The diagnostic likelihood ratio function and modified test for trend: Identifying, evaluating, and validating nontraditional biomarkers in case-control studies.
ROC curve
clinical decision making
diagnostic biomarkers
diagnostic likelihood ratio
multinomial regression
test for trend
Journal
Statistics in medicine
ISSN: 1097-0258
Titre abrégé: Stat Med
Pays: England
ID NLM: 8215016
Informations de publication
Date de publication:
20 Dec 2023
20 Dec 2023
Historique:
revised:
05
06
2023
received:
10
03
2021
accepted:
05
09
2023
medline:
20
11
2023
pubmed:
22
9
2023
entrez:
22
9
2023
Statut:
ppublish
Résumé
The ROC curve and its associated summary statistic, the AUC, are used to identify informative diagnostic biomarkers under the assumption that risk of disease is a monotone function of the biomarker. We refer to biomarkers that meet this assumption as traditional, and those that do not as nontraditional. Nontraditional biomarkers most often arise when both low and high biomarker values are associated with an outcome of interest, such as blood pressure with medical complications or leukocyte count with ICU prognosis. Since nontraditional biomarkers do not meet the assumptions for ROC-based analyses, we propose using the discrete diagnostic likelihood ratio (DLR) function to evaluate a wider class of informative biomarkers. We obtain the DLR function using the multinomial logistic regression (MLR) model to improve upon existing estimation techniques, and implement a likelihood ratio test to identify candidate informative traditional and nontraditional biomarkers. We propose a modification of the Cochran-Armitage test for trend that separates biomarkers deemed informative into traditional and nontraditional categories. The statistical properties of the likelihood ratio test and modified test for trend are explored under simulation. Together, these methods achieve the identification, evaluation, and validation of biomarkers from early discovery research. Finally, we show that incorporating covariates into the MLR model results in a covariate-adjusted DLR function that is useful for integrating multiple sources of information in clinical decision making. The methods are applied to gene expression data from subjects with high grade serous ovarian cancer, where stage, early stage vs late stage, is the outcome of interest.
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5313-5337Subventions
Organisme : NIMH NIH HHS
ID : 5-T32-MH-065218-15
Pays : United States
Organisme : NIMH NIH HHS
ID : 5-T32-MH-065218-15
Pays : United States
Informations de copyright
© 2023 John Wiley & Sons Ltd.
Références
Heffernan AJ, Denny KJ. Host diagnostic biomarkers of infection in the ICU: Where are we and where are we going? Curr Infect Dis Rep. 2021;23:1-11.
McQuade JL, Daniel CR, Helmink BA, Wargo JA. Modulating the microbiome to improve therapeutic response in cancer. Lancet Oncol. 2019;20(2):e77-e91.
Bodor JN, Boumber Y, Borghaei H. Biomarkers for immune checkpoint inhibition in non-small cell lung cancer (NSCLC). Cancer. 2020;126(2):260-270.
Alarcón-Zendejas AP, Scavuzzo A, Jiménez-Ríos MA, et al. The promising role of new molecular biomarkers in prostate cancer: From coding and non-coding genes to artificial intelligence approaches. Prostate Cancer Prostatic Dis. 2022;25(3):431-443.
Hanley J, McNeil B. The meaning and use of the area under the ROC curve. Radiology. 1983;143:29-36.
Obuchowski NA, Bullen JA. Receiver operating characteristic (ROC) curves: Review of methods with applications in diagnostic medicine. Phys Med Biol. 2018;63(7):07TR01.
Janssens ACJW, Martens FK. Reflection on modern methods: Revisiting the area under the ROC Curve. Int J Epidemiol. 2020;49(4):1397-1403. doi:10.1093/ije/dyz274
Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation. 2007;115(7):928-935. doi:10.1161/circulationaha.106.672402
Halligan S, Altman DG, Mallett S. Disadvantages of using the area under the receiver operating characteristic curve to assess imaging tests: A discussion and proposal for an alternative approach. Eur Radiol. 2015;25(4):932-939. doi:10.1007/s00330-014-3487-0
Pepe MS, Gu JW, Morris DE. The potential of genes and other markers to inform about risk. Cancer Epidemiol Biomark Prev. 2010;19(3):655-665. doi:10.1158/1055-9965.epi-09-0510
Parodi S, Muselli M, Carlini B, et al. Restricted ROC curves are useful tools to evaluate the performance of tumour markers. Stat Methods Med Res. 2016;25(1):294-314. doi:10.1177/0962280212452199
Mossman D, Peng H. Constructing “Proper” ROCs from ordinal response data using weighted power functions. Med Decis Mak. 2014;34(4):523-535. doi:10.1177/0272989X13503046
Martínez-Camblor P, Corral N, Rey C, Pascual J, Cernuda-Morollón E. Receiver operating characteristic curve generalization for non-monotone relationships. Stat Methods Med Res. 2017;26(1):113-123. doi:10.1177/0962280214541095
Ho KM. Effect of non-linearity of a predictor on the shape and magnitude of its receiver-operating-characteristic curve in predicting a binary outcome. Sci Rep. 2017;7(1):2045-2322.
