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Journal articles on the topic 'Misclassification'

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Published: 4 June 2021

Last updated: 21 February 2023

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1

Liu, Zhenbing, Chunyang Gao, Huihua Yang, and Qijia He. "A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem." Scientific Programming 2016 (2016): 1–9. http://dx.doi.org/10.1155/2016/8035089.

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Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC) methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC) for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use F-measure, G-mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.

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2

Sommer, Alfred. "Misclassification." Archives of Ophthalmology 126, no. 2 (February 1, 2008): 265. http://dx.doi.org/10.1001/archophthalmol.2007.73.

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3

Stenson, G. B., and R. A. Myers. "Accuracy of Pup Classifications and Its Effect on Population Estimates in the Hooded Seal (Cystophora cristata)." Canadian Journal of Fisheries and Aquatic Sciences 45, no. 4 (April 1, 1988): 715–19. http://dx.doi.org/10.1139/f88-086.

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Information from the classification of age-specific developmental stages has been used to adjust aerial survey estimates of pup production in a number of species of seals, including the hooded seal (Cystophora cristata). We test the assumption that hooded seal pups were accurately and consistently classified according to developmental stage and examine the consequences of misclassifications upon adjusted population estimates. We determined overall misclassification rates, the effect of survey height on classifications, and interobserver variability. At ice level, misclassifications rates were low (<3%). From an altitude of 30 m, newborn pups could not be classified correctly and misclassification rates for the two other stages of attended pups varied between 6.4 and 21.3%. There was no evidence of an overall bias in classifications or differences among observers although there was a significant interaction between day and stage. Individual pups appear to have been misclassified independently by each observer. Under actual survey conditions, observers classified a similar proportion of pups into each recognizable stage. The misclassification rates we observed did not significantly alter the previous population estimate. Methods for improving the current survey design include modifying classification criteria, providing observers with a period of on-ice training, and reducing the width of survey transects.

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4

Mangasarian, O. L. "Misclassification minimization." Journal of Global Optimization 5, no. 4 (December 1994): 309–23. http://dx.doi.org/10.1007/bf01096681.

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5

Pires, Magda Carvalho, and Roberto da Costa Quinino. "Repeated responses in misclassification binary regression: A Bayesian approach." Statistical Modelling 19, no. 4 (June 11, 2018): 412–43. http://dx.doi.org/10.1177/1471082x18773394.

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Binary regression models generally assume that the response variable is measured perfectly. However, in some situations, the outcome is subject to misclassification: a success may be erroneously classified as a failure or vice versa. Many methods, described in existing literature, have been developed to deal with misclassification, but we demonstrate that these methods may lead to serious inferential problems when only a single evaluation of the individual is taken. Thus, this study proposes to incorporate repeated and independent responses in misclassification binary regression models, considering the total number of successes obtained or even the simple majority classification. We use subjective prior distributions, as our conditional means prior, to evaluate and compare models. A data augmentation approach, Gibbs sampling, and Adaptive Rejection Metropolis Sampling are used for posterior inferences. Simulation studies suggested that repeated measures significantly improve the posterior estimates, in that these estimates are closer to those obtained in a case with no misclassifications with a lower standard deviation. Finally, we illustrate the usefulness of the new methodology with the analysis about defects in eyeglass lenses.

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6

Remaley, Alan T., Maureen L. Sampson, James M. DeLeo, Nancy A. Remaley, Beriuse D. Farsi, and Mark H. Zweig. "Prevalence-Value-Accuracy Plots: A New Method for Comparing Diagnostic Tests Based on Misclassification Costs." Clinical Chemistry 45, no. 7 (July 1, 1999): 934–41. http://dx.doi.org/10.1093/clinchem/45.7.934.

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Abstract The clinical accuracy of diagnostic tests commonly is assessed by ROC analysis. ROC plots, however, do not directly incorporate the effect of prevalence or the value of the possible test outcomes on test performance, which are two important factors in the practical utility of a diagnostic test. We describe a new graphical method, referred to as a prevalence-value-accuracy (PVA) plot analysis, which includes, in addition to accuracy, the effect of prevalence and the cost of misclassifications (false positives and false negatives) in the comparison of diagnostic test performance. PVA plots are contour plots that display the minimum cost attributable to misclassifications (z-axis) at various optimum decision thresholds over a range of possible values for prevalence (x-axis) and the unit cost ratio (UCR; y-axis), which is an index of the cost of a false-positive vs a false-negative test result. Another index based on the cost of misclassifications can be derived from PVA plots for the quantitative comparison of test performance. Depending on the region of the PVA plot that is used to calculate the misclassification cost index, it can potentially lead to a different interpretation than the ROC area index on the relative value of different tests. A PVA-threshold plot, which is a variation of a PVA plot, is also described for readily identifying the optimum decision threshold at any given prevalence and UCR. In summary, the advantages of PVA plot analysis are the following: (a) it directly incorporates the effect of prevalence and misclassification costs in the analysis of test performance; (b) it yields a quantitative index based on the costs of misclassifications for comparing diagnostic tests; (c) it provides a way to restrict the comparison of diagnostic test performance to a clinically relevant range of prevalence and UCR; and (d) it can be used to directly identify an optimum decision threshold based on prevalence and misclassification costs.

