Correcting for misclassification for a monotone disease process with an application in dental research

Abstract

Motivated by a longitudinal oral health study, we evaluate the performance of binary Markov models in which the response variable is subject to an unconstrained misclassification process and follows a monotone or progressive behavior. Theoretical and empirical arguments show that the simple version of the model can be used to estimate the prevalence, incidences, and misclassification parameters without the need of external information and that the incidence estimators associated with the model outperformed approaches previously proposed in the literature. We propose an extension of the simple version of the binary Markov model to describe the relationship between the covariates and the prevalence and incidence allowing for different classifiers. We implemented a Bayesian version of the extended model and show that, under the settings of our motivating example, the parameters can be estimated without any external information. Finally, the analyses of the motivating problem are presented.

Modeling of Multivariate Monotone Disease Processes in the Presence of Misclassification

“…It is important to stress that in a longitudinal setting, unlike cross sectional studies, the model parameters might be estimated without the use of external information about the misclassification parameters. For instance, García-Zattera et al (2010) showed that under simple restrictions on the parameter space, the model parameters associated with an inhomogeneous HMM for monotone responses are identified by the available data. They also proposed a univariate model to account for predictors allowing for irregularly spaced time intervals and different classifiers.…”

“…Hidden Markov models (HMM) for the analysis of misclassified alternating longitudinal responses has been considered in the literature by Cook, Ng, and Meade (2000), Rosychuk and Thompson (2001), Rosychuk and Thompson (2003), Nagelkerke, Chunge, and Kinot (1990), and Rosychuk and Islam (2009), whereas Espeland, Murphy, and Leverett (1988), Espeland, Platt, and Gallagher (1989), Schmid, Segal, and Rosner (1994), Singh and Rao (1995), Albert, Hunsberger, and Biro (1997), and García-Zattera et al (2010) addressed the problem of misclassified monotone longitudinal responses. It is important to stress that in a longitudinal setting, unlike cross sectional studies, the model parameters might be estimated without the use of external information about the misclassification parameters.…”

“…In the context of longitudinal univariate categorical data, generalized linear mixed models (see, e.g., Neuhaus 2002), generalized estimating equation (GEE)-based approaches (see, e.g., Neuhaus 2002), and transition models (see, e.g., García-Zattera et al 2010) have been proposed for correcting for misclassification. Due to the monotone nature of our motivating problem and because the main scientific objective here is the incidence estimation, we restrict ourselves to the latter class of models, where the parameters have a direct interpretation in terms of the conditional probabilities of developing CE in a given time interval.…”

Healthcare data mining from multi-source data

“…Label uncertainty has commonly been found in clinical judgments due to expert subjectivity and inadequate information [104]. Often, it is handled as noise, so the task has been to detect and correct such mislabeling [87,107,45]. However, in the case of multiple, non-exclusive medical conditions [82], such as comorbidity, it makes more sense to treat labels with degrees of certainty rather than forcing them to belong to one "true" class, because there is no such thing as a single true class in this kind of scenario.…”

“…Such noise is usually regarded as mislabeling to be detected and corrected [87,107,45]. For example, Garca-Zattera et al employed binary Markov 2.2 DATA MINING ON HEALTHCARE DATA 25 models to estimate misclassification parameters for dental research [45].…”