When estimating an effect of an action with a randomized or observational study, that study is often not a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions are ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing. In both cases, the proposed model synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches were able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge.

翻译：在利用随机或观察性研究估计某项干预效应时，该研究通常并非目标人群的随机样本。然而，可将该研究估计值迁移至目标人群。现有可迁移性方法通常依赖正向性假设，即目标人群中所有相关协变量模式均需在研究样本中被观测到。严格的纳入标准（尤其随机试验场景）可能导致该假设被违反。解决正向性违反的两种常见方法是限制目标人群或限制相关协变量集合。鉴于这两种限制均非理想方案，我们提出融合统计模型与模拟模型应对正向性违反问题，并给出相应的g-计算与逆概率加权估计量。通过性传播感染检测场景的模拟实验与示例分析，对比了限制方法与融合方法处理正向性违反的效果。结果表明，当与精心筛选的模拟模型配合使用时，所提出的模型融合方法能准确回答原始研究问题，而两种限制方法均未能实现该目标。鉴于公共卫生决策常需基于不完善的目标人群信息，模型融合法为结合经验数据与最佳可用外部知识提供了可行方案。