC area performed well, and imputation estimators were generally more efficient than the other estimators proposed. We applied the proposed method to a data set from research in Alzheimer's disease.
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Receiver operating characteristic surfaces in the presence of Verification Bias
Journal of the Royal Statistical Society: Series C (Applied Statistics), 2008Co-Authors: Yueh Yun Chi, Xiao-hua ZhouAbstract:on initial test measurements and induces Verification Bias in the assessment. We propose a non parametric likelihood-based approach to construct the empirical ROC surface in the presence of differential Verification, and to estimate the volume under the ROC surface. Estimators of the standard deviation are derived by both the Fisher information and the jackknife method, and their relative accuracy is evaluated in an extensive simulation study. The methodology is further extended to incorporate discrete baseline covariates in the selection process, and to compare the accuracy of a pair of diagnostic tests. We apply the proposed method to compare the diag nostic accuracy between mini-mental state examination and clinical evaluation of dementia, in discriminating between three disease states of Alzheimer's disease.
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Multiple imputation for correcting Verification Bias.
Statistics in Medicine, 2006Co-Authors: Ofer Harel, Xiao-hua ZhouAbstract:In the case in which all subjects are screened using a common test and only a subset of these subjects are tested using a golden standard test, it is well documented that there is a risk for Bias, called Verification Bias. When the test has only two levels (e.g. positive and negative) and we are trying to estimate the sensitivity and specificity of the test, we are actually constructing a confidence interval for a binomial proportion. Since it is well documented that this estimation is not trivial even with complete data, we adopt multiple imputation framework for Verification Bias problem. We propose several imputation procedures for this problem and compare different methods of estimation. We show that our imputation methods are better than the existing methods with regard to nominal coverage and confidence interval length. Copyright © 2006 John Wiley & Sons, Ltd.
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Multiple Imputation for Correcting Verification Bias
Statistics in medicine, 2006Co-Authors: Ofer Harel, Xiao-hua ZhouAbstract:In the case in which all subjects are screened using a common test and only a subset of these subjects are tested using a golden standard test, it is well documented that there is a risk for Bias, called Verification Bias. When the test has only two levels (e.g. positive and negative) and we are trying to estimate the sensitivity and specificity of the test, we are actually constructing a confidence interval for a binomial proportion. Since it is well documented that this estimation is not trivial even with complete data, we adopt multiple imputation framework for Verification Bias problem. We propose several imputation procedures for this problem and compare different methods of estimation. We show that our imputation methods are better than the existing methods with regard to nominal coverage and confidence interval length.
Johannes B. Reitsma - One of the best experts on this subject based on the ideXlab platform.
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Reference standards in diagnostic research: problems and solutions
Nederlands tijdschrift voor geneeskunde, 2014Co-Authors: Joris A.h. De Groot, Johannes B. Reitsma, Karel G.m. MoonsAbstract:The accuracy of diagnostic tests is of utmost importance as Biased test results may lead to wrong decisions in clinical practice. In diagnostic accuracy research the results of a diagnostic test, model or strategy are compared to those of the reference standard, i.e. the best available method to determine whether a certain condition or disease is present or absent. Problems with the reference standard lead to Biased test results. The umbrella term for this is 'Verification Bias'. Verification Bias arises if the reference standard cannot be applied to all patients, if investigators use different reference standards or simply because there is no reference standard. Correction of these problems is often possible, and, if it is applied in a transparent and reproducible fashion it will deliver useful diagnostic information. Clinicians who use a diagnostic test should take possible Verification Bias into account.
