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 uptake. In both cases, the proposed 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：在通过随机化或观察性研究估计干预效应时，该研究通常并非目标人群的随机样本。相反，研究中的估计值可以迁移至目标人群。然而，可迁移性方法通常依赖正性假设，即目标人群中的所有相关协变量模式在研究样本中均能被观测到。严格的入组标准（尤其是在随机试验背景下）可能导致该假设被违反。解决正性假设违反的两种常见方法是限制目标人群和限制相关协变量集。由于这两种限制均非理想方案，我们提出统计模型与仿真模型的融合方法以解决正性假设违反问题。我们提出相应的g-计算公式和逆概率加权估计量。通过性传播感染检测接受度背景下的仿真实验和实例说明，对限制方法与融合方法进行对比。在两种情景下，所提出的融合方法与精心选择的仿真模型相结合时，均能准确解决原始研究问题。而两种限制方法均无法准确回答核心问题。由于公共卫生决策常需在目标人群信息不完善的情况下做出，基于实证数据与最佳可用知识的外部信息的模型融合是一种可行的方案。