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Open AccessJournal ArticleDOI

Estimating Sensitivity and Sojourn Time in Screening for Colorectal Cancer A Comparison of Statistical Approaches

TLDR
Various analytic strategies for fitting exponential models to data from a screening program for colorectal cancer conducted in Calvados, France, between 1991 and 1994 are considered, yielding estimates of mean sojourn time and sensitivity.
Abstract
The effectiveness of cancer screening depends crucially on two elements: the sojourn time (that is, the duration of the preclinical screen-detectable period) and the sensitivity of the screening test. Previous literature on methods of estimating mean sojourn time and sensitivity has largely concentrated on breast cancer screening. Screening for colorectal cancer has been shown to be effective in randomized trials, but there is little literature on the estimation of sojourn time and sensitivity. It would be interesting to demonstrate whether methods commonly used in breast cancer screening could be used in colorectal cancer screening. In this paper, the authors consider various analytic strategies for fitting exponential models to data from a screening program for colorectal cancer conducted in Calvados, France, between 1991 and 1994. The models yielded estimates of mean sojourn time of approximately 2 years for 45- to 54-year-olds, 3 years for 55- to 64-year-olds, and 6 years for 65- to 74-year-olds. Estimates of sensitivity were approximately 75%, 50%, and 40% for persons aged 45-54, 55-64, and 65-74 years, respectively. There is room for improvement in all models in terms of goodness of fit, particularly for the first year after screening, but results from randomized trials indicate that the sensitivity estimates are roughly correct. Am J Epidemiol 1998;148:609-19.

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Journal ArticleDOI

Cánceres de intervalo y sensibilidad de los programas poblacionales de cribado de cáncer colorrectal

TL;DR: Se ha encontrado una mayor proporcion of mujeres a las que se diagnostico un cancer de intervalo, and que estas lesiones estaban mayoritariamente localizadas in el colon proximal.
Dissertation

Modelling breast cancer incidence, progression and screening test sensitivity using screening data

TL;DR: Mammography screening aims to reduce the number of breast cancer deaths, thought earlier diagnosis/treatment, and tumour development is often observed indirectly thought variations in breast cancer incidence caused by screening.
Journal ArticleDOI

Estimation of progression of multi-state chronic disease using the Markov model and prevalence pool concept

TL;DR: This article [1] has been retracted because the Editors are unable to ensure the scientific veracity of the findings or the ethical conduct of the authors despite an extensive investigation.
Journal ArticleDOI

Statistical models of tumour onset and growth for modern breast cancer screening cohorts.

TL;DR: A comprehensive continuous random effects model for the natural history of breast cancer, which models the unobservable processes of tumour onset, tumour growth, screening sensitivity, and symptomatic detection is presented.
Reference EntryDOI

Screening, Sojourn Time†

TL;DR: Sojourn time is the duration of a disease before clinical symptoms become apparent but during which it is detectable by a screening test, and common models for its distribution include the exponential and Weibull distributions.
References
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Journal ArticleDOI

Inference from Iterative Simulation Using Multiple Sequences

TL;DR: The focus is on applied inference for Bayesian posterior distributions in real problems, which often tend toward normal- ity after transformations and marginalization, and the results are derived as normal-theory approximations to exact Bayesian inference, conditional on the observed simulations.
Journal ArticleDOI

Sampling-Based Approaches to Calculating Marginal Densities

TL;DR: In this paper, three sampling-based approaches, namely stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm, are compared and contrasted in relation to various joint probability structures frequently encountered in applications.
Journal Article

Sampling-based approaches to calculating marginal densities

TL;DR: Stochastic substitution, the Gibbs sampler, and the sampling-importance-resampling algorithm can be viewed as three alternative sampling- (or Monte Carlo-) based approaches to the calculation of numerical estimates of marginal probability distributions.
Book

The Mathematica Book

TL;DR: Mathematica has defined the state of the art in technical computing for over a decade, and has become a standard in many of the world's leading companies and universities as discussed by the authors.

The Mathematica book

TL;DR: From the Publisher: Mathematica has defined the state of the art in technical computing for over a decade, and has become a standard in many of the world's leading companies and universities.
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