Abstract: A target trial dataset with trial covariates and clinical outcome data is obtained from a plurality of human patients. An artificial patient dataset is generated by using distributions of one or more corresponding covariates and their correlations derived from the set of trial covariates, and filtering the artificial patient dataset, with each data record of the artificial patient dataset storing covariates of a respective artificial patient. The artificial patient dataset is provided to a disease progression model to generate a simulated dataset with simulated covariates and clinical outcome data for artificial patients not receiving experimental treatment. A treatment effect of a drug is determined by analyzing the trial dataset and the simulated dataset together to incorporate the simulated dataset, wherein the simulated dataset is given a weight in comparison to a control arm of the trial dataset, with the weight based on at least a maximum weight value.

Abstract: One aspect of the invention refers to an oral pharmaceutical composition comprising the MDM2-antagonist of formula I in a dose range of 30 mg to 45 mg for use in the treatment of cancer, wherein this oral pharmaceutical composition is administered in a treatment cycle of once every three weeks (D1 q3w), wherein this treatment cycle of once every three weeks (D1 q3w) may be repeated that many times as considered beneficial for the patient from a medical point of view. Another aspect of the invention refers to an MDM2-antagonist of formula I for use in the first line systemic treatment (primary treatment) of dedifferentiated liposarcoma. A further aspect of the invention refers to the use of the MDM2-antagonist of formula I for the manufacture of a medicament for the treatment of a p53-wildtype and non-MDM2-amplified form of cancer.