Nonparametric Finite Mixtures for Overcoming Biomarker-Error Bias

Solomon W. Harrar

Personalized medicine research involves investigating the differential effect of treatments in patient groups defined by specific characteristics. In enrichment trials, participants are stratified based on biomarkers to assess the effectiveness of treatments on these groups. However, biomarkers are susceptible to misclassification errors, leading to bias. We propose nonparametric methods to estimate treatment effects and quantify the bias due to biomarker misclassification errors. Our methods are applicable to outcomes measured on ordinal, discrete, or continuous scales, without requiring assumptions such as the existence of moments. Simulation results show significant improvements in bias reduction, coverage probability, and power compared to existing methods. We illustrate the application of our methods using gene expression profiling of bronchial airway brushing in asthmatic and healthy control subjects.

I will use the first 10-15 minutes to share a brief account of my fall 2022 sabbatical experience.