Research intended to estimate the effect of an action, like in randomized trials, often do not have random samples of the intended target population. Instead, estimates can be transported to the desired target population. Methods for transporting between populations are often premised on a positivity assumption, such that all relevant covariate patterns in one population are also present in the other. However, eligibility criteria, particularly in the case of trials, can result in violations of positivity. To address nonpositivity, a synthesis of statistical and mechanistic models was previously proposed in the context of violations by a single binary covariate. Here, we extend the synthesis approach for positivity violations with a continuous covariate. For estimation, two novel augmented inverse probability weighting estimators are proposed, with one based on estimating the parameters of a marginal structural model and the other based on estimating the conditional average causal effect. Both estimators are compared to other common approaches to address nonpositivity via a simulation study. Finally, the competing approaches are illustrated with an example in the context of two-drug versus one-drug antiretroviral therapy on CD4 T cell counts among women with HIV.

翻译：旨在估计某种行动效应（如随机试验）的研究通常无法获得目标总体的随机样本。此时，可将估计结果迁移至所需的目标总体。跨总体迁移方法通常基于正性假设，即一个总体中的所有相关协变量模式也存在于另一个总体中。然而，在试验情境下，纳入标准可能导致正性违反。为应对非正性问题，此前已有研究针对单一二元协变量违反的情形提出统计模型与机理模型的合成方法。本文将合成方法扩展至连续协变量下的正性违反。在估计方面，我们提出了两种新型增强逆概率加权估计量：一种基于边际结构模型的参数估计，另一种基于条件平均因果效应的估计。通过模拟研究，将这两种估计量与处理非正性的其他常用方法进行了比较。最后，以HIV女性患者接受两种药物联合抗逆转录病毒治疗与单药治疗对CD4 T细胞计数影响的实例，展示了各竞争性方法的应用。