Hilden J. The Area under the ROC Curve and Its Competitors. Med Decis Mak. 1991;11:95-101.
Bantis LE, Tsimikas JV, Chambers GR, Capello M, Hanash S, Feng Z. The length of the receiver operating characteristic curve and the two cutoff Youden index within a robust framework for discovery, evaluation, and cutoff estimation in biomarker studies involving improper receiver operating characteristic curves. Stat Med. 2021;40(7):1767-1789. doi:10.1002/sim.8869
Dorfman DD, Berbaum KS, Metz CE, Lenth RV, Hanley JA, Dagga HA. Proper receiver operating characteristic analysis: The bigamma model. Acad Radiol. 1997;4(2):138-149. doi:10.1016/S1076-6332(97)80013-X
Apple R, Samuels L, Fonnesbeck C, et al. Body mass index and health-related quality of life. Obes Sci Pract. 2018;4(5):417-426.
Maipas S, Nonni A, Politi E, Sarlanis H, Kavantzas NG, Kavantzas N. The goodness-of-fit of the fractal dimension as a diagnostic factor in breast cancer. Cureus. 2018;10(11):e3630. doi:10.7759/cureus.3630
Gu W, Pepe MS. Estimating the diagnostic likelihood ratio of a continuous marker. Biostatistics. 2011;12(1):87-101.
Schmitz N, Kruse J, Tress W. Application of stratum-specific likelihood ratios in mental health screening. Soc Psychiatry Psychiatr Epidemiol. 2000;35(8):375-379. doi:10.1007/s001270050253
Dujardin B, dJ EV, Van Gompel A, Unger JP, Stuyft V dP. Likelihood ratios: a real improvement for clinical decision making? Eur J Epidemiol. 1994;10(1):29-36.
Cook C. Is clinical gestalt good enough? J Manual Manipulat Therapy. 2009;17(1):6-7.
Dale AP, Marchello C, Ebell MH. Clinical gestalt to diagnose pneumonia, sinusitis, and pharyngitis: a meta-analysis. Br J Gen Pract. 2019;69(684):e444-e453.
Gardner IA, Greiner M. Receiver-operating characteristic curves and likelihood ratios: Improvements over traditional methods for the evaluation and application of veterinary clinical pathology tests. Vet Clin Pathol. 2006;35(1):8-17.
Grimes DA, Schulz KF. Refining clinical diagnosis with likelihood ratios. Lancet. 2005;365(9469):1500-1505. doi:10.1016/S0140-6736(05)66422-7
Agresti A. Categorical Data Analysis. Vol 482. Hokoben: John Wiley and Sons; 2003.
Venables W, Ripley B. Modern Applied Statistics with S. 4th ed. New York: Springer; 2002.
Bishop C. Pattern Recognition and Machine Learning. New York: Springer; 2006.
Darroch JN, Ratcliff D. Generalized iterative scaling for log-linear models. Ann Math Stat. 1972;43(5):1470-1480.
Böhning D. Multinomial logistic regression algorithm. Ann Inst Stat Math. 1992;44(1):197-200.
Hosmer DW, Lemesbow S. Goodness of fit tests for the multiple logistic regression model. Commun Stat Theory Methods. 1980;9(10):1043-1069.
Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol. 2010;5(9):1315-1316.
Liu D, Zhou XH. ROC analysis in biomarker combination with covariate adjustment. Acad Radiol. 2013;20(7):874-882.
Pepe MS. A regression modelling framework for receiver operating characteristic curves in medical diagnostic testing. Biometrika. 1997;84(3):595-608.
Dodd LE, Pepe MS. Partial AUC estimation and regression. Biometrics. 2003;59(3):614-623.
Armitage P. Tests for Linear Trends in Proportions and Frequencies. Biometrics. 1955;11(3):375-386. doi:10.2307/3001775
Casella G, Berger RL. Statistical inference. California: Cengage Learning; 2001.
Krzanowski WJ, Hand DJ. ROC curves for continuous data. Florida: Chapman and Hall/CRC; 2009.
Millstein J, Budden T, Goode E, et al. Prognostic gene expression signature for high-grade serous ovarian cancer. Ann Oncol. 2020;31(9):1240-1250.