Presence Of Misclassification Research Articles

Bayesian analysis of esophageal cancer mortality in the presence of misclassification

Background: Esophageal cancer (EC) is one of the most common cancers worldwide. Mortality is a familiar projection that addresses the burden of cancers. With regards to cancer mortality, data are important and used to monitor the effects of screening programs, earlier diagnosis and other prognostic factors. But according to the Iranian death registry, about 20% of death statistics are still recorded in misclassified categories. The aim of this study is to estimate EC mortality in the Iranian population, using a Bayesian approach in order to revise this misclassification. Methods: We analyzed National death Statistics reported by the Iranian Ministry of Health and Medical Education from 1995 to 2004. ECs [ICD-9; C15] were expressed as annual mortality rates/100,000, overall, by sex, by age group and age standardized rate (ASR). The Bayesian approach was used to correct and account for misclassification effects in Poisson count regression, with a beta prior employed to estimate the mortality rate of EC in age and sex groups. Results: According to the Bayesian analysis, there were between 20 to 30 percent underreported deaths in mortality records related to EC, and the rate of mortality from EC has increased through recent years. Conclusions: Our findings suggested a substantial undercount of EC mortality in the Iranian population. Sopolicy makers who determine research and treatment priorities based on reported death rates should notice of this underreported data.

An efficient confidence interval estimation for prevalence calculated from misclassified data

The estimation of prevalence using a screening tool is done frequently in epidemiology research. The tools used for the estimation are usually associated with a certain level of misclassification. Additional adjustments are required to eliminate the bias in the prevalence and the confidence interval (CI) estimate. A frequently used method for this correction is by modifying the upper and lower limits, using sensitivity and specificity, increasing the width of the CI. The issue is exaggerated with a minimal sample size. Zhou and Li recently developed a method to estimate the CI using the Edgeworth expansion of the logit transformed binomial proportion. This article introduces a specialised tool by re-estimating the confidence limits adjusting for misclassified measurements, and assesses their characteristics through a simulation. The paper provides evidence that the re-estimated new interval performs better in the presence of misclassification.

Bounding the joint distribution of disability and employment with misclassification.

Understanding the relationship between disability and employment is critical and has long been the subject of study. However, estimating this relationship is difficult, particularly with survey data, since both disability and employment status are known to be misreported. Here, we use a partial identification approach to bound the joint distribution of disability and employment status in the presence of misclassification. Allowing for a modest amount of misclassification leads to bounds on the labor market status of the disabled that are not overly informative given the relative size of the disabled population. Thus, absent further assumptions, even a modest amount of misclassification creates much uncertainty about the employment gap between the non-disabled and disabled. However, additional assumptions considered are shown to have some identifying power. For example, under our most stringent assumptions, we find that the employment gap is at least 15.2% before the Great Recession and 22.0% afterward.

Estimation and inference for the population attributable risk in the presence of misclassification.

Because it describes the proportion of disease cases that could be prevented if an exposure were entirely eliminated from a target population as a result of an intervention, estimation of the population attributable risk (PAR) has become an important goal of public health research. In epidemiologic studies, categorical covariates are often misclassified. We present methods for obtaining point and interval estimates of the PAR and the partial PAR (pPAR) in the presence of misclassification, filling an important existing gap in public health evaluation methods. We use a likelihood-based approach to estimate parameters in the models for the disease and for the misclassification process, under main study/internal validation study and main study/external validation study designs, and various plausible assumptions about transportability. We assessed the finite sample perf ormance of this method via a simulation study, and used it to obtain corrected point and interval estimates of the pPAR for high red meat intake and alcohol intake in relation to colorectal cancer incidence in the HPFS, where we found that the estimated pPAR for the two risk factors increased by up to 317% after correcting for bias due to misclassification.

Bias correction methods for test-negative designs in the presence of misclassification.

The test-negative design (TND) has become a standard approach for vaccine effectiveness (VE) studies. However, previous studies suggested that it may be more vulnerable than other designs to misclassification of disease outcome caused by imperfect diagnostic tests. This could be a particular limitation in VE studies where simple tests (e.g. rapid influenza diagnostic tests) are used for logistical convenience. To address this issue, we derived a mathematical representation of the TND with imperfect tests, then developed a bias correction framework for possible misclassification. TND studies usually include multiple covariates other than vaccine history to adjust for potential confounders; our methods can also address multivariate analyses and be easily coupled with existing estimation tools. We validated the performance of these methods using simulations of common scenarios for vaccine efficacy and were able to obtain unbiased estimates in a variety of parameter settings.

Game Theoretic Suppression of Forged Messages in Online Social Networks

Online social networks (OSNs) suffer from forged messages. Current studies have typically been focused on the detection of forged messages and do not provide the analysis of the behaviors of message publishers and network strategies to suppress forged messages. This paper carries out the analysis by taking a game theoretic approach, where infinitely repeated games are constructed to capture the interactions between a publisher and a network administrator and suppress forged messages in OSNs. Critical conditions, under which the publisher is disincentivized to publish any forged messages, are identified in the absence and presence of misclassification on genuine messages. Closed-form expressions are established for the maximum number of forged messages that a malicious publisher could publish. Confirmed by the numerical results, the proposed infinitely repeated games reveal that forged messages can be suppressed by improving the payoffs for genuine messages, increasing the cost of bots, and/or reducing the payoffs for forged messages. The increasing detection probability of forged messages or decreasing misclassification probability of genuine messages also has a strong impact on the suppression of forged messages.

“Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification”—Author Response to Comment

American Journal of Agricultural EconomicsVolume 101, Issue 1 p. 19-19 Lead Article with Discussion and Response “Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification”—Author Response to Comment Tesfamicheal Wossen, Tesfamicheal Wossen Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorTahirou Abdoulaye, Tahirou Abdoulaye Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorArega Alene, Arega Alene Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorPierre Nguimkeu, Pierre Nguimkeu Assistant Professor Department of Economics, Georgia State UniversitySearch for more papers by this authorShiferaw Feleke, Shiferaw Feleke Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorIsmail Y. Rabbi, Ismail Y. Rabbi Molecular Geneticist/Breeder IITASearch for more papers by this authorMekbib G. Haile, Mekbib G. Haile World BankSearch for more papers by this authorVictor M. Manyong, Victor M. Manyong Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this author Tesfamicheal Wossen, Tesfamicheal Wossen Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorTahirou Abdoulaye, Tahirou Abdoulaye Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorArega Alene, Arega Alene Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorPierre Nguimkeu, Pierre Nguimkeu Assistant Professor Department of Economics, Georgia State UniversitySearch for more papers by this authorShiferaw Feleke, Shiferaw Feleke Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this authorIsmail Y. Rabbi, Ismail Y. Rabbi Molecular Geneticist/Breeder IITASearch for more papers by this authorMekbib G. Haile, Mekbib G. Haile World BankSearch for more papers by this authorVictor M. Manyong, Victor M. Manyong Economist International Institute of Tropical Agriculture (IITA)Search for more papers by this author First published: 15 October 2018 https://doi.org/10.1093/ajae/aay079 This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL No abstract is available for this article. Volume101, Issue1January 2019Pages 19-19 RelatedInformation

Comment on “Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification”

Wossen et al. (2018) puts the spotlight on an important, and until recently underestimated, phenomenon: farmers in developing countries may often misreport whether the crop varieties they grow are improved or not. Given the key role of improved varieties in the green revolution, and ongoing debates on whether productivity gains can still be achieved and reach areas of the developing world where diffusion of improved varieties has been limited, establishing reliable empirical evidence regarding the levels of adoption of improved varieties is a first-order priority. (This abstract was borrowed from another version of this item.)

Estimating the Productivity Impacts of Technology Adoption in the Presence of Misclassification

AbstractThis article examines the impact that misreporting adoption status has on the identification and estimation of causal effects on productivity. In particular, by comparing measurement error‐ridden self‐reported adoption data with measurement‐error‐free DNA‐fingerprinted adoption data, we investigate the extent to which such errors bias the causal effects of adoption on productivity. Taking DNA‐fingerprinted adoption data as a benchmark, we find 25% “false negatives” and 10% “false positives” in farmers’ responses. Our results show that misreporting of adoption status is not exogenous to household characteristics, and produces a bias of about 22 percentage points in the productivity impact of adoption. Ignoring inherent behavioral adjustments of farmers based on perceived adoption status has a bias of 13 percentage points. The results of this article underscore the crucial role that correct measurement of adoption plays in designing policy interventions that address constraints to technology adoption in agriculture.

Bayesian adjustment of gastric cancer mortality rate in the presence of misclassification.

