Double Bias: Group comparison and variable selection under length-biased sampling and covariate imbalance.

双偏差：长度偏差采样和协变量不平衡下的组比较和变量选择。

• 批准号: 217398-2013
• 负责人: Asgharian, Masoud
• 金额: $ 2.11万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=546596
• 关键词: Double; Bias; Group; comparison; variable

In the current literature, methodologies for drawing causal inference about a target population when the sampling population constitutes a systematically biased subpopulation are not available. In such situations, investigators must simultaneously tackle two distinct types of bias that plague the data: first, observations do not form a representative sample from the target population, and second, owing to observational nature of these data, the distribution of covariates differs for each stratum of the sampling population, leading to covariate imbalance precluding direct causal comparisons without appropriate adjustment. While each of these biases has received substantial attention in the literature in recent years, there is no methodology in the current literature that can account for both types of bias simultaneously. The aim of this research program within the next five years is to develop statistical methodologies specifically tailored for analysing data subject to these two types of biases. In particular, we will develop semiparametric methods to address this problem. Amongst other approaches, we will adapt Propensity Score Regression (PSR) and Inverse Probability of Treatment Weighting (IPTW) methods to account for length-bias sampling. Subsequently, we will develop techniques for variable selection under double bias. Feature selection under covariate imbalance is, in and of itself, a novel notion with important applications. The roots of biased sampling stem from the broad field of stereology, and practical applications of methodologies for biased sampling abound. Areas of application include, amongst others, the social sciences, biology and medical sciences, labor force studies in economics, quality control in the manufacturing industry, and physics. Two commonly cited examples of such are the Canadian Study of Health and Aging (CSHA), a nationwide cross-sectional study of dementia with follow-up conducted in Canada, and the Labor Force Survey of the Spanish Institute for Statistics. Novel applications to nanotechnology have also been discussed recently. We expect the methodology we will devise will spur the development of further methodologies for analyzing data from current and future studies.

在目前的文献中，当抽样人口构成一个有系统偏见的亚人口时，没有关于目标人口的因果推断的方法。在这种情况下，研究人员必须同时解决困扰数据的两种不同类型的偏差：第一，观察结果不构成目标人群的代表性样本，第二，由于这些数据的观察性质，协变量的分布对于抽样人群的每个阶层都不同，导致协变量不平衡，如果没有适当的调整，就无法进行直接的因果比较。虽然近年来这些偏差中的每一种都在文献中得到了大量的关注，但目前的文献中没有方法可以同时解释这两种类型的偏差。该研究计划的目标是在未来五年内开发专门用于分析受这两种类型偏见影响的数据的统计方法。特别是，我们将开发半参数方法来解决这个问题。在其他方法中，我们将采用倾向评分回归（PSR）和治疗加权逆概率（IPTW）方法来解释长度偏倚抽样。随后，我们将开发双偏置下的变量选择技术。协变量不平衡下的特征选择本身就是一个具有重要应用的新概念。有偏抽样的根源来自于体视学的广泛领域，有偏抽样方法的实际应用比比皆是。应用领域包括，除其他外，社会科学，生物学和医学科学，经济学中的劳动力研究，制造业的质量控制和物理学。这方面的两个经常引用的例子是加拿大健康与老龄化研究（CSHA），这是一项在加拿大进行的全国性痴呆症横断面研究，以及西班牙统计研究所的劳动力调查。最近也讨论了纳米技术的新应用。我们预计，我们将设计的方法将刺激进一步的方法学的发展，用于分析当前和未来的研究数据。