Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests

Abstract

A model-based clustering method is proposed to address two research aims in Alzheimer’s disease (AD): to evaluate the accuracy of imaging biomarkers in AD prognosis, and to integrate biomarker information and standard clinical test results into the diagnoses. One challenge in such biomarker studies is that it is often desired or necessary to conduct the evaluation without relying on clinical diagnoses or some other standard references. This is because (1) biomarkers may provide prognostic information long before any standard reference can be acquired; (2) these references are often based on or provide unfair advantage to standard tests. Therefore, they can mask the prognostic value of a useful biomarker, especially when the biomarker is much more accurate than the standard tests. In addition, the biomarkers and existing tests may be of mixed type and vastly different distributions. A model-based clustering method based on finite mixture modeling framework is introduced. The model allows for the inclusion of mixed typed manifest variables with possible differential covariates to evaluate the prognostic value of biomarkers in addition to standard tests without relying on potentially inaccurate reference diagnoses. Maximum likelihood parameter estimation is carried out via the EM algorithm. Accuracy measures and the ROC curves of the biomarkers are derived subsequently. Finally, the method is illustrated with a real example in AD.

AD risk score for the early phases of disease based on unsupervised machine learning

“…This feature allowed us to: (1) examine the contribution of each marker domain to the underlying burden of disease, and (2) examine the portability of the derived score to an external dataset in which only some of the markers are available, as long as marker data were missing at random. For more details about this unsupervised machine learning approach see Appendix B in supporting information and Wang et al 9 and Wang and Zhu. 10…”

“…The present study applies an unsupervised machine learning approach to develop an AD progression risk score, using a recently developed method tailored to AD. 9,10 It allows for synchronizing…”

“…The present study applies an unsupervised machine learning approach to develop an AD progression risk score, using a recently developed method tailored to AD 9,10 . It allows for synchronizing measurements of different scales and different biomarker domains, and with potentially heterogeneous and correlated distributions.…”