Nonparametric estimation of distributions and diagnostic accuracy based on group‐tested results with differential misclassification

Abstract

This article concerns the problem of estimating a continuous distribution in a diseased or nondiseased population when only group-based test results on the disease status are available. The problem is challenging in that individual disease statuses are not observed and testing results are often subject to misclassification, with further complication that the misclassification may be differential as the group size and the number of the diseased individuals in the group vary. We propose a method to construct nonparametric estimation of the distribution and obtain its asymptotic properties. The performance of the distribution estimator is evaluated under various design considerations concerning group sizes and classification errors. The method is exemplified with data from the National Health and Nutrition Examination Survey study to estimate the distribution and diagnostic accuracy of C-reactive protein in blood samples in predicting chlamydia incidence.

Accommodating detection limits of multiple exposures in environmental mixture analyses: an overview of statistical approaches

“…In our analysis, we did not include sampling weights because our goal was to understand the impact of different LOD accommodation approaches on downstream mixture analysis as a comparison with Gibson et al [ 23 ], which did not incorporate sampling weights. To incorporate sampling weights, Zhang et al [ 53 ] sampled one bootstrap sample with replacement from the NHANES data, with probabilities proportional to the sampling weights to test the results. We also implemented the same procedure.…”

Group Testing Can Improve the Cost-Efficiency of Prospective-Retrospective Biomarker Studies

“…This issue has been considered by several authors in different contexts. For example, McMahan et al (35) proposed a mechanistic modeling approach in which pool testing error rates are estimated from a rich set of low-level assay data; Hung and Swallow (36) and Zhang et al (37) postulate that the pool testing error rates are known functions of the pool size and the number of diseased individuals in the pool. Further research is warranted on how to incorporate the dilution effect in GT designs of P-R biomarker studies.…”

Nested Group Testing Procedure

“…Screening for diseases especially those with low prevalences can be very costly and time-consuming. Group testing, as a cost-effective strategy, has been widely used in many fields to identify diseased subjects, for example, genetics [ 10 ], infectious disease screening [ 9 , 11 , 26 ], pharmaceutical industries [ 14 ], and agriculture [ 20 ], among others. Such a strategy was first used by [ 6 ] to test pooled blood samples for syphilis antigen in the US army recruitment.…”