论文标题
外推时具有有界权重的复合可能性
Composite Likelihoods with Bounded Weights in Extrapolation of Data
论文作者
论文摘要
在促进及时获得安全有效的儿童药物的许多努力中，人们对外推的关注越来越多。宽松地，当可以假定疾病的过程和对药物的预期反应在儿科和参考人群中足够相似时，是对成年人或年龄段的结论或可用数据的利用。然后，外推可以将其表征为从参考（成人或年龄段）到目标小儿种群的信息的统计映射。信息的翻译或宽松的信息映射可以是通过复合可能性方法的方法，在这种方法中，参考人群的可能性是通过凸起加权的，并且该指数与目标人群中映射的信息的价值有关。体重在上方和下方都限制了一个事实，即尽管队列之间的反应差异很大，但（疾病和预期反应的相似性（疾病和预期反应）仍然有效。然后使用最大似然方法来估计参数，渐近理论用于得出用于推理中使用的估计值的分布。因此，目标人群的影响估计从参考人群借用信息。此外，该手稿还讨论了该方法与贝叶斯统计范式的关系。
Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm.
