Developments and debates on latent variable modeling in diagnostic studies when there is no gold standard

Abstract

Latent variable modeling is often used in diagnostic studies where a gold standard reference test is not available. Its applications have become increasing popular with the fast discovery of novel biomarkers and the effort to improve healthcare for each individual. This paper attempt to provide a review on current developments and debates of these models with a focus in diagnostic studies and to discuss the value as well as cautionary considerations in the applications of these models.

Keywords:

• Finite mixture
• latent variable
• diagnostic tests
• biomarkers
• imperfect gold standard
• validity
• identifiability

Disclosure statement

No potential conflict of interest was reported by the author.

Additional information

Notes on contributors

Zheyu Wang

Zheyu Wang, Ph.D., is a Biostatistician, Assistant Professor in the Division of Biostatistics & Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, and Department of Biostatistics at the Johns Hopkins University. She is also the Biostatistics Core Leader of the Armstrong Institute Center for Diagnostic Excellence at the Johns Hopkins University and lead statistician on a number of diagnostic studies. Her research focuses on statistical methodologies for biomarker and diagnostic test evaluation, with a special interest in latent variable models.