Partially Identified Prevalence Estimation under Misclassification using the Kappa Coefficient

We discuss a new strategy for prevalence estimation in the presence of misclassification. Our method is applicable when misclassification probabilities are unknown but independent replicate measurements are available. This yields the kappa coefficient, which indicates the agreement between the two measurements. From this information, a direct correction for misclassification is not feasible due to non-identifiability. However, it is possible to derive estimation intervals relying on the concept of partial identification. These intervals give interesting insights into possible bias due to misclassification. Furthermore, confidence intervals can be constructed. Our method is illustrated in several theoretical scenarios and in an example from oral health, where prevalence estimation of caries in children is the issue

Correcting for misclassification error in gross flows using double sampling: moment-based inference vs. likelihood-based inference

Gross flows are discrete longitudinal data that are defined as transition counts, between a finite number of states, from one point in time to another. We discuss the analysis of gross flows in the presence of misclassification error via double sampling methods. Traditionally, adjusted for misclassification error estimates are obtained using a moment-based estimator. We propose a likelihood-based approach that works by simultaneously modeling the true transition process and the misclassification error process within the context of a missing data problem. Monte-Carlo simulation results indicate that the maximumlikelihood estimator is more efficient than the moment-based estimator

Asymptotic Variance Estimation for the Misclassification SIMEX

Most epidemiological studies suffer from misclassification in the response and/or the covariates. Since ignoring misclassification induces bias on the parameter estimates, correction for such errors is important. For measurement error, the continuous analog to misclassification, a general approach for bias correction is the SIMEX (simulation extrapolation) originally suggested by Cook and Stefanski (1994). This approach has been recently extended to regression models with a possibly misclassified categorical response and/or the covariates by Küchenhoff et al. (2005), and is called the MC-SIMEX approach. To assess the importance of a regressor not only its (corrected) estimate is needed, but also its standard error. For the original SIMEX approach. Carroll et al. (1996) developed a method for estimating the asymptotic variance. Here we derive the asymptotic variance estimators for the MC-SIMEX approach, extending the methodology of Carroll et al. (1996). We also include the case where the misclassification probabilities are estimated by a validation study. An extensive simulation study shows the good performance of our approach. The approach is illustrated using an example in caries research including a logistic regression model, where the response and a binary covariate are possibly misclassified

APPLICATION OF RECURSIVE PARTITIONING TO AGRICULTURAL CREDIT SCORING

Recursive Partitioning Algorithm (RPA) is introduced as a technique for credit scoring analysis, which allows direct incorporation of misclassification costs. This study corroborates nonagricultural credit studies, which indicate that RPA outperforms logistic regression based on within-sample observations. However, validation based on more appropriate out-of-sample observations indicates that logistic regression is superior under some conditions. Incorporation of misclassification costs can influence the creditworthiness decision.finance, credit scoring, misclassification, recursive partitioning algorithm, Agricultural Finance,

The Problem of Worker Misclassification

Employee misclassification is a significant problem that continues to plague the labor market. Unscrupulous and unknowing employers alike are costing individual workers and society tremendously. Not only are workers missing out on legal protections, but society is losing contributions from employers that should be paid into different employment systems (payroll taxes, unemployment benefits, workers compensation benefits, etc.)