AbstractMotivationPrecision and recall have become very popular classification accuracy metrics in the statistical learning literature. These metrics are ordinarily defined under the assumption that the data are sampled randomly from the mixture of the populations. However, observational case-control studies for biomarker discovery often collect data that are sampled separately from the case and control populations, particularly in the case of rare diseases. This discrepancy may introduce severe bias in classifier accuracy estimation.ResultsWe demonstrate, using both analytical and numerical methods, that classifier precision estimates can display strong bias under separating sampling, with the bias magnitude depending on the difference between the case prevalences in the data and in the actual population. We show that this bias is systematic in the sense that it cannot be reduced by increasing sample size. If information about the true case prevalence is available from public health records, then a modified precision estimator is proposed that displays smaller bias, which can in fact be reduced to zero as sample size increases under regularity conditions on the classification algorithm. The accuracy of the theoretical analysis and the performance of the proposed precision estimator under separate sampling are investigated using synthetic and real data from observational case-control studies. The results confirmed that the proposed precision estimator indeed becomes unbiased as sample size increases, while the ordinary precision estimator may display large bias, particularly in the case of rare diseases.AvailabilityExtra plots are available as Supplementary Materials.Author summaryBiomedical data are often sampled separately from the case and control populations, particularly in the case of rare diseases. Precision is a popular classification accuracy metric in the statistical learning literature, which implicitly assumes that the data are sampled randomly from the mixture of the populations. In this paper we study the bias of precision under separate sampling using theoretical and numerical methods. We also propose a precision estimator for separate sampling in the case when the prevalence is known from public health records. The results confirmed that the proposed precision estimator becomes unbiased as sample size increases, while the ordinary precision estimator may display large bias, particularly in the case of rare diseases. In the absence of any knowledge about disease prevalence, precision estimates should be avoided under separate sampling.
Consistent numerical methods for state and control constrained trajectory optimisation with parameter dependency
