TY - JOUR
T1 - Model-based clustering for assessing the prognostic value of imaging biomarkers and mixed type tests
AU - Wang, Zheyu
AU - Sebestyen, Krisztian
AU - Monsell, Sarah E.
N1 - Funding Information:
Dr. Wang was supported partially by NACC project 2015-JI-05 and by NIH/NCI Grant P30CA006973. The NACC database is funded by NIA/NIH Grant U01 AG016976. NACC data are contributed by the NIA-funded ADCs (listed individually in the Supplement (see Appendix A)).
Publisher Copyright:
© 2016 Elsevier B.V.
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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.
KW - Biomarkers
KW - Diagnostic tests
KW - Differential covariate effect
KW - Finite mixture
KW - Imperfect gold standard
KW - Latent variable model
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U2 - 10.1016/j.csda.2016.10.026
DO - 10.1016/j.csda.2016.10.026
M3 - Article
C2 - 28966420
AN - SCOPUS:85008191041
SN - 0167-9473
VL - 113
SP - 125
EP - 135
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -