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

在目前的文献中，当抽样总体构成系统偏向的子总体时，没有关于目标总体的因果推断的方法可用。在这种情况下，调查人员必须同时处理困扰数据的两种不同类型的偏差：第一，观察值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，协变量的分布对于样本总体的每一层都不同，导致协变量不平衡，不能在没有适当调整的情况下进行直接因果比较。虽然这些偏见中的每一种都在最近几年的文献中得到了相当大的关注，但在当前的文献中，没有一种方法可以同时解释这两种类型的偏见。这项研究计划在未来五年内的目标是开发专门为分析受这两种偏差影响的数据而量身定做的统计方法。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，我们将采用倾向分数回归(PSR)和反概率治疗加权(IPTW)方法来考虑长度偏差抽样。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价值的新概念。有偏抽样的根源来自广泛的体视学领域，有偏抽样方法论的实际应用比比皆是。应用领域包括社会科学、生物和医学、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痴呆症横断面研究，并在加拿大进行了后续调查，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。我们预计，我们将设计的方法将推动进一步开发分析当前和未来研究数据的方法。

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.