On the Bias of Precision Estimation Under Separate Sampling.

ABSTRACT: Observational case-control studies for biomarker discovery in cancer studies often collect data that are sampled separately from the case and control populations. We present an analysis of the bias in the estimation of the precision of classifiers designed on separately sampled data. The analysis consists of both theoretical and numerical results, which show that classifier precision estimates can display strong bias under separating sampling, with the bias magnitude depending on the difference between the true case prevalence in the population and the sample prevalence in the data. 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 prec

SUBMITTER: Xie S 

PROVIDER: S-EPMC6636226 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature