×

zbMATH — the first resource for mathematics

Estimation of diagnostic accuracy of a combination of continuous biomarkers allowing for conditional dependence between the biomarkers and the imperfect reference-test. (English) Zbl 1372.62053
Summary: Estimating biomarker-index accuracy when only imperfect reference-test information is available is usually performed under the assumption of conditional independence between the biomarker and imperfect reference-test values. We propose to define a latent normally-distributed tolerance-variable underlying the observed dichotomous imperfect reference-test results. Subsequently, we construct a Bayesian latent-class model based on the joint multivariate normal distribution of the latent tolerance and biomarker values, conditional on latent true disease status, which allows accounting for conditional dependence. The accuracy of the continuous biomarker-index is quantified by the AUC of the optimal linear biomarker-combination. Model performance is evaluated by using a simulation study and two sets of data of Alzheimer’s disease patients (one from the memory-clinic-based Amsterdam Dementia Cohort and one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database). Simulation results indicate adequate model performance and bias in estimates of the diagnostic-accuracy measures when the assumption of conditional independence is used when, in fact, it is incorrect. In the considered case studies, conditional dependence between some of the biomarkers and the imperfect reference-test is detected. However, making the conditional independence assumption does not lead to any marked differences in the estimates of diagnostic accuracy.
MSC:
62P10 Applications of statistics to biology and medical sciences; meta analysis
62J20 Diagnostics, and linear inference and regression
62F15 Bayesian inference
Software:
R2WinBUGS
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Albert, A cautionary note on the robustness of latent class models for estimating diagnostic error without a gold standard, Biometrics 60 pp 427– (2004) · Zbl 1274.62486 · doi:10.1111/j.0006-341X.2004.00187.x
[2] Baker, How to interpret a small increase in AUC with an additional risk prediction marker: Decision analysis comes through, Statistics in Medicine 22 pp 3946– (2014) · doi:10.1002/sim.6195
[3] Beach, Accuracy of the clinical diagnosis of Alzheimer diseases at the national institute on aging Alzheimer disease centers, Journal of Neuropathology & Experimental Neurology 71 pp 566– (2012) · doi:10.1097/NEN.0b013e31824b211b
[4] Biomarkers Definitions Working Group, Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework, Clinical Pharmacology & Therapeutics 69 pp 89– (2001) · doi:10.1067/mcp.2001.113989
[5] Dendukuri, Bayesian approaches to modeling the conditional dependence between multiple diagnostic tests, Biometrics 57 pp 158– (2001) · Zbl 1209.62275 · doi:10.1111/j.0006-341X.2001.00158.x
[6] Garret, Latent class model diagnosis, Biometrics 56 pp 1055– (2000) · Zbl 1116.62428 · doi:10.1111/j.0006-341X.2000.01055.x
[7] Gelman, Inference from iterative simulation using multiple sequences, Statistical Science 7 pp 457– (1992) · Zbl 1386.65060 · doi:10.1214/ss/1177011136
[8] Geweke , J. 1992 169 193
[9] Huang, Optimal combinations of diagnostic tests based on AUC, Biometrics 67 pp 568– (2011) · Zbl 1217.62169 · doi:10.1111/j.1541-0420.2010.01450.x
[10] Jones, Identifiability of models for multiple diagnostic testing in the absence of a gold standard, Biometrics 66 pp 855– (2010) · Zbl 1203.62194 · doi:10.1111/j.1541-0420.2009.01330.x
[11] Lu, A Bayesian approach to simultaneously adjusting for verification and reference standard bias in diagnostic test studies, Statistics in Medicine 29 pp 2532– (2010) · doi:10.1002/sim.4018
[12] Lunn, The BUGS project: Evolution, critique, and future directions, Statistics in Medicine 28 pp 3049– (2009) · doi:10.1002/sim.3680
[13] McKhann, The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease, Alzheimer’s & Dementia 7 pp 263– (2011) · doi:10.1016/j.jalz.2011.03.005
[14] McLachlen , G. Peel , D. 2004
[15] O’Malley, Bayesian regression methodology for estimating a receiver operating characteristic curve with two radiologic applications: Prostate biopsy and spiral ct of ureteral stones, Academic Radiology 8 pp 731– (2001) · doi:10.1016/S1076-6332(03)80578-0
[16] O’Malley, Bayesian multivariate hierarchical transformation models for ROC analysis, Statistics in Medicine 25 pp 459– (2006) · doi:10.1002/sim.2187
[17] Pepe, Insights into latent class analysis of diagnostic test performance, Biostatistics 8 pp 474– (2007) · Zbl 1144.62100 · doi:10.1093/biostatistics/kxl038
[18] R Core Team 2013 http://www.R-project.org/
[19] Renard, Validation of surrogate endpoints in multiple randomized clinical trials with discrete outcomes, Biometrical journal 44 pp 921– (2002) · Zbl 04574051 · doi:10.1002/bimj.200290004
[20] Rindskopf, The value of latent class analysis in medical diagnosis, Statistics in Medicine 5 pp 21– (1986) · doi:10.1002/sim.4780050105
[21] Robert, Estimation of a normal mixture model through Gibbs sampling and prior feedback, Test 2 pp 125– (1993) · Zbl 0811.62037 · doi:10.1007/BF02562672
[22] Scheltens, How golden is the gold standard of neuropathology in dementia, Alzheimer’s & Dementia 7 pp 486– (2011) · doi:10.1016/j.jalz.2011.04.011
[23] Sturtz, R2WinBUGS: A Packages for Running WinBUGS from R, Journal of Statistical Software 12 pp 1– (2005) · doi:10.18637/jss.v012.i03
[24] Su, Linear combinations of multiple diagnostic markers, Journal of the American Statistical Association 88 pp 1350– (1993) · Zbl 0792.62099 · doi:10.1080/01621459.1993.10476417
[25] Wang, Estimating receiver operating characteristic curves with covariates when there is no perfect reference test for diagnosis of Johne’s Disease, Journal of Diary Science 89 pp 3038– (2006) · doi:10.3168/jds.S0022-0302(06)72577-2
[26] Welge, Combined CSF, tau, p-tau181 and amyloid’\(\beta\) 38/40/42 for diagnosing Alzheimer’s disease, Journal of Neural Transmission 116 pp 203– (2009) · doi:10.1007/s00702-008-0177-6
[27] Wei, Bayesian multivariate meta-analysis with multiple outcomes, Statistics in Medicine 32 pp 2911– (2013) · doi:10.1002/sim.5745
[28] Wollman, Sensitivity and specificity of neuroimaging for the diagnosis of Alzheimer’s disease, Dialogues in Clinical Neuroscience 5 pp 89– (2003)
[29] Yu, Combining multiple continuous test for the diagnosis of kidney impairment in the absence of a gold standard, Statistics in Medicine 30 pp 1712– (2011) · doi:10.1002/sim.4203
[30] Zou , K. H. Liu , A. Bandos , A. I. Ohno-Machado , L. Rockette , H. E. 2012
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.