When Sensitivity is a Function of Age and Time Spent in the Preclinical State in Periodic Cancer Screening

Wu, Dongfeng; Cariño, Ricolindo L.; Wu, Xinxin

doi:10.22237/jmasm/1209615840

Cited by 5 publications

(5 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In practice, it is likely that sensitivity increases as the tumor grows following preclinical onset. 26,27 In this case, the true sensitivity is a curve rather than a single value, and the true sensitivity under prospective screening can be thought of as a marginal expectation of the true, time-varying sensitivity. While this setting is more realistic, it makes visualizing the relationship between empirical sensitivity and true sensitivity more complex.…”

Section: Discussionmentioning

confidence: 99%

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure

Lange

Zhao

Göğebakan

et al. 2023

Stat Methods Med Res

View full text Add to dashboard Cite

The true sensitivity of a cancer screening test, defined as the frequency with which the test returns a positive result if the cancer is present, is a key indicator of diagnostic performance. Given the challenges of directly assessing test sensitivity in a prospective screening program, proxy measures for true sensitivity are frequently reported. We call one such proxy empirical sensitivity, as it is given by the observed ratio of screen-detected cancers to the sum of screen-detected and interval cancers. In the setting of the canonical three-state Markov model for progression from preclinical onset to clinical diagnosis, we formulate a mathematical relationship for how empirical sensitivity varies with the screening interval and the mean preclinical sojourn time and identify conditions under which empirical sensitivity exceeds or falls short of true sensitivity. In particular, when the inter-screening interval is short relative to the mean sojourn time, empirical sensitivity tends to exceed true sensitivity, unless true sensitivity is high. The Breast Cancer Surveillance Consortium (BCSC) has reported an estimate of 0.87 for the empirical sensitivity of digital mammography. We show that this corresponds to a true sensitivity of 0.82 under a mean sojourn time of 3.6 years estimated based on breast cancer screening trials. However, the BCSC estimate of empirical sensitivity corresponds to even lower true sensitivity under more contemporary, longer estimates of mean sojourn time. Consistently applied nomenclature that distinguishes empirical sensitivity from true sensitivity is needed to ensure that published estimates of sensitivity from prospective screening studies are properly interpreted.

show abstract

Section: Discussionmentioning

confidence: 99%

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure

Lange

Zhao

Göğebakan

et al. 2023

Stat Methods Med Res

View full text Add to dashboard Cite

show abstract

“…However, sensitivity of mammogram increases as a woman's age increases, while screening sensitivity of chest X-ray for lung cancer does not depend on age. In later developments, sensitivity was modeled as a function of the time spent in the preclinical state along with age at diagnosis (Wu et al, 2008), but sensitivity was influenced by the proportion of time in the preclinical state to the sojourn time more than by age for breast cancer screening. For this reason, Kim and Wu (2014) recently modeled sensitivity as a function of the sojourn time and time spent in the preclinical state and then applied the model to the Johns Hopkins Lung Project data.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Nonlinear Mixed-Effects Disease Progression Model

Kim

Jang

et al. 2015

J Biom Biostat

View full text Add to dashboard Cite

A nonlinear mixed-effects approach is developed for disease progression models that incorporate variation in age in a Bayesian framework. We further generalize the probability model for sensitivity to depend on age at diagnosis, time spent in the preclinical state and sojourn time. The developed models are then applied to the Johns Hopkins Lung Project data and the Health Insurance Plan for Greater New York data using Bayesian Markov chain Monte Carlo and are compared with the estimation method that does not consider random-effects from age. Using the developed models, we obtain not only age-specific individual-level distributions, but also population-level distributions of sensitivity, sojourn time and transition probability.

show abstract

“…Wu et al 4 recently proposed a method to model the sensitivity as a function of age at diagnosis and the time in the preclinical state. In this model, the ratio of the time spent in the preclinical state to the sojourn time was multiplied by a logistic function of age.…”

Section: Introductionmentioning

confidence: 99%

Estimation of sensitivity depending on sojourn time and time spent in preclinical state

Kim

2012

Stat Methods Med Res

Self Cite

View full text Add to dashboard Cite

The probability model for periodic screening was extended to provide statistical inference for sensitivity depending on sojourn time, in which the sensitivity was modeled as a function of time spent in the preclinical state and the sojourn time. The likelihood function with the proposed sensitivity model was then evaluated with simulated data to check its reliability in terms of the mean estimation and the standard error. Simulation results showed that the maximum likelihood estimates of the proposed model have little bias and small standard errors. The extended probability model was further applied to the Johns Hopkins Lung Project data using both maximum likelihood estimation and Bayesian Markov chain Monte Carlo.

show abstract

When Sensitivity is a Function of Age and Time Spent in the Preclinical State in Periodic Cancer Screening

Cited by 5 publications

References 12 publications

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure

Test sensitivity in a prospective cancer screening program: A critique of a common proxy measure

A Bayesian Nonlinear Mixed-Effects Disease Progression Model

Estimation of sensitivity depending on sojourn time and time spent in preclinical state

Contact Info

Product

Resources

About