Estimation of disease progression parameters from case-control data: Application to mammographic patterns and breast cancer natural history
Journal
Journal of Epidemiology and Biostatistics
Journal Volume
6
Journal Issue
2
Pages
235-242
Date Issued
2001
Author(s)
Abstract
BACKGROUND: Estimations of mean sojourn time (MST) and sensitivity (S) in disease screening have been previously calculated from case-control data, using simple models which did not include covariates. Many studies have shown an effect of mammographic parenchymal pattern (MPP) on breast-cancer risk and tumour histology. We have expanded previous models on these to estimate MST and S with the effects of MPP as a covariate. METHODS: Data were from a nested case-control study within the East Anglian screening programme, with 875 cases and 2,601 controls. Estimates of disease progression and screening parameters were based on conditional likelihood calculation, using a Markov process model. Ninety-five per cent confidence intervals (CI) were calculated using the profile likelihood wherever possible and using a numerical estimate of the information matrix or the area under the likelihood curve where necessary. RESULTS: We obtained estimates of the incidence of preclinical disease, rate of transition from preclinical to clinical and screening sensitivity, and evaluated the association of these parameters with mammographic parenchymal pattern. A higher incidence of preclinical disease was found for high-risk MPP [relative incidence = 1.62 (95% CI: 0.89; 2.73)]. However, no difference in progression rate from preclinical to clinical disease between different MPP was found. Dense MPPs were associated with decreased sensitivity [relative sensitivity = 0.24 (95% CI: 0.06; 15)]. Wide CIs were found, probably being a consequence of the relative sparsity of interval cancer data. DISCUSSION: It is possible to estimate multiple parameters of disease progression and screening quality from case-control data. The reduction in sensitivity of the screening process associated with high-risk patterns presented here, could be of paramount interest for proposing new screening strategies, such as possible additional screening tools.
SDGs
Type
journal article