Randomised clinical trials (RCTs) are generally regarded as the gold standard to assess treatment effects. However, because real world evidence on drug effectiveness and safety usually involves non-randomised study designs, statistical methods to adjust for confounding biases are often needed. In the last decade, prognostic score (PGS) analysis has been proposed as a new method to adjust for confounding bias, which aims to restore balance across the different treatment groups by identifying subjects with a similar prognosis for a given reference treatment. This requires the development of a multivariable prognostic model in the control arm of the study sample, which is then extrapolated to the different treatment arms. Because PGS strongly relies on the absence of hidden bias (i.e. no missing confounders), it is recommended to develop the prognostic models in large cohorts of control subjects in order to adjust for many covariates. When data are sparse, prognostic models can be obtained from the published literature. We extend a previously proposed method for prediction model aggregation to be used in non-randomised treatment studies to obtain valid inferences on treatment effectiveness. By aggregating these models, it becomes possible to improve the generalizability of same-sample PGS, when limited individual participant data are available for the target control population. We conducted extensive simulations to assess its the usefulness of model aggregation compared with other methods for confounding adjustment, when estimating treatment effects. We show that aggregating existing prognostic scores into a 'meta-score' is robust to misspecification, even when elementary scores wrongfully omit confounders or focus on different outcomes than the treatment effectiveness is targeting. We illustrate our methods in a setting of treatments for asthma.