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7

Cheng, Simon, and Brian Powell. "Misclassification by Whom?" American Sociological Review 76, no. 2 (March 31, 2011): 347–55. http://dx.doi.org/10.1177/0003122411401249.

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8

GREENLAND, SANDER, and JAMES M. ROBINS. "CONFOUNDING AND MISCLASSIFICATION." American Journal of Epidemiology 122, no. 3 (September 1985): 495–506. http://dx.doi.org/10.1093/oxfordjournals.aje.a114131.

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9

Walker, Alexander M., and Stephan F. Lanes. "Misclassification of covariates." Statistics in Medicine 10, no. 8 (August 1991): 1181–96. http://dx.doi.org/10.1002/sim.4780100803.

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10

Chen, Chunhui, and O. L. Mangasarian. "Hybrid misclassification minimization." Advances in Computational Mathematics 5, no. 1 (December 1996): 127–36. http://dx.doi.org/10.1007/bf02124738.

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11

Dodds, Peter R., and Jon H. Dodds. "Surgical Wound Misclassification." Journal of the American College of Surgeons 221, no. 3 (September 2015): 781. http://dx.doi.org/10.1016/j.jamcollsurg.2015.06.006.

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12

KHOSHGOFTAAR, TAGHI M., and EDWARD B. ALLEN. "LOGISTIC REGRESSION MODELING OF SOFTWARE QUALITY." International Journal of Reliability, Quality and Safety Engineering 06, no. 04 (December 1999): 303–17. http://dx.doi.org/10.1142/s0218539399000292.

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Reliable software is mandatory for complex mission-critical systems. Classifying modules as fault-prone, or not, is a valuable technique for guiding development processes, so that resources can be focused on those parts of a system that are most likely to have faults. Logistic regression offers advantages over other classification modeling techniques, such as interpretable coefficients. There are few prior applications of logistic regression to software quality models in the literature, and none that we know of account for prior probabilities and costs of misclassification. A contribution of this paper is the application of prior probabilities and costs of misclassification to a logistic regression-based classification rule for a software quality model. This paper also contributes an integrated method for using logistic regression in software quality modeling, including examples of how to interpret coefficients, how to use prior probabilities, and how to use costs of misclassifications. A case study of a major subsystem of a military, real-time system illustrates the techniques.

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13

Aloufi, Abdulaziz D., Jake M. Najman, and Abdullah A. Mamun. "The Association between Body Weight Misclassification in Adolescence and Body Fat and Waist Circumference in Adulthood: A Longitudinal Study." Nutrients 14, no. 22 (November 11, 2022): 4765. http://dx.doi.org/10.3390/nu14224765.

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This study examined the longitudinal association between adolescent body weight misclassifications and body fat and waist circumference during adulthood. A sample was derived from a large Australian birth cohort study. The data analyses were restricted to 1002 participants for whom data on both measured and perceived weight at a 14-year follow-up and the actual measure of adult body fat and waist circumference at a 30-year follow-up were available. To determine misclassifications, we compared the perceived weight with the measured weight. The results were presented as means and mean differences (with a 95% confidence interval) of the body fat percentages and waist circumference levels across the weight misclassification groups, adjusting for potential covariates. For both male and female adolescents, weight underestimation was significantly associated with an increase in body fat percentages and waist circumference in adulthood as compared to those who correctly estimated their weight. In the mean difference analyses, adolescent males and females who underestimated their weight were found to have significantly higher body fat, and waist circumference means than those who correctly estimated their weight in the unadjusted and adjusted comparisons. The adolescent males who overestimated their weight had higher body fat, and waist circumference means when they reached adulthood. Increased awareness of weight misclassification and actual weight among adolescents might contribute to better control of weight gain in adulthood.

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14

Nakashima, Tomoharu, Yasuyuki Yokota, Hisao Ishibuchi, Gerald Schaefer, Aleš Drastich, and Michal Závišek. "Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems." Journal of Advanced Computational Intelligence and Intelligent Informatics 11, no. 6 (July 20, 2007): 546–53. http://dx.doi.org/10.20965/jaciii.2007.p0546.

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We evaluate the performance of cost-sensitive fuzzy-rule-based systems for pattern classification problems. We assume that a misclassification cost is given a priori for each training pattern. The task of classification thus becomes to minimize both classification error and misclassification cost. We examine the performance of two types of fuzzy classification based on fuzzy if-then rules generated from training patterns. The difference is whether or not they consider misclassification costs in rule generation. In our computational experiments, we use several specifications of misclassification cost to evaluate the performance of the two classifiers. Experimental results show that both classification error and misclassification cost are reduced by considering the misclassification cost in fuzzy rule generation.

