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

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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

Comparison of Observed and Expected Numbers of Detected Cancers in the Research Center for Cancer Prevention and Screening Program

TL;DR: Although cancer screening programs in the present study increased the detection of potentially curable cancers, these modalities, particularly lung, breast and prostate screening, might detect cancers which would not necessarily be clinically significant.
Journal ArticleDOI

Modelling the overdiagnosis of breast cancer due to mammography screening in women aged 40 to 49 in the United Kingdom.

TL;DR: Although a high proportion of screen-detected in situ cancers weren't on-progressive, a majority of these would have presented clinically in theabsence of screening, results suggest annual screening is most suitable for women aged 40 to 49 in the United Kingdom due to short cancer sojourn times.
Journal ArticleDOI

Parameter estimates for invasive breast cancer progression in the Canadian National Breast Screening Study

TL;DR: Although younger women have a slower transition rate from healthy state to preclinical, their screen-detected tumour becomes evident sooner and women aged 50–59 have a higher mortality rate compared with younger women.
Journal ArticleDOI

Surrogate endpoints for cancer screening trials: general principles and an illustration using the UK Flexible Sigmoidoscopy Screening Trial

TL;DR: It is argued that a measure which weights incident cancers according to their predicted mortality has many advantages over other measures and should be used more routinely, and application to the UK Flexible Sigmoidoscopy Screening Trial data suggests that predicted colorectal cancer mortality is a more powerful endpoint than actual mortality and could advance the analysis time by about three years.
Journal ArticleDOI

Critical Analysis of Markov Models Used for the Economic Evaluation of Colorectal Cancer Screening: A Systematic Review.

TL;DR: Structural uncertainty analysis could be a useful strategy for understanding the impact of the assumptions of different models on cost-effectiveness results.
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.
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The Mathematica Book

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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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