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Referentiestandaarden in diagnostisch onderzoek: problemen en oplossingen
Nederlands Tijdschrift voor Geneeskunde, 2014Co-Authors: Joris A.h. De Groot, Johannes B. Reitsma, Karel G.m. MoonsAbstract:The accuracy of diagnostic tests is of utmost importance as Biased test results may lead to wrong decisions in clinical practice. In diagnostic accuracy research the results of a diagnostic test, model or strategy are compared to those of the reference standard, i.e. the best available method to determine whether a certain condition or disease is present or absent. Problems with the reference standard lead to Biased test results. The umbrella term for this is 'Verification Bias'. Verification Bias arises if the reference standard cannot be applied to all patients, if investigators use different reference standards or simply because there is no reference standard. Correction of these problems is often possible, and, if it is applied in a transparent and reproducible fashion it will deliver useful diagnostic information. Clinicians who use a diagnostic test should take possible Verification Bias into account
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Adjusting for Partial Verification or Workup Bias in Meta-Analyses of Diagnostic Accuracy Studies
American journal of epidemiology, 2012Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Johannes B. Reitsma, Nandini Dendukuri, James M. Brophy, Lawrence Joseph, Karel G.m. MoonsAbstract:A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can Bias the accuracy estimates. Numerous methods have been described for correcting this partial Verification Bias or workup Bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial Verification or workup Bias is present in a subset of the primary studies. The method corrects for Verification Bias without having to exclude primary studies with Verification Bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with Verification Bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients.
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Adjusting for differential-Verification Bias in diagnostic-accuracy studies: a Bayesian approach.
Epidemiology (Cambridge Mass.), 2011Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Johannes B. Reitsma, Nandini Dendukuri, Karel G.m. MoonsAbstract:In studies of diagnostic accuracy, the performance of an index test is assessed by verifying its results against those of a reference standard. If Verification of index-test results by the preferred reference standard can be performed only in a subset of subjects, an alternative reference test could be given to the remainder. The drawback of this so-called differential-Verification design is that the second reference test is often of lesser quality, or defines the target condition in a different way. Incorrectly treating results of the 2 reference standards as equivalent will lead to differential-Verification Bias. The Bayesian methods presented in this paper use a single model to (1) acknowledge the different nature of the 2 reference standards and (2) make simultaneous inferences about the population prevalence and the sensitivity, specificity, and predictive values of the index test with respect to both reference tests, in relation to latent disease status. We illustrate this approach using data from a study on the accuracy of the elbow extension test for diagnosis of elbow fractures in patients with elbow injury, using either radiography or follow-up as reference standards.
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Correcting for partial Verification Bias: a comparison of methods.
Annals of epidemiology, 2010Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Aeilko H. Zwinderman, Johannes B. Reitsma, Karel G.m. MoonsAbstract:Purpose A common problem in diagnostic research is that the reference standard has not been carried out in all patients. This partial Verification may lead to Biased accuracy measures of the test under study. The authors studied the performance of multiple imputation and the conventional correction method proposed by Begg and Greenes under a range of different situations of partial Verification. Methods In a series of simulations, using a previously published deep venous thrombosis data set (n = 1292), the authors set the outcome of the reference standard to missing based on various underlying mechanisms and by varying the total number of missing values. They then compared the performance of the different correction methods. Results The results of the study show that when the mechanism of missing reference data is known, accuracy measures can easily be correctly adjusted using either the Begg and Greenes method, or multiple imputation. In situations where the mechanism of missing reference data is complex or unknown, we recommend using multiple imputation methods to correct. Conclusions These methods can easily apply for both continuous and categorical variables, are readily available in statistical software and give reliable estimates of the missing reference data.
Todd A. Alonzo - One of the best experts on this subject based on the ideXlab platform.
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Estimation of the volume under the receiver-operating characteristic surface adjusting for non-ignorable Verification Bias.
Statistical methods in medical research, 2018Co-Authors: Ying Zhang, Todd A. Alonzo, Alzheimer’s Disease Neuroimaging InitiativeAbstract: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 random, 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.
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Inverse probability weighting estimation of the volume under the ROC surface in the presence of Verification Bias.
Biometrical journal. Biometrische Zeitschrift, 2016Co-Authors: Ying Zhang, Todd A. AlonzoAbstract:In diagnostic medicine, the volume under the receiver operating characteristic (ROC) surface (VUS) is a commonly used index to quantify the ability of a continuous diagnostic test to discriminate between three disease states. In practice, Verification of the true disease status may be performed only for a subset of subjects under study since the Verification procedure is invasive, risky, or expensive. The selection for disease examination might depend on the results of the diagnostic test and other clinical characteristics of the patients, which in turn can cause Bias in estimates of the VUS. This Bias is referred to as Verification Bias. Existing Verification Bias correction in three-way ROC analysis focuses on ordinal tests. We propose Verification Bias-correction methods to construct ROC surface and estimate the VUS for a continuous diagnostic test, based on inverse probability weighting. By applying U-statistics theory, we develop asymptotic properties for the estimator. A Jackknife estimator of variance is also derived. Extensive simulation studies are performed to evaluate the performance of the new estimators in terms of Bias correction and variance. The proposed methods are used to assess the ability of a biomarker to accurately identify stages of Alzheimer's disease.