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15

Nelson, Tyler, Joon Jin Song, Yoo-Mi Chin, and James D. Stamey. "Bayesian Correction for Misclassification in Multilevel Count Data Models." Computational and Mathematical Methods in Medicine 2018 (2018): 1–6. http://dx.doi.org/10.1155/2018/3212351.

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Covariate misclassification is well known to yield biased estimates in single level regression models. The impact on hierarchical count models has been less studied. A fully Bayesian approach to modeling both the misclassified covariate and the hierarchical response is proposed. Models with a single diagnostic test and with multiple diagnostic tests are considered. Simulation studies show the ability of the proposed model to appropriately account for the misclassification by reducing bias and improving performance of interval estimators. A real data example further demonstrated the consequences of ignoring the misclassification. Ignoring misclassification yielded a model that indicated there was a significant, positive impact on the number of children of females who observed spousal abuse between their parents. When the misclassification was accounted for, the relationship switched to negative, but not significant. Ignoring misclassification in standard linear and generalized linear models is well known to lead to biased results. We provide an approach to extend misclassification modeling to the important area of hierarchical generalized linear models.

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16

Castellanos, Arturo Rodríguez, and Belén Vallejo Alonso. "Spanish Mutual Fund Misclassification." Journal of Investing 14, no. 1 (February 28, 2005): 41–51. http://dx.doi.org/10.3905/joi.2005.479388.

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17

Edridge, William. "Classification, confusion and misclassification." South African Journal of Obstetrics and Gynaecology 23, no. 1 (May 16, 2017): 2. http://dx.doi.org/10.7196/sajog.2017.v23i1.1191.

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18

Chavance, Michel, and Georges Dellatolas. "Bias from Dependent Misclassification." Epidemiology 4, no. 2 (March 1993): 180. http://dx.doi.org/10.1097/00001648-199303000-00015.

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19

Kristensen, Fetter. "Bias from Dependent Misclassification." Epidemiology 4, no. 2 (March 1993): 181. http://dx.doi.org/10.1097/00001648-199303000-00016.

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20

LUCAS, L. J. "Misclassification of Nasopharyngeal Cancer." JNCI Journal of the National Cancer Institute 86, no. 20 (October 19, 1994): 1556–57. http://dx.doi.org/10.1093/jnci/86.20.1556.

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21

Mehra, Ila V. "Correction: Misclassification of Clonidine." Annals of Internal Medicine 120, no. 4 (February 15, 1994): 347. http://dx.doi.org/10.7326/0003-4819-120-4-199402150-00030.

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22

Campbell, Mary E., and Lisa Troyer. "Further Data on Misclassification." American Sociological Review 76, no. 2 (March 31, 2011): 356–64. http://dx.doi.org/10.1177/0003122411401251.

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23

Christensen, Dirk Lund, and Juan Lopez Taylor. "Misclassification of study population." Diabetes Research and Clinical Practice 159 (January 2020): 107656. http://dx.doi.org/10.1016/j.diabres.2019.03.011.

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24

Daniel Paulino, Carlos, Paulo Soares, and John Neuhaus. "Binomial Regression with Misclassification." Biometrics 59, no. 3 (September 2003): 670–75. http://dx.doi.org/10.1111/1541-0420.00077.

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25

Kim, Moon, Ravi Shukla, and Michael Tomas. "Mutual fund objective misclassification." Journal of Economics and Business 52, no. 4 (July 2000): 309–23. http://dx.doi.org/10.1016/s0148-6195(00)00022-9.

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26

Painter, John A., Drew L. Posey, and Christina Phares. "Tuberculosis misclassification among immigrants." International Journal of Tuberculosis and Lung Disease 19, no. 10 (October 1, 2015): 1259–60. http://dx.doi.org/10.5588/ijtld.15.0468.

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27

Küchenhoff, Helmut, Samuel M. Mwalili, and Emmanuel Lesaffre. "A General Method for Dealing with Misclassification in Regression: The Misclassification SIMEX." Biometrics 62, no. 1 (July 4, 2005): 85–96. http://dx.doi.org/10.1111/j.1541-0420.2005.00396.x.

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28

Vogel, C., and O. Gefeller. "Implications of Nondifferential Misclassification on Estimates of Attributable Risk." Methods of Information in Medicine 41, no. 04 (2002): 342–48. http://dx.doi.org/10.1055/s-0038-1634392.

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Summary Objectives: Only the effects of isolated nondifferential misclassification of exposure or disease on the estimates of attributable risk have been discussed in the literature. The aim of this paper is to broaden the spectrum of scenarios for which implications of misclassification are available. Methods: For this purpose, a matrix-based approach allowing a comprehensive, unified analysis of various structures of misclassification is introduced. The relative bias or – in the situation of covariate misclassification – the relative adjustment are presented for the different misclassification scenarios. Results: Under nondifferential misclassification of exposure or disease, the attributable risk is biased towards the null with the only exception of perfect sensitivity of exposure classification or perfect specificity of disease classification both leading to an unbiased attributable risk. From these two marginal effects, the consequences of simultaneous nondifferential independent misclassification of exposure and disease on the attributable risk are derived in the matrix-based approach. Misclassification of a dichotomous covariate leads to partial adjustment. Conclusions: To a large extent, the results for the attributable risk are in accordance with the well-known results for the relative risk. The algebraic differences between the two risk measures, however, make it necessary to repeat the methodological considerations for the attributable risk.

