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：在通过随机研究或观察性研究评估某个行动的效果时，该研究通常并非目标人群的随机样本。因此，需要将研究估计值迁移至目标人群。然而，可迁移性方法通常依赖正性假设，即目标人群中所有相关协变量模式均能在研究样本中观测到。严格的入组标准（尤其在随机试验中）可能导致该假设被违反。处理正性违反问题的两种常见方法是限制目标人群和限制相关协变量集合。鉴于这两种限制方法均非理想选择，我们提出统计模型与仿真模型的综合方法来解决正性违反问题。我们提出了相应的g计算和逆概率加权估计量。通过性传播感染检测接受率的仿真实验和示例分析，对比了限制方法与综合方法在处理正性违反问题上的效果。在两种场景中，当所提出的综合方法与精心选择的仿真模型配对使用时，均能准确回答原始研究问题。而两种限制方法均无法准确回应研究动机问题。鉴于公共卫生决策通常需在目标人群信息不完善的情况下进行，模型综合方法是一种可行方案——它结合了经验数据与基于最佳可用知识的外部信息。