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Estimates of sensitivity and specificity can be Biased when reporting the results of the second test in a screening trial conducted in series
BMC Medical Research Methodology, 2010Co-Authors: Brandy M Ringham, Todd A. Alonzo, Gary K Grunwald, Deborah H GlueckAbstract:Background Cancer screening reduces cancer mortality when early detection allows successful treatment of otherwise fatal disease. There are a variety of trial designs used to find the best screening test. In a series screening trial design, the decision to conduct the second test is based on the results of the first test. Thus, the estimates of diagnostic accuracy for the second test are conditional, and may differ from unconditional estimates. The problem is further complicated when some cases are misclassified as non-cases due to incomplete disease status ascertainment. Methods For a series design, we assume that the second screening test is conducted only if the first test had negative results. We derive formulae for the conditional sensitivity and specificity of the second test in the presence of differential Verification Bias. For comparison, we also derive formulae for the sensitivity and specificity for a single test design, both with and without differential Verification Bias. Results Both the series design and differential Verification Bias have strong effects on estimates of sensitivity and specificity. In both the single test and series designs, differential Verification Bias inflates estimates of sensitivity and specificity. In general, for the series design, the inflation is smaller than that observed for a single test design. The degree of Bias depends on disease prevalence, the proportion of misclassified cases, and on the correlation between the test results for cases. As disease prevalence increases, the observed conditional sensitivity is unaffected. However, there is an increasing upward Bias in observed conditional specificity. As the proportion of correctly classified cases increases, the upward Bias in observed conditional sensitivity and specificity decreases. As the agreement between the two screening tests becomes stronger, the upward Bias in observed conditional sensitivity decreases, while the specificity Bias increases. Conclusions In a series design, estimates of sensitivity and specificity for the second test are conditional estimates. These estimates must always be described in context of the design of the trial, and the study population, to prevent misleading comparisons. In addition, these estimates may be Biased by incomplete disease status ascertainment.
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Bias in trials comparing paired continuous tests can cause researchers to choose the wrong screening modality.
BMC medical research methodology, 2009Co-Authors: Deborah H Glueck, Todd A. Alonzo, Brandy M Ringham, Molly M. Lamb, Colin O'donnell, John T. Brinton, Keith E. Muller, John M. Lewin, Etta D. PisanoAbstract:To compare the diagnostic accuracy of two continuous screening tests, a common approach is to test the difference between the areas under the receiver operating characteristic (ROC) curves. After study participants are screened with both screening tests, the disease status is determined as accurately as possible, either by an invasive, sensitive and specific secondary test, or by a less invasive, but less sensitive approach. For most participants, disease status is approximated through the less sensitive approach. The invasive test must be limited to the fraction of the participants whose results on either or both screening tests exceed a threshold of suspicion, or who develop signs and symptoms of the disease after the initial screening tests. The limitations of this study design lead to a Bias in the ROC curves we call paired screening trial Bias. This Bias reflects the synergistic effects of inappropriate reference standard Bias, differential Verification Bias, and partial Verification Bias. The absence of a gold reference standard leads to inappropriate reference standard Bias. When different reference standards are used to ascertain disease status, it creates differential Verification Bias. When only suspicious screening test scores trigger a sensitive and specific secondary test, the result is a form of partial Verification Bias. For paired screening tests with bivariate normally distributed scores, we give formulae and programs to quantify the effect of paired screening trial Bias on a paired comparison of area under the curves. We fix the prevalence of disease, and the chance a diseased subject manifests signs and symptoms. We derive the formulas for true sensitivity and specificity, and those for the sensitivity and specificity observed by the study investigator. The observed area under the ROC curves is quite different from the true area under the ROC curves. The typical direction of the Bias is a strong inflation in sensitivity, paired with a concomitant slight deflation of specificity. In paired trials of screening tests, when area under the ROC curve is used as the metric, Bias may lead researchers to make the wrong decision as to which screening test is better.
