Estimation of disease progression parameters from case-control data: application to mammographic patterns and breast cancer natural history.
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.


CONCLUSIONS
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.
Couto
E
E
MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.
Harrison
D
D
Duffy
S
S
Myles
J
J
Sala
E
E
Warren
R
R
Day
N
N
Luben
R
R
Chen
H
H
D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000453:epidemiology / Q000473:pathology*; D016022:Case-Control Studies; D015331:Cohort Studies; D016001:Confidence Intervals; D018450:Disease Progression; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data*; D008390:Markov Chains; D011336:Probability; D012680:Sensitivity and Specificity; D006113:United Kingdom / Q000453:epidemiology