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：在利用随机研究或观察性研究评估某项干预效应时，该研究样本往往并非目标总体的随机抽样。因此，需要将研究估计值迁移至目标总体。然而，现有可迁移性方法通常依赖正性假设，即目标总体中所有相关协变量模式均需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。针对正性违反问题，常见处理方式包括限制目标总体或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相结合的综合方法。我们推导了对应的g-计算公式与逆概率加权估计量。通过性传播感染检测行为采纳案例的模拟实验与实例分析，比较了限制方法和综合方法对正性违反问题的处理效果。结果表明，在精心选择仿真模型的情况下，所提出的综合方法能准确解决原始研究问题，而两类限制方法均无法准确回应研究动机。鉴于公共卫生决策常需在目标总体信息不完备的情况下进行，基于经验数据与外部最优知识的模型综合方法是一种切实可行的策略。