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Bias in trials comparing paired continuous tests can cause researchers to choose the wrong screening modality
BMC Medical Research Methodology, 2009Co-Authors: Deborah H Glueck, Todd A. Alonzo, Brandy M Ringham, Molly M. Lamb, John T. Brinton, Keith E. Muller, John M. Lewin, Colin I O'donnell, Etta D. PisanoAbstract:Background To compare the diagnostic accuracy of two continuous screening tests, a common approach is to test the difference between the areas under the receiver operating characteristic (ROC) curves. After study participants are screened with both screening tests, the disease status is determined as accurately as possible, either by an invasive, sensitive and specific secondary test, or by a less invasive, but less sensitive approach. For most participants, disease status is approximated through the less sensitive approach. The invasive test must be limited to the fraction of the participants whose results on either or both screening tests exceed a threshold of suspicion, or who develop signs and symptoms of the disease after the initial screening tests. The limitations of this study design lead to a Bias in the ROC curves we call paired screening trial Bias . This Bias reflects the synergistic effects of inappropriate reference standard Bias, differential Verification Bias, and partial Verification Bias. The absence of a gold reference standard leads to inappropriate reference standard Bias. When different reference standards are used to ascertain disease status, it creates differential Verification Bias. When only suspicious screening test scores trigger a sensitive and specific secondary test, the result is a form of partial Verification Bias. Methods For paired screening tests with bivariate normally distributed scores, we give formulae and programs to quantify the effect of paired screening trial Bias on a paired comparison of area under the curves. We fix the prevalence of disease, and the chance a diseased subject manifests signs and symptoms. We derive the formulas for true sensitivity and specificity, and those for the sensitivity and specificity observed by the study investigator. Results The observed area under the ROC curves is quite different from the true area under the ROC curves. The typical direction of the Bias is a strong inflation in sensitivity, paired with a concomitant slight deflation of specificity. Conclusion In paired trials of screening tests, when area under the ROC curve is used as the metric, Bias may lead researchers to make the wrong decision as to which screening test is better.
J. A. H. De Groot - One of the best experts on this subject based on the ideXlab platform.
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Diagnostic research in the presence of an incomplete or imperfect reference standard
2011Co-Authors: J. A. H. De GrootAbstract:Diagnostic accuracy research is a vital step in the evaluation of new diagnostic technologies. It is the ability of a test to correctly discriminate between patients that have and do not have the target disease. In studies of diagnostic accuracy, results of the tests under study are compared with results of a reference standard applied to the same patients. In this research framework, the reference standard is the best available method to verify the presence or absence of the target disease, and thus provides the final classification of patients into target disease present or absent. This process is known as disease Verification. Ideally, the reference standard provides error-free disease classification. In some situations, it is not possible to verify the disease outcome with the preferred reference standard in all patients or any patient at all. Failure to apply the reference standard may result in various types of disease Verification problems. Biased and exaggerated estimates of accuracy of a test can lead to inefficiencies in testing in clinical practice, unnecessary costs, and could trigger physicians to making incorrect treatment decisions. This thesis examines the problem of Verification Bias in studies of diagnostic accuracy. In particular, we aimed to investigate the available methods to alleviate the various problems of Verification Bias and, more importantly, to improve the methodology and analysis of primary diagnostic accuracy studies and diagnostic meta-analyses in the presence of various forms of Verification Bias.
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Multiple imputation to correct for partial Verification Bias revisited (5880–5889)
Statistics in Medicine, 2009Co-Authors: J. A. H. De Groot, Kristel J.m. Janssen, Aeilko H. Zwinderman, K. G. M. Moons, Johannes B. ReitsmaAbstract:Statist. Med. DOI: 10.1002/sim.3410. Published Online: 27th August 2008 10:34AM. The Editors regret that the print publication of this paper, which was published on Early View on 27th August 2008, has occurred after the publication of a submitted Correction (Statistics in Medicine 2008; 27:4614) to the paper by Harel and Zhou [3]. The lack of reference in this Correction to previous correspondence [4,5] and to this paper by de Groot, Janssen, Zwinderman, Moons and Reitsma is particularly unfortunate and we apologize to our readers and to de Groot, Janssen, Zwinderman, Moons and Reitsma.
