Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.
ROC curve
likelihood ratio
multiple biomarkers
smoothing spline density estimation
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
30 Mar 2024
30 Mar 2024
Historique:
revised:
14
11
2023
received:
03
05
2023
accepted:
11
01
2024
medline:
18
3
2024
pubmed:
31
1
2024
entrez:
31
1
2024
Statut:
ppublish
Résumé
The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1372-1383Subventions
Organisme : National Science Foundation
ID : DMS-1916174
Informations de copyright
© 2024 John Wiley & Sons Ltd.
Références
Su JQ, Liu JS. Linear combinations of multiple diagnostic markers. J Am Stat Assoc. 1993;88(424):1350-1355.
Pepe M, Thompson M. Combining diagnostic test results to increase accuracy. Biostatistics. 2000;1(2):123-140.
Liu C, Liu A, Halabi S. A min-max combination of biomarkers to improve diagnostic accuracy. Stat Med. 2011;30(16):2005-2014.
Yin J, Tian L. Optimal linear combinations of multiple diagnostic biomarkers based on Youden index. Stat Med. 2014;33(8):1426-1440.
Yan Q, Bantis LE, Stanford JL, Feng Z. Combining multiple biomarkers linearly to maximize the partial area under the ROC curve. Stat Med. 2018;37(4):627-642.
McIntosh MW, Pepe MS. Combining several screening tests: optimality of the risk score. Biometrics. 2002;58(3):657-664.
Neyman J, Pearson ES. On the problem of the most efficient tests of statistical hypotheses. Philos Trans R Soc A. 1933;231:289-337.
Qin J, Zhang B. Best combination of multiple diagnostic tests for screening purposes. Stat Med. 2010;29(28):2905-2919.
Chen B, Li P, Qin J, Yu T. Using a monotonic density ratio model to find the asymptotically optimal combination of multiple diagnostic tests. J Am Stat Assoc. 2016;111(514):861-874.
Liu D, Han Y, Liu A. Marginal, conditional, and pseudo likelihood ratio approaches for biomarker combination to predict a binary disease outcome. Stat Med. 2022;41(14):2574-2585.
Ravikumar P, Lafferty J, Liu H, Wasserman L. Sparse additive models. J R Stat Soc Series B Stat Methodol. 2009;71(5):1009-1030.
Fong Y, Yin S, Huang Y. Combining biomarkers linearly and nonlinearly for classification using the area under the ROC curve. Stat Med. 2016;35(21):3792-3809.
Gu C. Smoothing Spline ANOVA Models. New York, NY: Springer; 2013.
Fan J, Feng Y, Song R. Nonparametric independence screening in sparse ultra-high-dimensional additive models. J Am Stat Assoc. 2011;106(494):544-557.
Zhu LP, Li L, Li R, Zhu LX. Model-free feature screening for ultrahigh-dimensional data. J Am Stat Assoc. 2011;106(496):1464-1475.
Liu Y, Shen X, Doss H. Multicategory ψ$$ \psi $$-learning and support vector machine: computational tools. J Comput Graph Stat. 2005;14(1):219-236.
Mills J, Hediger M, Molloy C, et al. Elevated levels of growth-related hormones in autism and autism spectrum disorder. Clin Endocrinol (Oxf). 2007;67(2):230-237.
Kooperberg C, Stone CJ. A study of logspline density estimation. Comput Stat Data Anal. 1991;12(3):327-347.
Ramsay JO, Silverman BW. Functional Data Analysis. 2nd ed. New York, NY: Springer Science+Business Media, Inc; 2005.
Cheng G, Shang Z. Local and global asymptotic inference in smoothing spline models. Ann Stat. 2013;41(5):2608-2638.