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29

Li, Yige, and Tyler J. VanderWeele. "Direct Effects under Differential Misclassification in Outcomes, Exposures, and Mediators." Journal of Causal Inference 8, no. 1 (January 1, 2020): 286–99. http://dx.doi.org/10.1515/jci-2019-0020.

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Abstract Direct effects in mediation analysis quantify the effect of an exposure on an outcome not mediated by a certain intermediate. When estimating direct effects through measured data, misclassification may occur in the outcomes, exposures, and mediators. In mediation analysis, any such misclassification may lead to biased estimates in the direct effects. Basing on the conditional dependence between the mismeasured variable and other variables given the true variable, misclassification mechanisms can be divided into non-differential misclassification and differential misclassification. In this article, several scenarios of differential misclassification will be discussed and sensitivity analysis results on direct effects will be derived for those eligible scenarios. According to our findings, the estimated direct effects are not necessarily biased in intuitively predictable directions when the misclassification is differential. The bounds of the true effects are functions of measured effects and sensitivity parameters. An example from the 2018 NCHS data will illustrate how to conduct sensitivity analyses with our results on misclassified outcomes, gestational hypertension and eclampsia, when the exposure is Hispanic women versus non-Hispanic White women and the mediator is weights gain during pregnancy.

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30

Clement Adeyeye, Awogbemi. "A Note on Different Types of Probabilities of Misclassification." Academic Journal of Applied Mathematical Sciences, no. 68 (September 10, 2020): 181–86. http://dx.doi.org/10.32861/ajams.68.181.186.

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Whenever a discriminant function is constructed, the attention of a researcher is often focused on classification. The underlined interest is how well does a discriminant function perform in classifying future observations correctly. In order to assess the performance of any classification rule, probabilities of misclassification of a discriminant function serves as a basis for the procedure. Different forms of probabilities of misclassification and their associated properties were considered in this study. The misclassification probabilities were defined in terms of probability density functions (pdf) and classification regions. Apparent probability of misclassification is expressed as the proportion of observations in the initial sample which are misclassified by the sample discriminant function. Different methods of estimating probabilities of misclassification were related to each other using their individual shortcomings. The status of degrees of uncertainties associated with probabilities of misclassification and their implications were also specified.

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Ashford, Kristin, Amanda Wiggins, Emily Rayens, Sara Assef, Amanda Fallin, and Mary Kay Rayens. "Perinatal Biochemical Confirmation of Smoking Status by Trimester." Nicotine & Tobacco Research 19, no. 5 (April 11, 2017): 631–35. http://dx.doi.org/10.1093/ntr/ntw332.

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Abstract Introduction: Tobacco use during pregnancy is the most modifiable risk factor associated with poor pregnancy outcomes. Self-reported tobacco use has been demonstrated to have high misclassification rates. The aims were to examine misclassification rates of perinatal tobacco use during each trimester of pregnancy and 8 weeks postpartum, and to evaluate characteristics associated with misclassification of tobacco use status. Methods: This is secondary analysis of a prospective, multicenter trial of pregnant women, and it includes participants who were biochemically identified as tobacco users during their first trimester (N = 103). Each trimester and once postpartum, tobacco use was assessed via self-report and validated using a cutoff of 100 ng/mL for urine cotinine via NicAlert test strips to indicate current use. Those who self-reported as nonusers but were identified as users via urine cotinine were considered misclassified; misclassification rates were determined for each time period. Logistic regression assessed maternal factors associated with misclassification status. Results: Misclassification rates declined from 35.0% at first trimester to 31.9% and 26.6% at the second and third; the postpartum rate was 30.4%. These rates did not differ significantly from each other at the 0.05 level. Race/ethnicity was associated with misclassification status; white/non-Hispanic women were 87% less likely to be misclassified (p < .001). Conclusion: Misclassification of prenatal smoking status decreases as pregnancy progresses, though the observed rate change was not significant. Minority women may be at particular risk for non-disclosure of tobacco use. Biochemical validation should be considered when assessing perinatal tobacco use via self-report, given high misclassification rates throughout the perinatal period. Implications: These results demonstrate that regardless of trimester, more than one-quarter of tobacco-using pregnant women may not disclose tobacco use throughout pregnancy and early postpartum. Although the rate of misclassification decreased from first to third trimester and then increased in the immediate postpartum, these changes in misclassification rates were not significant. Minority groups may be at particular risk of misclassification compared with white/non-Hispanic women. Biochemical validation is warranted throughout pregnancy to encourage cessation as tobacco use is one of the most easily-modified risk factors for poor birth outcomes.

