Nonparametric Finite Mixture: Applications in Overcoming Misclassification Bias

• Published in: Journal of the American Statistical Association Vol. 119; no. 547; pp. 2269 - 2281
• Main Authors: Ye, Zi, Harrar, Solomon W.
• Format: Journal Article
• Language: English
• Published: Alexandria Taylor & Francis 02.07.2024; Taylor & Francis Ltd
• Subjects: Asthma; Asymptotic; Asymptotic methods; Asymptotic properties; Bias; Diagnostic misclassification; Diagnostic software; Error analysis; Gene expression; Nonparametric relative effect; Ordinal data; patients; Precision medicine; probability; Profiles; Research subjects; Simulation; Statistical analysis; Statistical inference; Statistical methods; Statistics; Validated data
• ISSN: 0162-1459; 1537-274X; 1537-274X
• DOI: 10.1080/01621459.2023.2256501

Investigating the differential effect of treatments in groups defined by patient characteristics is of paramount importance in personalized medicine research. In some studies, participants are first classified as having or not of the characteristic of interest by diagnostic tools, but such classifie...