AIMTo correct for misclassification error in registering causes of death in Iran death registry using Bayesian method.METHODSNational death statistic from 2006 to 2010 for gastric cancer which reported annually by the Ministry of Health and Medical Education included in this study. To correct the rate of gastric cancer mortality with reassigning the deaths due to gastric cancer that registered as cancer without detail, a Bayesian method was implemented with Poisson count regression and beta prior for misclassified parameter, assuming 20% misclassification in registering causes of death in Iran.RESULTSRegistered mortality due to gastric cancer from 2006 to 2010 was considered in this study. According to the Bayesian re-estimate, about 3%-7% of deaths due to gastric cancer have registered as cancer without mentioning details. It makes an undercount of gastric cancer mortality in Iranian population. The number and age standardized rate of gastric cancer death is estimated to be 5805 (10.17 per 100000 populations), 5862 (10.51 per 100000 populations), 5731 (10.23 per 100000 populations), 5946 (10.44 per 100000 populations), and 6002 (10.35 per 100000 populations), respectively for years 2006 to 2010.CONCLUSIONThere is an undercount in gastric cancer mortality in Iranian registered data that researchers and authorities should notice that in sequential estimations and policy making.

Reader Reaction: A Note on the Evaluation of Group Testing Algorithms in the Presence of Misclassification

In the context of group testing screening, McMahan, Tebbs, and Bilder (2012, Biometrics 68, 287-296) proposed a two-stage procedure in a heterogenous population in the presence of misclassification. In earlier work published in Biometrics, Kim, Hudgens, Dreyfuss, Westreich, and Pilcher (2007, Biometrics 63, 1152-1162) also proposed group testing algorithms in a homogeneous population with misclassification. In both cases, the authors evaluated performance of the algorithms based on the expected number of tests per person, with the optimal design being defined by minimizing this quantity. The purpose of this article is to show that although the expected number of tests per person is an appropriate evaluation criteria for group testing when there is no misclassification, it may be problematic when there is misclassification. Specifically, a valid criterion needs to take into account the amount of correct classification and not just the number of tests. We propose, a more suitable objective function that accounts for not only the expected number of tests, but also the expected number of correct classifications. We then show how using this objective function that accounts for correct classification is important for design when considering group testing under misclassification. We also present novel analytical results which characterize the optimal Dorfman (1943) design under the misclassification.

Rejoinder to "Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification".

This article is the rejoinder to:

Rejoinder to "Reader reaction: A note on the evaluation of group testing algorithms in the presence of misclassification".

This article is the rejoinder to:

Effects of imperfect test sensitivity and specificity on observational studies of influenza vaccine effectiveness

BackgroundThe recently developed test-negative design is now standard for observational studies of influenza vaccine effectiveness (VE). It is unclear how influenza test misclassification biases test-negative VE estimates relative to VE estimates from traditional cohort or case-control studies. MethodsWe simulated populations whose members may develop acute respiratory illness (ARI) due to influenza and to non-influenza pathogens. In these simulations, vaccination reduces the risk of influenza but not of non-influenza ARI. Influenza test sensitivity and specificity, risks of influenza and non-influenza ARI, and VE were varied across the simulations. In each simulation, we estimated influenza VE using a cohort design, a case-control design, and a test-negative design. ResultsIn the absence of influenza test misclassification, all three designs accurately estimated influenza VE. In the presence of misclassification, all three designs underestimated VE. Bias in VE estimates was slightly greater in the test-negative design than in cohort or case-control designs. Assuming the use of highly sensitive and specific reverse-transcriptase polymerase chain reaction tests for influenza, bias in the test-negative studies was trivial across a wide range of realistic values for VE. DiscussionAlthough influenza test misclassification causes more bias in test-negative studies than in traditional cohort or case-control studies, the difference is trivial for realistic combinations of attack rates, test sensitivity/specificity, and VE.

Misclassification in binary responses and effect on genome-wide association studies

Misclassification of dependent variables is a major issue in many areas of science that can arise when indirect markers are used to classify subjects or continuous traits are treated as categorical. In human medicine, this can have significant impacts on diagnostic accuracy. In animal science applications, misclassification can negatively affect both the accuracy of selection and the ability to ascertain the biological mechanisms for traits of interest. When dealing with traits influenced by genetic factors, genomic markers, such as SNP, can provide direct measurements of the underlying mechanisms controlling phenotypic responses. Unfortunately, in the presence of misclassification in the discrete dependent variables, the robustness of the analysis and the validity of the results could be severely compromised. To quantify the impact of misclassification on genome-wide association studies for binary responses, a real databased simulation was carried out. The simulated data consisted of 2,400 animals genotyped for 50K SNP. A binary trait with heritability equal to 0.10 and prevalence of 20% was generated. A rate of 1, 5, and 10% misclassification was artificially introduced to the true binary responses. Using a latent-threshold model, 3 analyses were carried out for each misclassification rate using 1) the true data (M1), 2) the contaminated data and ignoring misclassification (M2), and 3) the contaminated data and accounting for misclassification (M3). The results indicate that ignoring misclassification, when it exists in the data such as in M2, will lead to major deterioration in the performance of the model. When misclassification was contemplated in the model (M2), the results indicated a strong capacity of the procedure in dealing with potential misclassification in the training set. In fact, a large portion of miscoded samples in the training set was identified and corrected. The results of this study suggest that the proposed method is adequate and effective for practical genome-wide association studies for binary response classification.