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32

Scott, Laura, and George Maldonado. "O4E.4 Application of probabilistic bias analysis to adjust for exposure misclassification in a cohort of trichlorophenol workers." Occupational and Environmental Medicine 76, Suppl 1 (April 2019): A40.2—A40. http://dx.doi.org/10.1136/oem-2019-epi.109.

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This method was developed to demonstrate the application of probabilistic bias analysis to quantify and adjust for exposure misclassification in a historical cohort mortality study of New Zealand trichlorophenol workers where exposure to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) was measured as a multi-level variable. Published exposure information available for this cohort of workers was used to specify the initial bias parameter distributions, which were then varied under 18 different scenarios to assess the potential impact of differing amounts of misclassification as well as both non-differential and differential exposure misclassification. For each scenario, each bias parameter distribution was sampled 50 000 times using Monte Carlo simulation techniques to generate adjusted counts of cases and non-cases of ischemic heart disease (IHD) by exposure group. These counts were then used to calculate odds ratios adjusted for exposure misclassification and the associated exposure misclassification error terms. Given the specified assumptions, the geometric mean (GM) adjusted odds ratio had a range of 2.89 to 5.05, and the GM error term ranged from 0.60 to 1.05. In all non-differential scenarios and scenarios in which non-cases had greater proportions of misclassification, the observed odds ratio of 3.05 was closer to the null (i.e., 1) than the GM adjusted odds ratio. For the differential simulations where cases had higher proportions of misclassification, the direction of the error was dependent on the level of misclassification error, with smaller proportions of misclassification resulting in the observed odds ratio being farther away from the null than the GM adjusted odds ratio. These findings demonstrate that probabilistic bias analysis of historical cohort mortality studies can be an effective tool for understanding trends in study error stemming from exposure misclassification and confirm the importance of quantifying potential sources of systematic error.

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33

Xu, Wei Xiang, Jing Xu, Xu Min Liu, and Rui Dong. "A Pruning Method Based on Conditional Misclassification." Applied Mechanics and Materials 44-47 (December 2010): 3448–52. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.3448.

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The methods of pruning have great influence on the effect of the decision tree. By researching on the pruning method based on misclassification, introduced the conception of condition misclassification and improved the standard of pruning. Propose the conditional misclassification pruning method for decision tree optimization and apply it in C4.5 algorithm. The experiment result shows that the condition misclassification pruning can avoid over pruned problem and non-enough pruned problem to some extent and improve the accurate of classification.

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Baghestani, Ahmad Reza, Mohamad Amin Pourhoseingholi, Sara Ashtari, Hadis Najafimehr, Luca Busani, and Mohammad Reza Zali. "Trend of Gastric Cancer after Bayesian Correction of Misclassification Error in Neighboring Provinces of Iran." Galen Medical Journal 8 (July 9, 2019): 1223. http://dx.doi.org/10.31661/gmj.v8i0.1223.

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Background: Some errors may occur in the disease registry system. One of them is misclassification error in cancer registration. It occurs because some of the patients from deprived provinces travel to their adjacent provinces to receive better healthcare without mentioning their permanent residence. The aim of this study was to re-estimate the incidence of gastric cancer using the Bayesian correction for misclassification across Iranian provinces. Materials and Methods: Data of gastric cancer incidence were adapted from the Iranian national cancer registration reports from 2004 to 2008. Bayesian analysis was performed to estimate the misclassification rate with a beta prior distribution for misclassification parameter. Parameters of beta distribution were selected according to the expected coverage of new cancer cases in each medical university of the country. Results: There was a remarkable misclassification with reference to the registration of cancer cases across the provinces of the country. The average estimated misclassification rate was between 15% and 68%, and higher rates were estimated for more deprived provinces. Conclusion: Misclassification error reduces the accuracy of the registry data, in turn causing underestimation and overestimation in the assessment of the risk of cancer in different areas. In conclusion, correcting the regional misclassification in cancer registry data is essential for discerning high-risk regions and making plans for cancer control and prevention. [GMJ.2019;8:e1223]

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35

Tzavidis, Nikos, and Yan-Xia Lin. "Estimating from cross-sectional categorical data subject to misclassification and double sampling: Moment-based, maximum likelihood and quasi-likelihood approaches." Journal of Applied Mathematics and Decision Sciences 2006 (March 2, 2006): 1–13. http://dx.doi.org/10.1155/jamds/2006/42030.

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We discuss alternative approaches for estimating from cross-sectional categorical data in the presence of misclassification. Two parameterisations of the misclassification model are reviewed. The first employs misclassification probabilities and leads to moment-based inference. The second employs calibration probabilities and leads to maximum likelihood inference. We show that maximum likelihood estimation can be alternatively performed by employing misclassification probabilities and a missing data specification. As an alternative to maximum likelihood estimation we propose a quasi-likelihood parameterisation of the misclassification model. In this context an explicit definition of the likelihood function is avoided and a different way of resolving a missing data problem is provided. Variance estimation for the alternative point estimators is considered. The different approaches are illustrated using real data from the UK Labour Force Survey and simulated data.

