Estimation of Operating Characteristics for Dependent Diagnostic Tests Based on Latent Markov Models

We describe a method for making inferences about the joint operating characteristics of multiple diagnostic tests applied longitudinally and in the absence of a definitive reference test. Log‐linear models are adopted for the classification distributions conditional on the latent state, where inclus...

Full description

Saved in:
Bibliographic Details
Published in:Biometrics 2000-12, Vol.56 (4), p.1109-1117
Main Authors: Cook, Richard J, Ng, Edmund T. M, Meade, Maureen O
Format: Article
Language:English
Subjects:
Adult respiratory distress syndrome
Analytical estimating
Biometrics
biometry
Biometry - methods
diagnostic techniques
Diagnostic Techniques and Procedures
Disease models
Humans
Latent Markov process
linear models
Log-linear model
Longitudinal data
Marginal likelihood
Markov chain
Markov Chains
Markov models
Markov processes
Medical diagnostic tests
Missing data
Modeling
Models, Statistical
Observer Variation
Parametric models
patients
Probability
Reliability
Reproducibility of Results
Respiratory Distress Syndrome, Adult - classification
Respiratory Distress Syndrome, Adult - diagnosis
Sensitivity
Sensitivity and Specificity
Specificity
Time Factors
Transition probabilities
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We describe a method for making inferences about the joint operating characteristics of multiple diagnostic tests applied longitudinally and in the absence of a definitive reference test. Log‐linear models are adopted for the classification distributions conditional on the latent state, where inclusion of appropriate interaction terms accommodates conditional dependencies among the tests. A marginal likelihood is constructed by marginalizing over a latent two‐state Markov process. Specific latent processes we consider include a first‐order Markov model, a second‐order Markov model, and a time‐nonhomogeneous Markov model, although the method is described in full generality. Adaptations to handle missing data are described. Model diagnostics are considered based on the bootstrap distribution of conditional residuals. The methods are illustrated by application to a study of diffuse bilateral infiltrates among patients in intensive care wards in which the objective was to assess aspects of validity and clinical agreement.
ISSN:0006-341X
1541-0420
DOI:10.1111/j.0006-341X.2000.01109.x