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

在目前的文献中，当抽样人口构成有系统偏差的亚人口时，对目标人口进行因果推断的方法是不可用的。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：首先，观察结果不能从目标人群中形成具有代表性的样本；其次，由于这些数据的观察性质，抽样人群的每个阶层的协变量分布不同，导致协变量失衡，无法在没有适当调整的情况下进行直接因果比较。虽然这些偏差近年来在文献中得到了大量关注，但目前文献中没有一种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偏差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，我们将采用倾向得分回归（PSR）和处理加权逆概率（IPTW）方法来解释长度偏差抽样。随后，我们将开发双偏差下的变量选择技术。协变量不平衡下的特征选择是一个具有重要应用价值的新概念。偏抽样的根源源于广泛的立体学领域，偏抽样方法的实际应用比比皆是。除其他外，应用领域包括社会科学、生物学和医学、经济学中的劳动力研究、制造业的质量控制和物理学。两个经常被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的痴呆横断面研究，并进行了随访，以及西班牙统计研究所的劳动力调查。最近还讨论了纳米技术的新应用。我们期望我们将设计的方法将促进进一步分析当前和未来研究数据的方法的发展。

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.