In pediatric and orphan disease indications, the conduct of a fully powered randomized control trials (RCTs) are sometimes unfeasible because of their size, duration, cost, or ethical concerns. One solution to reducing sample size requirements is to leverage data from pooled external controls or real world data (RWD) in accordance with the concepts of extrapolation. Usually, propensity score matching methods can be used to create defined groups of patients that are controlled for confounding based on a set of measured covariates. However, the balance check on all confounding covariates and tweaks to the propensity score model are necessary to achieve the joint balance across all covariates. In this talk, we will discuss a composite likelihood method with individual weight estimated on the control observations using entropy balancing technique, which outperforms the propensity score matching in terms of assuring the balance of covariates in the pre-specified moments automatically. We will discuss how this method can be applied in the context of uncontrolled and asymmetrically allocated randomized controlled trials.