论文标题
与精确医学和差异网络分析应用的定性互动的统计推断
Statistical Inference for Qualitative Interactions with Applications to Precision Medicine and Differential Network Analysis
论文作者
论文摘要
当治疗效果或缔合的量度因子群体的符号而变化时，就会发生定性相互作用。在许多生物医学环境中特别感兴趣的是缺席/存在定性相互作用，这些相互作用发生在一个亚种群中，但在另一个子群中存在效果时发生。缺乏/存在相互作用是在精确医学中的新兴应用中出现的，其目的是确定一组预测性生物标志物，这些标志物在某些亚种群中对临床结果具有预后价值，而不是其他人群。它们也自然出现在基因调节网络推断中，目的是确定与患病和健康个体相对应的网络的差异，或者与疾病的不同亚型相对应；这种差异导致鉴定基于网络的生物标志物的疾病。在本文中，我们认为，尽管缺乏/存在假设很重要，但为此假设开发统计检验是一个棘手的问题。为了克服这一挑战，我们在新的推理框架中近似问题。特别是，我们建议通过量化效应大小的相对差异来推断缺乏/存在相互作用，并推论当相对差异很大时，就会发生不存在/存在相互作用。通过仿真研究以及对癌症基因组地图集的乳腺癌数据的分析来说明所提出的方法。
Qualitative interactions occur when a treatment effect or measure of association varies in sign by sub-population. Of particular interest in many biomedical settings are absence/presence qualitative interactions, which occur when an effect is present in one sub-population but absent in another. Absence/presence interactions arise in emerging applications in precision medicine, where the objective is to identify a set of predictive biomarkers that have prognostic value for clinical outcomes in some sub-population but not others. They also arise naturally in gene regulatory network inference, where the goal is to identify differences in networks corresponding to diseased and healthy individuals, or to different subtypes of disease; such differences lead to identification of network-based biomarkers for diseases. In this paper, we argue that while the absence/presence hypothesis is important, developing a statistical test for this hypothesis is an intractable problem. To overcome this challenge, we approximate the problem in a novel inference framework. In particular, we propose to make inferences about absence/presence interactions by quantifying the relative difference in effect size, reasoning that when the relative difference is large, an absence/presence interaction occurs. The proposed methodology is illustrated through a simulation study as well as an analysis of breast cancer data from the Cancer Genome Atlas.
