AbstractsComputer Science

Since different patients may have different causes of getting a disease, treating every patient having a certain disease in the same manner is not always be the best way to go. A treatment having effect in one type of patients may not have the same effect in a different type of patients. This makes it possible to partition a patient population into subpopulations in which a drug has distinct expected response. In this thesis the patient population is partitioned into two subpopulations where we have prior knowledge that one of them has a higher expected response to a drug than the other. based on responses to a drug in Phase II, it has been analyzed in which of the populations Phase III should continue. The results show that the decision is highly dependent on the utility function on which the analysis is based. One interesting case is when the vast majority of the patient population belongs o the subpopulations with the higher expected response and a utility function that takes into account the prevalence of the populations. In that case the simulations show that when the difference in expected response between the subpopulations is large, it is a safer choice in continuing in Phase III in the subpopulation having the higher expected response than in the full population even though the expected utility will be less. This is an expected result which indicates that the approach used to model the situation studied in this report is reasonable.