A computer-implemented method for measuring the treatment effect of a medical drug for a disease, the method comprising: obtaining a disease progression model of the disease, wherein the disease progression model is based on historical covariates and clinical outcome data reflecting the progression of the disease for a plurality of patients affected by said disease, and wherein the disease progression model quantitively describes the time course of disease progression by one or more corresponding covariates; receiving a target trial dataset with trial covariates (tc) and clinical outcome data (tco) obtained from a plurality of human patients participating in a target trial, wherein the target trial comprises at least one treatment arm with human patients receiving experimental treatment, and a control arm with patients not receiving experimental treatment (lint); characterized in that generating an artificial patient dataset by using distributions of one or more corresponding covariates and their correlations derived from the set of trial covariates (tc), and filtering the artificial patient dataset in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients (303-1, 303-2) which do not meet the inclusion criteria, or which meet the exclusion criteria of the target trial, are removed from the artificial patient dataset; or by random sampling the trial covariates (tc) with replacement, with each data record of the artificial patient dataset storing covariates of a respective artificial patient; with the artificial patient dataset as input for the disease progression model, generating a simulated dataset with simulated covariates (sc) and simulated clinical outcome data (sco) for artificial patients not receiving experimental treatment; determining the treatment effect (10d-te) of the drug by analyzing the trial dataset and the simulated dataset together using a power prior approach to incorporate the simulated dataset as prior information, wherein the simulated dataset is given a weight in comparison to the control arm of the trial dataset, with the weight based on at least a maximum weight value received from a user. obtaining a disease progression model of the disease, wherein the disease progression model is based on historical covariates and clinical outcome data reflecting the progression of the disease for a plurality of patients affected by said disease, and wherein the disease progression model quantitively describes the time course of disease progression by one or more corresponding covariates; receiving a target trial dataset with trial covariates (tc) and clinical outcome data (tco) obtained from a plurality of human patients participating in a target trial, wherein the target trial comprises at least one treatment arm with human patients receiving experimental treatment, and a control arm with patients not receiving experimental treatment (lint); characterized in that generating an artificial patient dataset by using distributions of one or more corresponding covariates and their correlations derived from the set of trial covariates (tc), and filtering the artificial patient dataset in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients (303-1, 303-2) which do not meet the inclusion criteria, or which meet the exclusion criteria of the target trial, are removed from the artificial patient dataset; or by random sampling the trial covariates (tc) with replacement, with each data record of the artificial patient dataset storing covariates of a respective artificial patient; using distributions of one or more corresponding covariates and their correlations derived from the set of trial covariates (tc), and filtering the artificial patient dataset in accordance with inclusion-exclusion criteria of the target trial so that data records associated with artificial patients (303-1, 303-2) which do not meet the inclusion criteria, or which meet the exclusion criteria of the target trial, are removed from the artificial patient dataset; or by random sampling the trial covariates (tc) with replacement, with each data record of the artificial patient dataset storing covariates of a respective artificial patient; with the artificial patient dataset as input for the disease progression model, generating a simulated dataset with simulated covariates (sc) and simulated clinical outcome data (sco) for artificial patients not receiving experimental treatment; determining the treatment effect (10d-te) of the drug by analyzing the trial dataset and the simulated dataset together using a power prior approach to incorporate the simulated dataset as prior information, wherein the simulated dataset is given a weight in comparison to the control arm of the trial dataset, with the weight based on at least a maximum weight value received from a user.