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multiple imputation to correct for partial Verification Bias revisited 5880 5889
Statistics in Medicine, 2009Co-Authors: J. A. H. De Groot, Kristel J.m. Janssen, Aeilko H. Zwinderman, K. G. M. Moons, Johannes B. ReitsmaAbstract:Statist. Med. DOI: 10.1002/sim.3410. Published Online: 27th August 2008 10:34AM. The Editors regret that the print publication of this paper, which was published on Early View on 27th August 2008, has occurred after the publication of a submitted Correction (Statistics in Medicine 2008; 27:4614) to the paper by Harel and Zhou [3]. The lack of reference in this Correction to previous correspondence [4,5] and to this paper by de Groot, Janssen, Zwinderman, Moons and Reitsma is particularly unfortunate and we apologize to our readers and to de Groot, Janssen, Zwinderman, Moons and Reitsma.
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Multiple imputation to correct for partial Verification Bias revisited.
Statistics in medicine, 2008Co-Authors: J. A. H. De Groot, Kristel J.m. Janssen, Aeilko H. Zwinderman, K. G. M. Moons, Johannes B. ReitsmaAbstract:Partial Verification refers to the situation where a subset of patients is not verified by the reference (gold) standard and is excluded from the analysis. If partial Verification is present, the observed (naive) measures of accuracy such as sensitivity and specificity are most likely to be Biased. Recently, Harel and Zhou showed that partial Verification can be considered as a missing data problem and that multiple imputation (MI) methods can be used to correct for this Bias. They claim that even in simple situations where the Verification is random within strata of the index test results, the so-called Begg and Greenes (B&G) correction method underestimates sensitivity and overestimates specificity as compared with the MI method. However, we were able to demonstrate that the B&G method produces similar results as MI, and that the claimed difference has been caused by a computational error. Additional research is needed to better understand which correction methods should be preferred in more complex scenarios of missing reference test outcome in diagnostic research.
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Multiple imputation to correct for partial Verification Bias revisited (5880-5889): Erratum
Statistics in Medicine, 2008Co-Authors: J. A. H. De Groot, Kristel J.m. Janssen, Aeilko H. Zwinderman, K. G. M. Moons, Johannes B. ReitsmaAbstract:Statist. Med. DOI: 10.1002/sim.3410. Published Online: 27th August 2008 10:34AM.The Editors regret that the print publication of this paper, which was published on Early View on 27th August 2008, has occurred after the publication of a submitted Correction (Statistics in Medicine 2008; 27:4614) to the paper by Harel and Zhou [3].The lack of reference in this Correction to previous correspondence [4,5] and to this paper by de Groot, Janssen, Zwinderman, Moons and Reitsma is particularly unfortunate and we apologize to our readers and to de Groot, Janssen, Zwinderman, Moons and Reitsma
Patrick M M Bossuyt - One of the best experts on this subject based on the ideXlab platform.
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Adjusting for Partial Verification or Workup Bias in Meta-Analyses of Diagnostic Accuracy Studies
American journal of epidemiology, 2012Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Johannes B. Reitsma, Nandini Dendukuri, James M. Brophy, Lawrence Joseph, Karel G.m. MoonsAbstract:A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can Bias the accuracy estimates. Numerous methods have been described for correcting this partial Verification Bias or workup Bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial Verification or workup Bias is present in a subset of the primary studies. The method corrects for Verification Bias without having to exclude primary studies with Verification Bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with Verification Bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients.
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Adjusting for differential-Verification Bias in diagnostic-accuracy studies: a Bayesian approach.
Epidemiology (Cambridge Mass.), 2011Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Johannes B. Reitsma, Nandini Dendukuri, Karel G.m. MoonsAbstract:In studies of diagnostic accuracy, the performance of an index test is assessed by verifying its results against those of a reference standard. If Verification of index-test results by the preferred reference standard can be performed only in a subset of subjects, an alternative reference test could be given to the remainder. The drawback of this so-called differential-Verification design is that the second reference test is often of lesser quality, or defines the target condition in a different way. Incorrectly treating results of the 2 reference standards as equivalent will lead to differential-Verification Bias. The Bayesian methods presented in this paper use a single model to (1) acknowledge the different nature of the 2 reference standards and (2) make simultaneous inferences about the population prevalence and the sensitivity, specificity, and predictive values of the index test with respect to both reference tests, in relation to latent disease status. We illustrate this approach using data from a study on the accuracy of the elbow extension test for diagnosis of elbow fractures in patients with elbow injury, using either radiography or follow-up as reference standards.
