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

E. Couto, D. Harrison, S. Duffy, J. Myles, E. Sala, R. Warren, N. Day, R. Luben, H. Chen
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引用次数: 7

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.
从病例对照数据估计疾病进展参数:应用于乳房x线摄影模式和乳腺癌自然史。
背景:疾病筛查中的平均停留时间(MST)和敏感性(S)的目标以前是使用不包括协变量的简单模型从病例对照数据中计算出来的。许多研究表明乳房x线摄影实质模式(MPP)对乳腺癌风险和肿瘤组织学的影响。我们已经扩展了之前的模型,以MPP作为协变量的影响来估计MST和S。方法数据来自东安格利亚筛查项目的巢式病例对照研究,共有875例病例和2,601例对照。疾病进展和筛选参数的估计基于条件似然计算,使用马尔可夫过程模型。95%置信区间(CI)的计算尽可能使用轮廓似然,必要时使用信息矩阵的数值估计或似然曲线下的面积。结果我们获得了临床前病变发生率、临床前向临床转移率和筛查敏感性的估计值,并评估了这些参数与乳腺实质形态的相关性。高危MPP的临床前疾病发生率较高[相对发病率= 1.62 (95% CI: 0.89;2.73)]。然而,不同MPP之间从临床前到临床疾病的进展率没有差异。密集mpp与敏感性降低相关[相对敏感性= 0.24 (95% CI: 0.06;15)]。发现了宽ci,可能是间隔期癌症数据相对稀疏的结果。从病例对照数据中估计疾病进展和筛查质量的多个参数是可能的。本文提出的与高风险模式相关的筛查过程的敏感性降低,可能是提出新的筛查策略(如可能的额外筛查工具)的最重要兴趣。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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