Joint Estimation of Diagnostic Accuracy Measures for Paired Organs – Application in Ophthalmology

Leon, Alexander R. de; Soo, Andrea; Bonzo, Daniel; Rudnisky, Christopher J.

doi:10.1002/bimj.200800123

Cited by 11 publications

(32 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…28 Percentile bootstrap 95% CIs for these estimates were constructed on the basis of 10 000 bootstrap samples. 28 All statistical analyses were performed using SAS 9.3 (SAS Inc., Cary NC).…”

Section: Methodsmentioning

confidence: 99%

Comparison Among Methods of Retinopathy Assessment (CAMRA) Study

et al. 2015

View full text Add to dashboard Cite

Purpose We compared smartphone fundus photography, nonmydriatic fundus photography, and 7-field mydriatic fundus photography for their abilities to detect and grade diabetic retinopathy (DR). Design This was a prospective, comparative study of 3 photography modalities. Participants Diabetic patients (n = 300) were recruited at the ophthalmology clinic of a tertiary diabetes care center in Chennai, India. Methods Patients underwent photography by all 3 modalities, and photographs were evaluated by 2 retina specialists. Main Outcome Measures The sensitivity and specificity in the detection of DR for both smartphone and nonmydriatic photography were determined by comparison with the standard method, 7-field mydriatic fundus photography. Results The sensitivity and specificity of smartphone fundus photography, compared with 7-field mydriatic fundus photography, for the detection of any DR were 50% (95% confidence interval [CI], 43–56) and 94% (95% CI, 92–97), respectively, and of nonmydriatic fundus photography were 81% (95% CI, 75–86) and 94% (95% CI, 92–96%), respectively. The sensitivity and specificity of smartphone fundus photography for the detection of vision-threatening DR were 59% (95% CI, 46–72) and 100% (95% CI, 99–100), respectively, and of nonmydriatic fundus photography were 54% (95% CI, 40–67) and 99% (95% CI, 98–100), respectively. Conclusions Smartphone and nonmydriatic fundus photography are each able to detect DR and sight-threatening disease. However, the nonmydriatic camera is more sensitive at detecting DR than the smartphone. At this time, the benefits of the smartphone (connectivity, portability, and reduced cost) are not offset by the lack of sufficient sensitivity for detection of DR in most clinical circumstances.

show abstract

Section: Methodsmentioning

confidence: 99%

Comparison Among Methods of Retinopathy Assessment (CAMRA) Study

et al. 2015

View full text Add to dashboard Cite

show abstract

“…Marginally, we have ( Y i j L , Y i j R ) ⊤ ∼CCM ( π j , ρ j = ρ + κ j (1− ρ )) and Y i j k ∼Bernoulli ( π j ), where π j = P ( Y i j L =1)= P ( Y i j R =1) and ρ j = c o r r ( Y i j L , Y i j R ), for j =1,2. We refer to the joint model in () as the extended CCM (ECCM), because it can be viewed as a generalization of the CCM to paired binocular binary data settings, with its paired binocular margins modelled by CCMs ; note that the joint distribution of the frailties P 1 ∼Beta ( α 1 , β 1 ) and P 2 ∼Beta ( α 2 , β 2 ) belongs to the family of Sarmanov distributions .…”

Section: Case Of Organ Exchangeabilitymentioning

confidence: 99%

“…de Leon et al. showed simple and easy non‐iterative calculation of moments estimates for ECCM and of the corresponding marginal sensitivity and specificity; standard errors (SEs) of the estimates can be obtained via either the delta method or the bootstrap. de Leon et al.…”

Section: Case Of Organ Exchangeabilitymentioning

confidence: 99%

“…de Leon et al. empirically showed that the resulting estimates and their bootstrap SEs yield generally acceptably small biases. We adopt their estimation method for ECCM in what follows and investigate the finite‐sample performance of the resulting plug‐in moments estimates

