Analysis of Recurrent Polyp Data in the Presence of Misclassification

Several standard methods are available to analyze and estimate parameters of count data. None of these methods are designed to account for potential misclassification of the data, where counts are observed or recorded as higher or lower than their actual value. These false counts can result in erroneous conclusions and biased estimates. For this paper, a standard estimation model was modified in several ways in order to incorporate each misclassification mechanism. The probability distribution of the observed data was derived and combined with informative distributions for the misclassification parameters. Once this additional information was taken into account, a distribution of observed data conditional on only the parameter of interest was obtained. By incorporating information about the misclassification mechanisms, the resulting estimation will be more accurate than the standard methods. To demonstrate the flexibility of this approach, data from a count distribution affected by various misclassification mechanisms were simulated. Each dataset was analyzed by several standard estimation methods and an appropriate new method. The results from all simulated data were compared, and the impact of each mechanism in regards to each estimation method was discussed. Data from a colorectal polyp prevention study were also analyzed with all available methods to showcase the incorporation of additional covariates