HSM: Estimation of intervention effects for adaptive enrichment design RCTs that incorporate identification of predictive biomarkers

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Advances in medicine have led to the acknowledgement that differences in patient characteristics may lead to differences in how patients respond to therapies such as drugs. Patient characteristics that have had much medical research interest recently are those based on the genetic make-up. For example, patients may be categorised as those who express a certain genetic make-up and those who do not. The term generally used is biomarker, with the patients who express a certain genetic make-up referred as being biomarker positive and those who do not referred as biomarker negative. The term is usually not restricted to genetics and so could be used, for example, to categorise patients into those below and those above a certain age.This project is concerned with settings where the biomarker may interact with a therapy, that is, patients' responses to a therapy depends on whether they are biomarker positive or negative. If a patient's response depends on whether he/she is biomarker positive or negative, the biomarker is said to be a predictive biomarker.Stratified medicine is the branch of medicine where testing of new therapies allows for the possibility of differences in therapy effects in different subpopulations defined by biomarker status. One strategy is to have two separate trials. The first trial consisting of patients from the full population and its aim is to use a predictive biomarker to select the subpopulation that will benefit from the new therapy. The second trial recruits patients from the selected subpopulation and its aim is to use the data collected to get a definitive estimate of the size of the effect (benefit) of the new therapy in this subpopulation.An efficient strategy is to have a single trial that includes an interim analysis partway through the trial to select the subpopulation that benefits from the new therapy. After the interim analysis, more patients are recruited from the selected subpopulation and their data, together with data used in the interim analysis, are used is to get a definitive estimate of the size of the effect of the new therapy in the selected subpopulation. Such designs are commonly referred to as adaptive enrichment designs. They are efficient because fewer patients would be required to test a new therapy.Estimating the size of the effect of the new therapy when an adaptive enrichment design is used needs to adjust for the fact that the interim data used to select the subpopulation are also used in estimating the effect. If this is not done and the standard methods are used, the effect will be overestimated because the larger effect observed in the selected subpopulation using the interim analysis data may have occurred by chance. This is undesirable because definitive estimates of the size of the effect of new therapies are used by many stakeholders to make a decision on whether to adopt a therapy.Only one appropriate method for estimating effects of therapies, and which is for a specific design, has been developed for adaptive enrichment designs. This is one of the barriers of using these designs. The aim of this project is to remove this barrier by developing new methods for estimating size of effects while adjusting for subpopulation selection made using a predictive biomarker.The difference between the existing method and this work is that we will consider different forms of biomarkers, such as having more than one biomarker, and we will also consider a common type of patient outcome data in stratified medicine: time to event data such as overall survival for patients.This work will increase the use of adaptive enrichment designs. Consequently, this will save resources while testing new therapies and lead to more rapid development of safe effective treatments.