论文标题
最小化患病率估计的不确定性
Minimizing Uncertainty in Prevalence Estimates
论文作者
论文摘要
估计患病率是具有一定医学状况的人群的比例，是流行病学的基础。传统方法依赖于从人群中随机进行的测试样本的分类。这种估计患病率的方法有偏见，并且不确定。在这里，我们为患病率构建了一个新的，公正的，最小的差异估计器。最近的结果表明，可以通过计算参数来估计，该参数将测量空间的任意子集中的样品分数与诊断测试的条件概率模型所期望的分数进行了比较。该估计器的方差取决于子集的选择和落入其中的样品的分数。我们采用浴缸原理来最大程度地重施加差异作为一维优化问题。使用对称属性，我们表明所产生的目标函数是良好的，并且可以在数值上最小化。
Estimating prevalence, the fraction of a population with a certain medical condition, is fundamental to epidemiology. Traditional methods rely on classification of test samples taken at random from a population. Such approaches to estimating prevalence are biased and have uncontrolled uncertainty. Here, we construct a new, unbiased, minimum variance estimator for prevalence. Recent result show that prevalence can be estimated from counting arguments that compare the fraction of samples in an arbitrary subset of the measurement space to what is expected from conditional probability models of the diagnostic test. The variance of this estimator depends on both the choice of subset and the fraction of samples falling in it. We employ a bathtub principle to recast variance minimization as a one-dimensional optimization problem. Using symmetry properties, we show that the resulting objective function is well-behaved and can be numerically minimized.
