Screening without a "Gold Standard": The Hui-Walter Paradigm Revisited

The authors develop an approach based on sampling two populations that yields increasingly accurate inferences as the sample size increases and discusses a recent article in which a Bayesian approach to this problem was developed based on data that are sampled from a single population.

Abstract

The authors consider screening populations with two screening tests but where a definitive "gold standard" is not readily available. They discuss a recent article in which a Bayesian approach to this problem is developed based on data that are sampled from a single population. It was subsequently pointed out that such inferences will not necessarily be accurate in the sense that standard errors for parameters may not decrease as n increases. This problem will generally occur when the data are insufficient to estimate all of the parameters as is the case when screening a single population with two tests. If both tests are applied to units sampled from two populations, however, this particular difficulty disappears. In this article the authors further examine this issue and develop an approach based on sampling two populations that yields increasingly accurate inferences as the sample size increases.