\overset{bSen}{false} = \frac{{\overset{P}{false}}_{1111} + 1.19em ({\overset{P}{false}}_{1011} + {\overset{P}{false}}_{0111} 1.19em) + 1.19em ({\overset{P}{false}}_{1010} + {\overset{P}{false}}_{0101} 1.19em) + 1.19em ({\overset{P}{false}}_{1110} + {\overset{P}{false}}_{1101} 1.19em)}{{\overset{P}{false}}_{\cdot \cdot 11} + {\overset{P}{false}}_{\cdot \cdot 10} + {\overset{P}{false}}_{\cdot \cdot 01}} and \overset{bSp}{false} = \frac{{\overset{P}{false}}_{0000}}{{\overset{P}{false}}_{\cdot \cdot 00}},

where

{\overset{P}{false}}_{ℓ_{1} r_{1} ℓ_{2} r_{2}}

is obtained by plug‐in using the moments estimates

{\overset{π}{false}}_{1}, {\overset{π}{false}}_{2}, {\overset{ρ}{false}}_{1}, {\overset{ρ}{false}}_{2}

, and

\overset{ρ}{false}

; see for details.…”

Section: Case Of Organ Exchangeabilitymentioning

confidence: 99%

See 1 more Smart Citation

Binocular sensitivity and specificity of screening tests in cross‐sectional diagnostic studies of paired organs

Perera

Ren

Punzalan

et al. 2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

We introduce new binocular accuracy measures as alternatives to conventional marginal measures that can be used to evaluate screening tests in diagnostic studies involving paired organs (e.g. eyes and ears). Specifically, we consider screening studies based on a cross-sectional design, where both diagnosis and disease status are determined after study enrolment or sampling, yielding paired binocular binary data described via two models, namely, the extended common correlation model and the Gaussian copula probit model. The first relies on the assumption of exchangeability of fellow organs, while the second is more flexible. Binocular versions of sensitivity and specificity are defined, respectively, as the probability of at least one correct positive diagnosis in patients with one or both organs truly diseased and the probability of two correct negative diagnoses for patients with both organs truly un-diseased. Comparisons between the conventional marginal and binocular sensitivities and specificities are illustrated for both models using data from a diabetic retinopathy study. We show that our methodology provides a viable alternative to conventional ways of assessing diagnostic accuracy of screening tests for paired organs. The binocular versions of sensitivity and specificity reflect the way screening tests are conducted in practice, and they overcome the shortcomings of conventional measures. Copyright © 2017 John Wiley & Sons, Ltd.

show abstract

“…We now illustrate the proposed methodology described in Section 3 on data from the diabetic retinopathy study described in Section 1. The study involved N = 94 patients with diabetes in Alberta, Canada, who were referred to a comprehensive retina practice in Edmonton.…”

Section: Application To Diabetic Retinopathy Datamentioning

confidence: 99%

Joint estimation of multiple disease‐specific sensitivities and specificities via crossed random effects models for correlated reader‐based diagnostic data: application of data cloning

Withanage

Leon

Rudnisky

2015

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

We present a model for describing correlated binocular data from reader-based diagnostic studies, where the same group of readers evaluates the presence or absence of certain diseases on binocular organs (e.g., fellow eyes) of patients. Multiple random effects are incorporated to meaningfully delineate various associations in the data including crossed random effects to account for reader-specific variability and to incorporate cross correlations. To overcome the computational complexity involved in the evaluation and maximization of the marginal likelihood, we adopt the data cloning approach, which calculates maximum likelihood estimates under the Bayesian paradigm. The bias and efficiency of the estimates are assessed in two simulation studies. We apply our model to data from a diabetic retinopathy study.

show abstract

Joint Estimation of Diagnostic Accuracy Measures for Paired Organs – Application in Ophthalmology

Cited by 11 publications

References 22 publications

Comparison Among Methods of Retinopathy Assessment (CAMRA) Study

Comparison Among Methods of Retinopathy Assessment (CAMRA) Study

Binocular sensitivity and specificity of screening tests in cross‐sectional diagnostic studies of paired organs

Joint estimation of multiple disease‐specific sensitivities and specificities via crossed random effects models for correlated reader‐based diagnostic data: application of data cloning

Contact Info

Product

Resources

About