Literature DB >> 35044258

A family of estimators to diagnostic accuracy when candidate tests are subject to detection limits-Application to diagnosing early stage Alzheimer disease.

Chengjie Xiong1,2, Jingqin Luo1,3,4, Folasade Agboola1,2, Elizabeth Grant1,2, John C Morris2,5,6.   

Abstract

In disease diagnosis, individuals are usually assumed to be one of the two basic types, healthy or diseased, as typically based on an established gold standard. Candidate markers for diagnosing a disease often are much cheaper and less invasive than the gold standard but must be evaluated against the gold standard for their sensitivity and specificity to accurately diagnose the disease. When candidate diagnostic markers are fully measured, receiver operating characteristic curves have been the standard approaches for assessing diagnostic accuracy. However, full measurements of diagnostic markers may not be available above or below certain limits due to various practical and technical limitations. For example, in the diagnosis of Alzheimer disease using cerebrospinal fluid biomarkers, the Roche Elecsys® immunoassays have a measuring range for multiple cerebrospinal fluid molecular concentrations. Many cognitive tests used in diagnosing dementia due to Alzheimer disease are also subject to detection limits, often referred to as the floor and ceiling effects in the neuropsychological literature. We propose a new statistical methodology for estimating the diagnostic accuracy when a diagnostic marker is subject to detection limits by dividing the entire study sample into two sub-samples by a threshold of the diagnostic marker. We then propose a family of estimators to the area under the receiver operating characteristic curve by combining a conditional nonparametric estimator and another conditional semi-parametric estimator derived from Cox's proportional hazards model. We derive the variance to the proposed estimators, and further, assess the performance of the proposed estimators as a function of possible thresholds through an extensive simulation study, and recommend the optimum thresholds. Finally, we apply the proposed methodology to assess the ability of several cerebrospinal fluid biomarkers and cognitive tests in diagnosing early stage Alzheimer disease dementia.

Entities:  

Keywords:  Alzheimer disease; Cox proportional hazards model; area under the receiver operating characteristic curve; bootstrap; confidence interval estimate; detection limits; diagnostic accuracy; maximum likelihood estimate; nonparametric estimate; receiver operating characteristic curve

Mesh:

Substances:

Year:  2022        PMID: 35044258      PMCID: PMC9018582          DOI: 10.1177/09622802211072511

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   2.494


  36 in total

Review 1.  Analysis and design issues for studies using censored biomarker measurements with an example of viral load measurements in HIV clinical trials.

Authors:  M D Hughes
Journal:  Stat Med       Date:  2000-12-15       Impact factor: 2.373

2.  Measuring and estimating diagnostic accuracy when there are three ordinal diagnostic groups.

Authors:  Chengjie Xiong; Gerald van Belle; J Philip Miller; John C Morris
Journal:  Stat Med       Date:  2006-04-15       Impact factor: 2.373

3.  Relaxing the rule of ten events per variable in logistic and Cox regression.

Authors:  Eric Vittinghoff; Charles E McCulloch
Journal:  Am J Epidemiol       Date:  2006-12-20       Impact factor: 4.897

4.  Receiver operating characteristic curve inference from a sample with a limit of detection.

Authors:  Neil J Perkins; Enrique F Schisterman; Albert Vexler
Journal:  Am J Epidemiol       Date:  2006-11-16       Impact factor: 4.897

5.  Pooling biospecimens and limits of detection: effects on ROC curve analysis.

Authors:  Sunni L Mumford; Enrique F Schisterman; Albert Vexler; Aiyi Liu
Journal:  Biostatistics       Date:  2006-03-10       Impact factor: 5.899

6.  Importance of events per independent variable in proportional hazards regression analysis. II. Accuracy and precision of regression estimates.

Authors:  P Peduzzi; J Concato; A R Feinstein; T R Holford
Journal:  J Clin Epidemiol       Date:  1995-12       Impact factor: 6.437

7.  Youden index and Associated Cut-points for Three Ordinal Diagnostic Groups.

Authors:  Jingqin Luo; Chengjie Xiong
Journal:  Commun Stat Simul Comput       Date:  2013-01       Impact factor: 1.118

8.  ROC curve inference for best linear combination of two biomarkers subject to limits of detection.

Authors:  Neil J Perkins; Enrique F Schisterman; Albert Vexler
Journal:  Biom J       Date:  2011-05       Impact factor: 2.207

9.  Smooth ROC curves and surfaces for markers subject to a limit of detection using monotone natural cubic splines.

Authors:  Leonidas E Bantis; John V Tsimikas; Stelios D Georgiou
Journal:  Biom J       Date:  2013-04-03       Impact factor: 2.207

10.  Adequate sample size for developing prediction models is not simply related to events per variable.

Authors:  Emmanuel O Ogundimu; Douglas G Altman; Gary S Collins
Journal:  J Clin Epidemiol       Date:  2016-03-08       Impact factor: 6.437

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