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36

Briggs, William, Matt Pocernich, and David Ruppert. "Incorporating Misclassification Error in Skill Assessment." Monthly Weather Review 133, no. 11 (November 1, 2005): 3382–92. http://dx.doi.org/10.1175/mwr3032.1.

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Abstract It is desirable to account for misclassification error of meteorological observations so that the true skill of the forecast can be assessed. Errors in observations can occur, among other places, in pilot reports of icing and in tornado spotting. Not accounting for misclassification error gives a misleading picture of the forecast’s true performance. An extension to the climate skill score test developed in Briggs and Ruppert is presented to account for possible misclassification error of the meteorological observation. This extension supposes a statistical misclassification-error model where “gold standard” data, or expert opinion, is available to characterize the misclassification-error characteristics of the observation. These model parameters are then inserted into the Briggs and Ruppert skill score for which a statistical test of significance can be performed.

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37

Brennan, Alana T., Kelly D. Getz, Daniel R. Brooks, and Matthew P. Fox. "An underappreciated misclassification mechanism: implications of nondifferential dependent misclassification of covariate and exposure." Annals of Epidemiology 58 (June 2021): 104–23. http://dx.doi.org/10.1016/j.annepidem.2021.02.007.

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38

Miller, W. G., J. M. McKenney, M. R. Conner, and V. M. Chinchilli. "Total error assessment of five methods for cholesterol screening." Clinical Chemistry 39, no. 2 (February 1, 1993): 297–304. http://dx.doi.org/10.1093/clinchem/39.2.297.

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Abstract We report the accuracy, imprecision, total analytical errors, and patient misclassification errors for cholesterol measured from capillary whole blood, venous whole blood, and venous plasma samples by five devices used in public cholesterol screening environments: Reflotron, Vision, Ektachem DT-60, QuickRead, and Liposcan. None of the methods met the National Cholesterol Education Program (NCEP) performance recommendations of 3% CV with 3% bias. The Vision and Reflotron methods used with venous samples gave individual results with total errors consistent with a combined CV and bias in the 4-5% range; capillary blood samples had total errors > 5% (combined CV and bias criteria). The DT-60 performance was near the 5% total error criterion for capillary samples and was > 5% for venous samples. Misclassification of individuals into desirable or referral groups for venous samples was as great as 5.1% for the DT-60, 5.7% for the Vision, and 7.1% for the Reflotron. Misclassifications for capillary blood samples were as great as 6.7%, 18.3%, and 14.1% for DT-60, Vision, and Reflotron, respectively. The QuickRead and Liposcan results were substantially poorer than those obtained by the other methods.

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Hughes, G., T. R. Gottwald, and K. Yamamura. "Survey Methods for Assessment of Citrus tristeza virus Incidence in Urban Citrus Populations." Plant Disease 86, no. 4 (April 2002): 367–72. http://dx.doi.org/10.1094/pdis.2002.86.4.367.

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This article concerns survey methodology for pathogens in urban citrus populations, motivated in particular by the need for assessments of Citrus tristeza virus (CTV) incidence. We envisage a large area R not devoted primarily to the commercial cultivation of citrus, that nevertheless has a substantial population of citrus trees. It is desired to sample the citrus population of area R in order to be able to make a statement about the level of infection of the population with CTV, or with particular isolates thereof. We describe a two-stage acceptance sampling scheme in which area R is divided into N sampling units, of which n are inspected. The size of the sampling units, while much smaller than R, is still large, so subsampling is carried out, introducing the possibility of misclassification of sampling units. To account for misclassification of sampling units, a larger number must be inspected than if it were assumed that there were no misclassifications. We describe the calculation of sample sizes required for subsampling within sampling units and for the total number of sampling units to be inspected, using parameters that can be adjusted to meet different specified regulatory scenarios.

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Haozous, Emily A., Carolyn J. Strickland, Janelle F. Palacios, and Teshia G. Arambula Solomon. "Blood Politics, Ethnic Identity, and Racial Misclassification among American Indians and Alaska Natives." Journal of Environmental and Public Health 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/321604.

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Misclassification of race in medical and mortality records has long been documented as an issue in American Indian/Alaska Native data. Yet, little has been shared in a cohesive narrative which outlines why misclassification of American Indian/Alaska Native identity occurs. The purpose of this paper is to provide a summary of the current state of the science in racial misclassification among American Indians and Alaska Natives. We also provide a historical context on the importance of this problem and describe the ongoing political processes that both affect racial misclassification and contribute to the context of American Indian and Alaska Native identity.

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Peters, A., B. Lausen, G. Michelson, and O. Gefeller. "Diagnosis of Glaucoma by Indirect Classifiers." Methods of Information in Medicine 42, no. 01 (2003): 99–103. http://dx.doi.org/10.1055/s-0038-1634214.

