MODEL-BASED SCREENING BY RISK WITH APPLICATION TO DOWNS-SYNDROME
MODEL-BASED SCREENING BY RISK WITH APPLICATION TO DOWNS-SYNDROME
复制标题
DOI：
10.1002/sim.4780110211
复制
发表时间：
1992-01-30
影响因子：
2
通讯作者：
THOMPSON, SG
中科院分区：
文献类型：
--
作者：
ROYSTON, P;THOMPSON, SG
可以通过给定当疾病存在时其分布发生变化的变量值来评估个体受影响的风险来进行疾病筛查。通过识别那些风险大于某个临界值的个体来获得最佳筛查策略。本文总结了从受疾病影响的似然比得出风险的方式，并比较了估计似然比的三种不同方法，即直接估计、逻辑回归和分布建模。对于具有多元正态分布的连续变量，按风险筛选相当于使用二次判别。本文展示了如何针对使用指定风险截止值的筛查策略得出风险以及相关检测和误报率的估计。按风险进行筛查具有反直觉的特性，即随着受影响个体和未受影响个体之间筛查变量分布的分离程度的增加，检测率和假阳性率可能都会增加。该方法是利用唐氏综合症产前筛查数据进行探索的。选择的方法是基于模型的；对模型进行描述并测试拟合优度。在进行适当的风险评估之前，必须克服异常值和非正态性引起的并发症。收缩的概念用于估计新数据集中可能预期的检测率和误报率。
Screening for a disorder may be carried out by assessing the risk that an individual is affected given the values of variables whose distributions alter when the disorder is present. An optimal screening policy is obtained by identifying those individuals whose risk is greater than some cut-off value. This paper summarizes the way in which risk is derived from the likelihood ratio of being affected by the disorder, and compares three different methods of estimating the likelihood ratio, namely direct estimation, logistic regression and distribution modelling. For continuous variables that have a multivariate normal distribution, screening by risk is equivalent to the use of quadratic discrimination. The paper shows how estimates of the risk and associated detection and false positive rates can be derived for a screening policy which uses specified risk cut-offs. Screening by risk has the counter-intuitive property that as the separation in the distribution of screening variables between affected and unaffected individuals increases, the detection and false positive rates may both increase. The approach is explored using data on antenatal screening for Down's syndrome. The method of choice is model-based; the model is described and tested for goodness of fit. Complications arising from outliers and non-normality must be overcome before an appropriate assessment of risk can be made. The concept of shrinkage is used to estimate the detection and false positive rates that may be expected in a new data set.