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：在通过随机或观察研究估计某项行动的效果时,该研究往往不是对预期目标人口的随机抽样,相反,该研究的估计数可以传送到目标人口;然而,可迁移方法一般依赖一种假定假设,即研究抽样中也观察到目标人口的所有相关共变模式;严格的资格标准,特别是在随机试验中,可能导致违反这一假设;处理推定违反现象的两种共同办法限制目标人口,限制相关的共变组合;由于这些限制都不是理想的,因此我们建议综合统计和模拟模型,以解决推定违反现象;我们建议相应的加权和反概率加权比率;处理推定违反现象的限制和综合方法与模拟试验中的模拟试验和说明性例子形成对比;在这两种情况下,拟议的模型综合方法在与经过深思熟虑的模拟模型结合时,准确处理了最初的研究问题;由于这些限制方法都不理想,因此我们建议综合统计和模拟模型,以解决激励问题;由于公共卫生决定往往必须采用不完善的衡量指标的方法,并且根据不完善的外部数据进行最佳综合。</s>