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Summary Objectives: Demonstration of the applicability of a framework called indirect classification to the example of glaucoma classification. Indirect classification combines medical a priori knowledge and statistical classification methods. The method is compared to direct classification approaches with respect to the estimated misclassification error. Methods: Indirect classification is applied using classification trees and the diagnosis of glaucoma. Misclassification errors are reduced by bootstrap aggregation. As direct classification methods linear discriminant analysis, classification trees and bootstrap aggregated classification trees are utilized in the problem of glaucoma diagnosis. Misclassification rates are estimated via 10-fold cross-validation. Results: Indirect classification techniques reduce the misclassification error in the context of glaucoma classification compared to direct classification methods. Conclusions: Embedding a priori knowledge into statistical classification techniques can improve misclassification results. Indirect classification offers a framework to realize this combination.

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Armbruster, Marco, Markus Guba, Joachim Andrassy, Markus Rentsch, Vincent Schwarze, Johannes Rübenthaler, Thomas Knösel, Jens Ricke, and Harald Kramer. "Measuring HCC Tumor Size in MRI—The Sequence Matters!" Diagnostics 11, no. 11 (October 28, 2021): 2002. http://dx.doi.org/10.3390/diagnostics11112002.

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Background: The aim of this paper was to assess and compare the accuracy of common magnetic resonance imaging (MRI) pulse sequences in measuring the lesion sizes of hepatocellular carcinomas (HCCs) with respect to the Milan criteria and histopathology as a standard of reference. Methods: We included 45 patients with known HCC who underwent contrast-enhanced MRI of the liver prior to liver transplantation or tumor resection. Tumor size was assessed pathologically for all patients. The MRI protocol contained axial T2-weighted images as well as T1-weighted imaging sequences before and after application of Gd-EOB-DTPA. Tumor diameters, the sharpness of lesions, and the presence of artifacts were evaluated visually on all available MRI sequences. MRI measurements and pathologically assessed tumor dimensions were correlated using Pearson’s correlation coefficient and Bland–Altman plots. The rate of misclassifications following Milan criteria was assessed. Results: The mean absolute error (in cm) of MRI size measurements in comparison to pathology was the smallest for the hepatobiliary phase T1-weighted acquisition (0.71 ± 0.70 cm, r = 0.96) and largest for the T2w turbo-spin-echo (TSE) sequence (0.85 ± 0.78 cm, r = 0.94). The misclassification rate regarding tumor size under the Milan criteria was lowest for the T2w half-Fourier acquisition single-shot turbo spin-echo sequence and the hepatobiliary phase T1w acquisition (each 8.6%). The highest rate of misclassification occurred in the portal venous phase T1w acquisition and T2w TSE sequence (each 14.3%). Conclusions: The hepatobiliary phase T1-weighted acquisition seems to be most accurate among commonly used MRI sequences for measuring HCC tumor size, resulting in low rates of misclassification with respect to the Milan criteria.

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Fan, Liqiong, Sharon D. Yeatts, Bethany J. Wolf, Leslie A. McClure, Magdy Selim, and Yuko Y. Palesch. "The impact of covariate misclassification using generalized linear regression under covariate–adaptive randomization." Statistical Methods in Medical Research 27, no. 1 (November 23, 2015): 20–34. http://dx.doi.org/10.1177/0962280215616405.

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Under covariate adaptive randomization, the covariate is tied to both randomization and analysis. Misclassification of such covariate will impact the intended treatment assignment; further, it is unclear what the appropriate analysis strategy should be. We explore the impact of such misclassification on the trial’s statistical operating characteristics. Simulation scenarios were created based on the misclassification rate and the covariate effect on the outcome. Models including unadjusted, adjusted for the misclassified, or adjusted for the corrected covariate were compared using logistic regression for a binary outcome and Poisson regression for a count outcome. For the binary outcome using logistic regression, type I error can be maintained in the adjusted model, but the test is conservative using an unadjusted model. Power decreased with both increasing covariate effect on the outcome as well as the misclassification rate. Treatment effect estimates were biased towards the null for both the misclassified and unadjusted models. For the count outcome using a Poisson model, covariate misclassification led to inflated type I error probabilities and reduced power in the misclassified and the unadjusted model. The impact of covariate misclassification under covariate–adaptive randomization differs depending on the underlying distribution of the outcome.

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Carter, Elizabeth A., Lauren J. Waterhouse, Roy Xiao, and Randall S. Burd. "Use of Payer as a Proxy for Health Insurance Status on Admission Results in Misclassification of Insurance Status among Pediatric Trauma Patients." American Surgeon 82, no. 2 (February 2016): 146–51. http://dx.doi.org/10.1177/000313481608200218.