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Correcting for partial Verification Bias: a comparison of methods.
Annals of epidemiology, 2010Co-Authors: Joris A.h. De Groot, Patrick M M Bossuyt, Kristel J.m. Janssen, Aeilko H. Zwinderman, Johannes B. Reitsma, Karel G.m. MoonsAbstract:Purpose A common problem in diagnostic research is that the reference standard has not been carried out in all patients. This partial Verification may lead to Biased accuracy measures of the test under study. The authors studied the performance of multiple imputation and the conventional correction method proposed by Begg and Greenes under a range of different situations of partial Verification. Methods In a series of simulations, using a previously published deep venous thrombosis data set (n = 1292), the authors set the outcome of the reference standard to missing based on various underlying mechanisms and by varying the total number of missing values. They then compared the performance of the different correction methods. Results The results of the study show that when the mechanism of missing reference data is known, accuracy measures can easily be correctly adjusted using either the Begg and Greenes method, or multiple imputation. In situations where the mechanism of missing reference data is complex or unknown, we recommend using multiple imputation methods to correct. Conclusions These methods can easily apply for both continuous and categorical variables, are readily available in statistical software and give reliable estimates of the missing reference data.
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effect of study design on the association between nuchal translucency measurement and down syndrome
Obstetrics & Gynecology, 1999Co-Authors: Jeroen G Lijmer, Jan Van Der Meulen, Caterina M. Bilardo, Eva Pajkrt, Patrick M M BossuytAbstract:Abstract Objective: To evaluate the effect of Verification Bias on the accuracy of first-trimester nuchal translucency measurement for Down syndrome detection. Methods: We used MEDLINE and EMBASE to identify all papers relating the results of nuchal translucency measurement to fetal karyotype. The detected studies were scored for Verification Bias. Fifteen studies without and ten with Verification Bias were included. Results: Sensitivity and specificity were calculated for each study. For studies with Verification Bias, adjusted estimates of the sensitivity were calculated assuming a fetal loss rate for Down syndrome pregnancies of 48%. The sample size weighted sensitivity was 55% in studies without and 77% in those with Verification Bias, for specificities of 96% and 97%, respectively. After adjustment for Verification Bias, the sample size weighted sensitivity changed from 77% to 63%. Conclusion: Studies with Verification Bias reported higher sensitivities, but also slightly higher specificities of nuchal translucency measurement than studies without Verification Bias. The difference in sensitivity is greater than could be explained by Verification Bias. We postulate that the experience of the sonographist might be an explanation for the differences.
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Effect of study design on the association between nuchal translucency measurement and Down syndrome.
Obstetrics and gynecology, 1999Co-Authors: B W Mol, Jeroen G Lijmer, Caterina M. Bilardo, Eva Pajkrt, J Van Der Meulen, Patrick M M BossuytAbstract:To evaluate the effect of Verification Bias on the accuracy of first-trimester nuchal translucency measurement for Down syndrome detection. We used MEDLINE and EMBASE to identify all papers relating the results of nuchal translucency measurement to fetal karyotype. The detected studies were scored for Verification Bias. Fifteen studies without and ten with Verification Bias were included. Sensitivity and specificity were calculated for each study. For studies with Verification Bias, adjusted estimates of the sensitivity were calculated assuming a fetal loss rate for Down syndrome pregnancies of 48%. The sample size weighted sensitivity was 55% in studies without and 77% in those with Verification Bias, for specificities of 96% and 97%, respectively. After adjustment for Verification Bias, the sample size weighted sensitivity changed from 77% to 63%. Studies with Verification Bias reported higher sensitivities, but also slightly higher specificities of nuchal translucency measurement than studies without Verification Bias. The difference in sensitivity is greater than could be explained by Verification Bias. We postulate that the experience of the sonographist might be an explanation for the differences.