A multilevel model for spatially correlated binary data in the presence of misclassification: an application in oral health research

Dental caries is a highly prevalent disease affecting the tooth's hard tissues by acid-forming bacteria. The past and present caries status of a tooth is characterized by a response called caries experience (CE). Several epidemiological studies have explored risk factors for CE. However, the detection of CE is prone to misclassification because some cases are neither clearly carious nor noncarious, and this needs to be incorporated into the epidemiological models for CE data. From a dentist's point of view, it is most appealing to analyze CE on the tooth's surface, implying that the multilevel structure of the data (surface-tooth-mouth) needs to be taken into account. In addition, CE data are spatially referenced, that is, an active lesion on one surface may impact the decay process of the neighboring surfaces, and that might also influence the process of scoring CE. In this paper, we investigate two hypotheses: that is, (i) CE outcomes recorded at surface level are spatially associated; and (ii) the dental examiners exhibit some spatial behavior while scoring CE at surface level, by using a spatially referenced multilevel autologistic model, corrected for misclassification. These hypotheses were tested on the well-known Signal Tandmobiel® study on dental caries, and simulation studies were conducted to assess the effect of misclassification and strength of spatial dependence on the autologistic model parameters. Our results indicate a substantial spatial dependency in the examiners' scoring behavior and also in the prevalence of CE at surface level.

Comparison of disease prevalence in two populations in the presence of misclassification

Comparing disease prevalence in two groups is an important topic in medical research, and prevalence rates are obtained by classifying subjects according to whether they have the disease. Both high-cost infallible gold-standard classifiers or low-cost fallible classifiers can be used to classify subjects. However, statistical analysis that is based on data sets with misclassifications leads to biased results. As a compromise between the two classification approaches, partially validated sets are often used in which all individuals are classified by fallible classifiers, and some of the individuals are validated by the accurate gold-standard classifiers. In this article, we develop several reliable test procedures and approximate sample size formulas for disease prevalence studies based on the difference between two disease prevalence rates with two independent partially validated series. Empirical studies show that (i) the Score test produces close-to-nominal level and is preferred in practice; and (ii) the sample size formula based on the Score test is also fairly accurate in terms of the empirical power and type I error rate, and is hence recommended. A real example from an aplastic anemia study is used to illustrate the proposed methodologies.

Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification

Motivated by a longitudinal oral health study, the Signal–Tandmobiel® study, we propose a multivariate binary inhomogeneous Markov model in which unobserved correlated response variables are subject to an unconstrained misclassification process and have a monotone behavior. The multivariate baseline distributions and Markov transition matrices of the unobserved processes are defined as a function of covariates through the specification of compatible full conditional distributions. Distinct misclassification models are discussed. In all cases, the possibility that different examiners were involved in the scoring of the responses of a given subject across time is taken into account. A full Bayesian implementation of the model is described and its performance is evaluated using simulated data. We provide theoretical and empirical evidence that the parameters can be estimated without any external information about the misclassification parameters. Finally, the analyses of the motivating study are presented. Appendices 1–7 are available in the online supplementary materials.

Optimality of group testing in the presence of misclassification

Several optimality properties of Dorfman's (1943) group testing procedure are derived for estimation of the prevalence of a rare disease whose status is classified with error. Exact ranges of disease prevalence are obtained for which group testing provides more efficient estimation when group size increases.

On the power of the Cochran–Armitage test for trend in the presence of misclassification

The Cochran-Armitage (CA) test is commonly used in both epidemiology and genetics to test for linear trend in two-way tables with a binary outcome. There has been increasing interest in the power and size of the test and in determination of sample size, especially when there is potential misclassification in the 'exposure' category. This article provides a unified approach to determination of the power function over different sampling strategies (fixed overall sample size or fixed marginal sample sizes) and allowing for misclassification in one or both variables. The misclassification may be either differential or non-differential. In addition to the standard CA test, results are also given which provide some insight into the performance of the modified CA test, which utilizes a standard error obtained without invoking the null hypothesis. Even without misclassification, some new expressions are also obtained for determining power with a fixed overall sample size. Numerical illustrations are presented with an emphasis on the more commonly occurring problem of misclassification in the exposure category.