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The purpose of this study was to quantify health insurance misclassification among children treated at a pediatric trauma center and to determine factors associated with misclassification. Demographic, medical, and financial information were collected for patients at our institution between 2008 and 2010. Two health insurance variables were created: true (insurance on hospital admission) and payer (source of payment). Multivariable logistic regression was used to determine which factors were independently associated with health insurance misclassification. The two values of health insurance status were abstracted from the hospital financial database, the trauma registry, and the patient medical record. Among 3630 patients, 123 (3.4%) had incorrect health insurance designation. Misclassification was highest in patients who died: 13.9 per cent among all deaths and 30.8 per cent among emergency department deaths. The adjusted odds of misclassification were 6.7 (95% confidence interval: 1.7, 26.6) among patients who died and 16.1 (95% confidence interval: 3.2, 80.77) among patients who died in the emergency department. Using payer as a proxy for health insurance results in misclassification. Approaches are needed to accurately ascertain true health insurance status when studying the impact of insurance on treatment outcomes.

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Balan, Lacramioara, and Rajesh Paleti. "Modified Mixed Generalized Ordered Response Model to Handle Misclassification in Injury Severity Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 30 (September 11, 2018): 53–63. http://dx.doi.org/10.1177/0361198118796352.

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Traditional crash databases that record police-reported injury severity data are prone to misclassification errors. Ignoring these errors in discrete ordered response models used for analyzing injury severity can lead to biased and inconsistent parameter estimates. In this study, a mixed generalized ordered response (MGOR) model that quantifies misclassification rates in the injury severity variable and adjusts the bias in parameter estimates associated with misclassification was developed. The proposed model does this by considering the observed injury severity outcome as a realization from a discrete random variable that depends on true latent injury severity that is unobservable to the analyst. The model was used to analyze misclassification rates in police-reported injury severity in the 2014 General Estimates System (GES) data. The model found that only 68.23% and 62.75% of possible and non-incapacitating injuries were correctly recorded in the GES data. Moreover, comparative analysis with the MGOR model that ignores misclassification not only has lower data fit but also considerable bias in both the parameter and elasticity estimates. The model developed in this study can be used to analyze misclassification errors in ordinal response variables in other empirical contexts.

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Cakici, N. Meltem, and Paurav Shukla. "Country-of-origin misclassification awareness and consumers’ behavioral intentions." International Marketing Review 34, no. 3 (May 8, 2017): 354–76. http://dx.doi.org/10.1108/imr-08-2015-0178.

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Purpose Extant research shows that consumers regularly misclassify country-of-origin (COO) associated with brands. The purpose of this paper is to examine changes in behavioral intentions (i.e. purchase intentions for self and others and brand judgments) when consumers are made aware that they have misclassified the COO and then are informed of the brand’s correct origin. Drawing on cognitive dissonance theory, the authors also explore the moderating roles of consumer affinity, animosity, and product knowledge. Design/methodology/approach Two experiments test the direct and moderating effects of COO misclassification awareness on behavioral intentions. Findings The findings show detrimental effects of misclassification on behavioral intentions when consumers have high affinity with misclassified COO. Moreover, the experiments demonstrate a significantly greater decrease in behavioral intentions among experts than novices in the low-affinity condition and the reverse effect in the high-affinity condition. Practical implications The negative effects of COO misclassification on consumer behavioral intentions highlight the need for managers to proactively avoid misclassification. The findings should also aid managers in developing responsive marketing campaigns that consider consumer affinity, animosity, and level of product knowledge. Originality/value This research is the first to compare consumer behavioral responses before and after COO misclassification awareness. The study demonstrates that cognitive dissonance underpins the process of misclassification. It also contributes to COO literature by examining the interaction of consumer affinity and animosity with product knowledge and their influence on consumer behavior in the case of COO misclassification.

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Lewbel, Arthur. "IDENTIFICATION OF THE BINARY CHOICE MODEL WITH MISCLASSIFICATION." Econometric Theory 16, no. 4 (August 2000): 603–9. http://dx.doi.org/10.1017/s0266466600164060.

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Misclassification in binary choice (binomial response) models occurs when the dependent variable is measured with error, that is, when an actual “one” response is sometimes recorded as a zero and vice versa. This paper shows that binary response models with misclassification are semiparametrically identified, even when the probabilities of misclassification depend in unknown ways on model covariates and the distribution of the errors is unknown.

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Cummings, P. "Misclassification of seat belt use." Injury Prevention 9, no. 1 (March 1, 2003): 91. http://dx.doi.org/10.1136/ip.9.1.91.

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MacLehose, Richard F., Andrew F. Olshan, Amy H. Herring, Margaret A. Honein, Gary M. Shaw, and Paul A. Romitti. "Bayesian Methods for Correcting Misclassification." Epidemiology 20, no. 1 (January 2009): 27–35. http://dx.doi.org/10.1097/ede.0b013e31818ab3b0.

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Gupta, Pushpa Lata, Riley T. James, and Thomas J. White. "Misclassification Probabilities for Quadratic Discrimination." SIAM Journal on Scientific and Statistical Computing 7, no. 4 (October 1986): 1400–1417. http://dx.doi.org/10.1137/0907093.

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